Philip H. S. Torr

发表

PoseField: An Efficient Mean-Field Based Method for Joint Estimation of Human Pose, Segmentation, and Depth

Vibhav Vineet, Glenn Sheasby, Jonathan Warrell, 2013, EMMCVPR.

Automatic dense visual semantic mapping from street-level imagery

Philip H. S. Torr, Sunando Sengupta, Paul Sturgess, 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Multi-Agent Common Knowledge Reinforcement Learning

Shimon Whiteson, Philip H. S. Torr, Jakob N. Foerster, 2018, NeurIPS.

Res2Net: A New Multi-Scale Backbone Architecture

Kai Zhao, Xinyu Zhang, Philip H. S. Torr, 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Learning feed-forward one-shot learners

Luca Bertinetto, Andrea Vedaldi, Philip H. S. Torr, 2016, NIPS.

The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix

Philip H. S. Torr, David W. Murray, D. W. Murray, 1997, International Journal of Computer Vision.

On the Robustness of Semantic Segmentation Models to Adversarial Attacks

Philip H. S. Torr, Ondrej Miksik, Anurag Arnab, 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

The Problem of Degeneracy in Structure and Motion Recovery from Uncalibrated Image Sequences

Andrew W. Fitzgibbon, Andrew Zisserman, Philip H. S. Torr, 1999, International Journal of Computer Vision.

Holistic, Instance-Level Human Parsing pdf

Philip H. S. Torr, Anurag Arnab, Qizhu Li, 2017, BMVC.

Incremental dense semantic stereo fusion for large-scale semantic scene reconstruction

Patrick Pérez, Olaf Kähler, Matthias Nießner, 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

Efficient piecewise learning for conditional random fields

Philip H. S. Torr, Karteek Alahari, Chris Russell, 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

Statistical detection of independent movement from a moving camera

Philip H. S. Torr, David W. Murray, 1993, Image Vis. Comput..

ROAM: A Rich Object Appearance Model with Application to Rotoscoping

Philip H. S. Torr, Patrick Pérez, Ondrej Miksik, 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Devon: Deformable Volume Network for Learning Optical Flow

Heng Wang, Yao Lu, Juho Kannala, 2018, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

Randomized trees for human pose detection

Philip H. S. Torr, Carlos Orrite-Uruñuela, Grégory Rogez, 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference

Philip H. S. Torr, Anton van den Hengel, Chunhua Shen, 2015, International Journal of Computer Vision.

Real-Time Highly Accurate Dense Depth on a Power Budget Using an FPGA-CPU Hybrid SoC

Philip H. S. Torr, Stuart Golodetz, Oscar Rahnama, 2019, IEEE Transactions on Circuits and Systems II: Express Briefs.

Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos pdf

Suman Saha, Philip H. S. Torr, Fabio Cuzzolin, 2016, BMVC.

Latent SVMs for Human Detection with a Locally Affine Deformation Field

Andrew Zisserman, Philip H. S. Torr, Lubor Ladicky, 2012, BMVC.

Fully-Convolutional Siamese Networks for Object Tracking pdf

Luca Bertinetto, Andrea Vedaldi, Philip H. S. Torr, 2016, ECCV Workshops.

Deeply Supervised Salient Object Detection with Short Connections

Zhuowen Tu, Ali Borji, Xiaowei Hu, 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Target Identity-aware Network Flow for online multiple target tracking

Afshin Dehghan, Mubarak Shah, Philip H. S. Torr, 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Building Models of Regular Scenes from Structure and Motion

Philip H. S. Torr, Anton van den Hengel, Thorsten Thormählen, 2006, BMVC.

StyP-Boost: A Bilinear Boosting Algorithm for Learning Style-Parameterized Classifiers

Jonathan Warrell, Philip H. S. Torr, Simon Prince, 2010, BMVC.

3D Model Acquisition from Extended Image Sequences

Paul A. Beardsley, Andrew Zisserman, Philip H. S. Torr, 1996, ECCV.

Estimating uncertainty in dense stereo disparity maps

Ingemar J. Cox, Andrew Blake, Antonio Criminisi, 2003 .

The Visual Object Tracking VOT2017 Challenge Results

Michael Felsberg, Huchuan Lu, Lei Zhang, 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

A Unified View of Piecewise Linear Neural Network Verification

Pushmeet Kohli, Philip H. S. Torr, Rudy Bunel, 2017, NeurIPS.

Global Contrast Based Salient Region Detection

Shi-Min Hu, Niloy J. Mitra, Philip H. S. Torr, 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

GA-Net: Guided Aggregation Net for End-To-End Stereo Matching

Ruigang Yang, Feihu Zhang, Philip H. S. Torr, 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Interactive 3D Model Completion

Philip H. S. Torr, Anton van den Hengel, Thorsten Thormählen, 2007, 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007).

A Six Point Solution for Structure and Motion

Andrew Zisserman, Philip H. S. Torr, Richard I. Hartley, 2000, ECCV.

Lessons from reinforcement learning for biological representations of space

Andrew Glennerster, Philip H. S. Torr, Alex Muryy, 2019, Vision Research.

Learning Layered Motion Segmentation of Video

Andrew Zisserman, Philip H. S. Torr, M. Pawan Kumar, 2005, ICCV.

Learning Layered Pictorial Structures from Video

Andrew Zisserman, Philip H. S. Torr, M. Pawan Kumar, 2004, ICVGIP.

Bottom-Up Top-Down Cues for Weakly-Supervised Semantic Segmentation

Yunchao Wei, Philip H. S. Torr, Ming-Ming Cheng, 2016, EMMCVPR.

Learning to Navigate the Energy Landscape

Matthias Nießner, Pushmeet Kohli, Philip H. S. Torr, 2016, 2016 Fourth International Conference on 3D Vision (3DV).

Model-based hand tracking using a hierarchical Bayesian filter

Björn Stenger, Roberto Cipolla, Philip H. S. Torr, 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

GDumb: A Simple Approach that Questions Our Progress in Continual Learning

Philip H. S. Torr, Ameya Prabhu, Puneet K. Dokania, 2020, ECCV.

Dynamic Graph Cuts and Their Applications in Computer Vision

Pushmeet Kohli, Philip H. S. Torr, Pushmeet Kohli, 2010, Computer Vision: Detection, Recognition and Reconstruction.

Robust computation and parametrization of multiple view relations

Andrew Zisserman, Philip H. S. Torr, Andrew Zisserman, 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

Using the Pn Potts model with learning methods to segment live cell images

Dimitris N. Metaxas, Philip H. S. Torr, Christophe Restif, 2007, 2007 IEEE 11th International Conference on Computer Vision.

Joint Optimization for Object Class Segmentation and Dense Stereo Reconstruction

W. F. Clocksin, Philip H. S. Torr, Sunando Sengupta, 2011, International Journal of Computer Vision.

P³ & Beyond: Move Making Algorithms for Solving Higher Order Functions

Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar, 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

A Projected Gradient Descent Method for CRF Inference Allowing End-to-End Training of Arbitrary Pairwise Potentials

Philip H. S. Torr, Fredrik Kahl, Anurag Arnab, 2017, EMMCVPR.

Solving Markov Random Fields using Second Order Cone Programming Relaxations

Andrew Zisserman, Philip H. S. Torr, M. Pawan Kumar, 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

WebSeg: Learning Semantic Segmentation from Web Searches pdf

Jiangjiang Liu, Philip H. S. Torr, Ming-Ming Cheng, 2018, ArXiv.

Amortized Rejection Sampling in Universal Probabilistic Programming pdf

Yee Whye Teh, Philip H. S. Torr, Tom Rainforth, 2019, ArXiv.

A Tiered Move-making Algorithm for General Non-submodular Pairwise Energies pdf

Vibhav Vineet, Jonathan Warrell, Philip H. S. Torr, 2014, ArXiv.

Exact and Approximate Inference in Associative Hierarchical Networks using Graph Cuts pdf

Pushmeet Kohli, Philip H. S. Torr, Lubor Ladicky, 2010, UAI.

BING: Binarized normed gradients for objectness estimation at 300fps

Philip H. S. Torr, Ziming Zhang, Paul L. Rosin, 2019, Computational Visual Media.

Hypergraph Convolution and Hypergraph Attention

Song Bai, Feihu Zhang, Philip H. S. Torr, 2019, Pattern Recognit..

Learning Layered Motion Segmentations of Video

Andrew Zisserman, Philip H. S. Torr, M. Pawan Kumar, 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

Hierarchical Model Fitting to 2D and 3D Data

Philip H. S. Torr, Anton van den Hengel, Thorsten Thormählen, 2006, International Conference on Computer Graphics, Imaging and Visualisation (CGIV'06).

3D Hand Shape and Pose From Images in the Wild

Philip H. S. Torr, Adnane Boukhayma, Rodrigo de Bem, 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Learning to Compare: Relation Network for Few-Shot Learning

Tao Xiang, Yongxin Yang, Philip H. S. Torr, 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Rethinking Class Relations: Absolute-relative Few-shot Learning pdf

Hongdong Li, Philip H. S. Torr, Piotr Koniusz, 2020, ArXiv.

Filter-Based Mean-Field Inference for Random Fields with Higher-Order Terms and Product Label-Spaces

Vibhav Vineet, Jonathan Warrell, Philip H. S. Torr, 2012, International Journal of Computer Vision.

Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation

Carsten Rother, Vibhav Vineet, Philip H. S. Torr, 2013, NIPS.

With Friends Like These, Who Needs Adversaries?

Philip H. S. Torr, Nicholas A. Lord, Saumya Jetley, 2018, NeurIPS.

R3SGM: Real-Time Raster-Respecting Semi-Global Matching for Power-Constrained Systems

Philip H. S. Torr, Simon Walker, Stuart Golodetz, 2018, 2018 International Conference on Field-Programmable Technology (FPT).

Higher Order Potentials in End-to-End Trainable Conditional Random Fields pdf

Philip H. S. Torr, Anurag Arnab, Sadeep Jayasumana, 2015, ArXiv.

Taxonomic Multi-class Prediction and Person Layout Using Efficient Structured Ranking

Matthew B. Blaschko, Andrew Zisserman, Philip H. S. Torr, 2012, ECCV.

Joint Optimization for Object Class Segmentation and Dense Stereo Reconstruction

W. F. Clocksin, Philip H. S. Torr, Sunando Sengupta, 2012, International Journal of Computer Vision.

Robust Higher Order Potentials for Enforcing Label Consistency

Pushmeet Kohli, Philip H. S. Torr, Lubor Ladicky, 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

OBJCUT for Face Detection

Pushmeet Kohli, Philip H. S. Torr, Jonathan Rihan, 2006, ICVGIP.

Visual Dialogue without Vision or Dialogue pdf

Philip H. S. Torr, N. Siddharth, Daniela Massiceti, 2018, ArXiv.

Approximate structured output learning for Constrained Local Models with application to real-time facial feature detection and tracking on low-power devices

Philip H. S. Torr, Paul Sturgess, Shuai Zheng, 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

PoseCut: Simultaneous Segmentation and 3D Pose Estimation of Humans Using Dynamic Graph-Cuts

Pushmeet Kohli, Philip H. S. Torr, Matthieu Bray, 2006, ECCV.

Extending Pictorial Structures for Object Recognition

Andrew Zisserman, Philip H. S. Torr, M. Pawan Kumar, 2004, BMVC.

Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence

Philip H. S. Torr, Puneet Kumar Dokania, Arslan Chaudhry, 2018, ECCV.

On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation

Luigi di Stefano, Philip H. S. Torr, Stuart Golodetz, 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Learning to superoptimize programs - Workshop Version pdf

Pushmeet Kohli, Philip H. S. Torr, Rudy Bunel, 2016, ArXiv.

Multi-shot Temporal Event Localization: a Benchmark pdf

Xiang Bai, Philip H. S. Torr, Yao Hu, 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks

Philip H. S. Torr, Songhwai Oh, Chanho Ahn, 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Few-Shot Action Recognition with Permutation-Invariant Attention

Hongdong Li, Xiaojuan Qi, Philip H. S. Torr, 2020, ECCV.

The light-path less traveled

Philip H. S. Torr, Peter F. Sturm, Srikumar Ramalingam, 2011, CVPR 2011.

Pixelwise Instance Segmentation with a Dynamically Instantiated Network

Philip H. S. Torr, Anurag Arnab, Anurag Arnab, 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Proposal generation for object detection using cascaded ranking SVMs

Jonathan Warrell, Philip H. S. Torr, Ziming Zhang, 2011, CVPR 2011.

Deeper Interpretability of Deep Networks pdf

Song-Chun Zhu, Philip H. S. Torr, Philippe G. Schyns, 2018, ArXiv.

Fast Memory-Efficient Generalized Belief Propagation

Philip H. S. Torr, M. Pawan Kumar, M. P. Kumar, 2006, ECCV.

Bayesian Stochastic Mesh Optimization for 3D reconstruction

Roberto Cipolla, Philip H. S. Torr, George Vogiatzis, 2003, BMVC.

Maintaining multiple motion model hypotheses over many views to recover matching and structure

Andrew W. Fitzgibbon, Andrew Zisserman, Philip H. S. Torr, 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

Robust Vision

Paul A. Beardsley, Philip H. S. Torr, David W. Murray, 1994, BMVC.

Efficiently solving convex relaxations for MAP estimation

Philip H. S. Torr, M. Pawan Kumar, M. P. Kumar, 2008, ICML '08.

Scalable Cascade Inference for Semantic Image Segmentation

Philip H. S. Torr, Paul Sturgess, Lubor Ladicky, 2012, BMVC.

Piecewise Linear Neural Network verification: A comparative study pdf

Pushmeet Kohli, Philip H. S. Torr, Rudy Bunel, 2017, ArXiv.

Filtering using a tree-based estimator

Björn Stenger, Roberto Cipolla, Philip H. S. Torr, 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

Multiple-Instance Learning with Structured Bag Models

Jonathan Warrell, Philip H. S. Torr, J. Warrell, 2011, EMMCVPR.

Proximal Mean-Field for Neural Network Quantization

Philip H. S. Torr, Richard I. Hartley, Puneet Kumar Dokania, 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

FACMAC: Factored Multi-Agent Centralised Policy Gradients pdf

Shimon Whiteson, Wendelin Böhmer, Philip H. S. Torr, 2020, NeurIPS.

Dense Semantic Image Segmentation with Objects and Attributes

Carsten Rother, Vibhav Vineet, Jonathan Warrell, 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

Body Language Based Individual Identification in Video Using Gait and Actions

Girijesh Prasad, Philip H. S. Torr, Joan Condell, 2008, ICISP.

Robust parameterization and computation of the trifocal tensor

Andrew Zisserman, Philip H. S. Torr, Andrew Zisserman, 1997, Image Vis. Comput..

Global stereo reconstruction under second order smoothness priors

Ian Reid, Philip H. S. Torr, Andrew Fitzgibbon, 2008, CVPR.

Learning discriminative space-time actions from weakly labelled videos

Philip H. S. Torr, Fabio Cuzzolin, Michael Sapienza, 2012, BMVC.

Continual Learning with Tiny Episodic Memories pdf

Marc'Aurelio Ranzato, Marcus Rohrbach, Philip H. S. Torr, 2019, ArXiv.

From Visual Query to Visual Portrayal

Ian D. Reid, Ali Shahrokni, Philip H. S. Torr, 2008, BMVC.

Urban 3D semantic modelling using stereo vision

Ali Shahrokni, Philip H. S. Torr, Sunando Sengupta, 2013, 2013 IEEE International Conference on Robotics and Automation.

BING: Binarized normed gradients for objectness estimation at 300fps

Philip H. S. Torr, Ziming Zhang, Ming-Ming Cheng, 2014, Computational Visual Media.

Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation pdf

Philip H. S. Torr, Puneet Kumar Dokania, Arslan Chaudhry, 2017, BMVC.

Modelling and Interpretation of Architecture from Several Images

Roberto Cipolla, Philip H. S. Torr, Anthony R. Dick, 2004, International Journal of Computer Vision.

The Sixth Visual Object Tracking VOT2018 Challenge Results

Michael Felsberg, Huchuan Lu, Fahad Shahbaz Khan, 2018, ECCV Workshops.

A Signal Propagation Perspective for Pruning Neural Networks at Initialization pdf

Philip H. S. Torr, Stephen Gould, Thalaiyasingam Ajanthan, 2019, ICLR.

Learning to superoptimize programs - Workshop Version pdf

Pushmeet Kohli, Philip H. S. Torr, Rudy Bunel, 2016, ICLR.

Spatio-Temporal Action Instance Segmentation and Localisation

Suman Saha, Philip H. S. Torr, Fabio Cuzzolin, 2020 .

Robust Parameterization and Computation of the Trifocal Tensor

Andrew Zisserman, Philip H. S. Torr, 1996, BMVC.

DenseCut: Densely Connected CRFs for Realtime GrabCut

Carsten Rother, Philip H. S. Torr, Victor Adrian Prisacariu, 2015, Comput. Graph. Forum.

End-to-End Representation Learning for Correlation Filter Based Tracking

Luca Bertinetto, Andrea Vedaldi, Philip H. S. Torr, 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Outlier detection and motion segmentation

Philip H. S. Torr, David W. Murray, D. W. Murray, 1993, Other Conferences.

The Visual Object Tracking VOT2013 Challenge Results

Michael Felsberg, Seunghoon Hong, Bohyung Han, 2013, 2013 IEEE International Conference on Computer Vision Workshops.

Fully-trainable deep matching pdf

Andrea Vedaldi, Philip H. S. Torr, James Thewlis, 2016, BMVC.

Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials

Pushmeet Kohli, Philip H. S. Torr, Mathieu Salzmann, 2019, SIAM J. Imaging Sci..

IMPSAC: Synthesis of Importance Sampling and Random Sample Consensus

Philip H. S. Torr, Colin Davidson, 2000, ECCV.

OBJCUT: Efficient Segmentation Using Top-Down and Bottom-Up Cues

Andrew Zisserman, Philip H. S. Torr, M. Pawan Kumar, 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Bilateral Functions for Global Motion Modeling

Minh N. Do, Philip H. S. Torr, Ming-Ming Cheng, 2014, ECCV.

The StarCraft Multi-Agent Challenge pdf

Shimon Whiteson, Philip H. S. Torr, Jakob N. Foerster, 2019, AAMAS.

An Object Category Specific mrffor Segmentation

Andrew Zisserman, Philip H. S. Torr, M. Pawan Kumar, 2006, Toward Category-Level Object Recognition.

Object Proposal Generation Using Two-Stage Cascade SVMs

Philip H. S. Torr, Ziming Zhang, Ziming Zhang, 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Learning Anchor Planes for Classification

Philip H. S. Torr, Ziming Zhang, Lubor Ladicky, 2011, NIPS.

Dynamic Graph Cuts for Efficient Inference in Markov Random Fields

Pushmeet Kohli, Philip H. S. Torr, Pushmeet Kohli, 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Feature Based Methods for Structure and Motion Estimation

Andrew Zisserman, Philip H. S. Torr, Andrew Zisserman, 1999, Workshop on Vision Algorithms.

Collaborative Large-Scale Dense 3D Reconstruction with Online Inter-Agent Pose Optimisation

Philip H. S. Torr, David W. Murray, Victor Adrian Prisacariu, 2018, IEEE Transactions on Visualization and Computer Graphics.

A Robust Stereo Prior for Human Segmentation

Glenn Sheasby, Philip H. S. Torr, Julien P. C. Valentin, 2012, ACCV.

Bottom-up Instance Segmentation using Deep Higher-Order CRFs pdf

Philip H. S. Torr, Anurag Arnab, Anurag Arnab, 2016, BMVC.

P3 & Beyond: Solving Energies with Higher Order Cliques

Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar, 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

The Stochastic Search Network

John Mark Bishop, Philip H. S. Torr, 1992 .

Efficient discriminative learning of parts-based models

Andrew Zisserman, Philip H. S. Torr, M. Pawan Kumar, 2009, 2009 IEEE 12th International Conference on Computer Vision.

Mining Pixels: Weakly Supervised Semantic Segmentation Using Image Labels pdf

Yunchao Wei, Philip H. S. Torr, Ming-Ming Cheng, 2016, ArXiv.

Reconstructing relief surfaces

Roberto Cipolla, Steven M. Seitz, Philip H. S. Torr, 2008, Image and Vision Computing.

SemanticPaint

Matthias Nießner, David Kim, Antonio Criminisi, 2015, ACM Trans. Graph..

Exploiting projective geometry for view-invariant monocular human motion analysis in man-made environments

Josechu J. Guerrero, Philip H. S. Torr, Carlos Orrite-Uruñuela, 2014, Comput. Vis. Image Underst..

Coarse-to-fine Planar Regularization for Dense Monocular Depth Estimation

Philip H. S. Torr, Christopher Zach, Stephan Liwicki, 2016, ECCV.

Concerning Bayesian Motion Segmentation, Model, Averaging, Matching and the Trifocal Tensor

Andrew Zisserman, Philip H. S. Torr, Andrew Zisserman, 1998, ECCV.

Human Instance Segmentation from Video using Detector-based Conditional Random Fields

Vibhav Vineet, Jonathan Warrell, Philip H. S. Torr, 2011, BMVC.

Dense stereo using pivoted dynamic programming

Antonio Criminisi, Philip H. S. Torr, A. Criminisi, 2004, Image Vis. Comput..

Graph Cut Based Inference with Co-occurrence Statistics

Pushmeet Kohli, Philip H. S. Torr, Lubor Ladicky, 2010, ECCV.

Filter-Based Mean-Field Inference for Random Fields with Higher-Order Terms and Product Label-Spaces

Vibhav Vineet, Jonathan Warrell, Philip H. S. Torr, 2012, ECCV.

Conditional Random Fields as Recurrent Neural Networks

Vibhav Vineet, Philip H. S. Torr, Chang Huang, 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

IMPSAC: Synthesis of Importance Sampling and Random Sample Consensus

Philip H. S. Torr, Colin Davidson, P. Torr, 2000, IEEE Trans. Pattern Anal. Mach. Intell..

Reduce, reuse & recycle: Efficiently solving multi-label MRFs

Pushmeet Kohli, Philip H. S. Torr, Karteek Alahari, 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

Invariant Fitting of Two View Geometry

Andrew W. Fitzgibbon, Philip H. S. Torr, 2003, BMVC.

An assessment of information criteria for motion model selection

Philip H. S. Torr, 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

OBJ CUT

Andrew Zisserman, Philip H. S. Torr, M. Pawan Kumar, 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

Struck: Structured output tracking with kernels

Philip H. S. Torr, Amir Saffari, Sam Hare, 2011, ICCV.

Learning Disentangled Representations with Semi-Supervised Deep Generative Models

Frank D. Wood, Pushmeet Kohli, Philip H. S. Torr, 2017, NIPS.

A Framework for the Volumetric Integration of Depth Images pdf

Olaf Kähler, Ian D. Reid, Philip H. S. Torr, 2014, ArXiv.

On New View Synthesis Using Multiview Stereo

Andrew W. Fitzgibbon, Ian D. Reid, Philip H. S. Torr, 2007, BMVC.

Higher Order Conditional Random Fields in Deep Neural Networks

Philip H. S. Torr, Anurag Arnab, Sadeep Jayasumana, 2015, ECCV.

SemanticPaint: interactive segmentation and learning of 3D world

Matthias Nießner, David Kim, Antonio Criminisi, 2015, SIGGRAPH Talks.

InfiniTAM v3: A Framework for Large-Scale 3D Reconstruction with Loop Closure pdf

Olaf Kähler, Philip H. S. Torr, David W. Murray, 2017, ArXiv.

Learning Class-Specific Edges for Object Detection and Segmentation

Andrew W. Fitzgibbon, Andrew Zisserman, Philip H. S. Torr, 2006, ICVGIP.

Layer Extraction with a Bayesian Model of Shapes

Roberto Cipolla, Philip H. S. Torr, Anthony R. Dick, 2000, ECCV.

Hand Pose Estimation Using Hierarchical Detection

Björn Stenger, Roberto Cipolla, Philip H. S. Torr, 2004, ECCV Workshop on HCI.

NAPSAC: High Noise, High Dimensional Robust Estimation - it's in the Bag

Slawomir J. Nasuto, Philip H. S. Torr, R. Craddock, 2002, BMVC.

Using Strong Shape Priors for Stereo

Pushmeet Kohli, Philip H. S. Torr, Matthieu Bray, 2006, ICVGIP.

Controllable Text-to-Image Generation

Thomas Lukasiewicz, Xiaojuan Qi, Philip H. S. Torr, 2019, NeurIPS.

Associative Hierarchical Random Fields

Pushmeet Kohli, Philip H. S. Torr, Lubor Ladicky, 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Spatio-temporal human action localisation and instance segmentation in temporally untrimmed videos pdf

Suman Saha, Philip H. S. Torr, Fabio Cuzzolin, 2017, ArXiv.

Measuring uncertainty in graph cut solutions

Pushmeet Kohli, Philip H. S. Torr, Pushmeet Kohli, 2008, Comput. Vis. Image Underst..

Inducing Interpretable Representations with Variational Autoencoders pdf

Frank D. Wood, Pushmeet Kohli, Philip H. S. Torr, 2016, ArXiv.

Human Pose Estimation Using a Joint Pixel-wise and Part-wise Formulation

Andrew Zisserman, Philip H. S. Torr, Lubor Ladicky, 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

Stochastic Motion Clustering

Philip H. S. Torr, David W. Murray, D. W. Murray, 1994, ECCV.

Learning a Kinematic Prior for Tree-Based Filtering

Björn Stenger, Roberto Cipolla, Philip H. S. Torr, 2003, BMVC.

Playing Doom with SLAM-Augmented Deep Reinforcement Learning pdf

Philip H. S. Torr, Ondrej Miksik, N. Siddharth, 2016, ArXiv.

Computationally efficient face detection

Andrew Blake, Bernhard Schölkopf, Sami Romdhani, 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

Understanding the effects of data parallelism and sparsity on neural network training

Martin Jaggi, Philip H. S. Torr, Thalaiyasingam Ajanthan, 2020, ICLR.

Deeply Supervised Salient Object Detection with Short Connections

Zhuowen Tu, Ali Borji, Xiaowei Hu, 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Hierarchical Part-Based Human Body Pose Estimation

Roberto Cipolla, Philip H. S. Torr, Arasanathan Thayananthan, 2005, BMVC.

Learning Arbitrary Potentials in CRFs with Gradient Descent pdf

Philip H. S. Torr, Richard I. Hartley, Fredrik Kahl, 2017, ArXiv.

Efficient Continuous Relaxations for Dense CRF pdf

Pushmeet Kohli, Philip H. S. Torr, Rudy Bunel, 2016, ECCV.

DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents

Philip H. S. Torr, Wongun Choi, Paul Vernaza, 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Template-Based Hand Detection and Tracking

Björn Stenger, Roberto Cipolla, Philip H. S. Torr, 2003, Advanced Studies in Biometrics.

WordCraft: An Environment for Benchmarking Commonsense Agents pdf

Shimon Whiteson, Philip H. S. Torr, Pasquale Minervini, 2020, ArXiv.

Mirror Descent View for Neural Network Quantization pdf

Philip H. S. Torr, Puneet K. Dokania, Thalaiyasingam Ajanthan, 2019, ArXiv.

Multi-agent Diverse Generative Adversarial Networks

Philip H. S. Torr, Vinay P. Namboodiri, Arnab Ghosh, 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Improved Moves for Truncated Convex Models

Philip H. S. Torr, Olga Veksler, M. Pawan Kumar, 2008, J. Mach. Learn. Res..

An Integrated Bayesian Approach to Layer Extraction from Image Sequences

Richard Szeliski, P. Anandan, Philip H. S. Torr, 2001, IEEE Trans. Pattern Anal. Mach. Intell..

Dense Stereo Using Pivoted Dynamic

Antonio Criminisi, Philip H. S. Torr, A. Criminisi, 2002, British Machine Vision Conference.

Efficient online structured output learning for keypoint-based object tracking

Philip H. S. Torr, Amir Saffari, Sam Hare, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

Object Proposals Estimation in Depth Image Using Compact 3D Shape Manifolds

Niloy J. Mitra, Carsten Rother, Philip H. S. Torr, 2015, GCPR.

Efficient Dense Stereo with Occlusions for New View-Synthesis by Four-State Dynamic Programming

Andrew Blake, Antonio Criminisi, Carsten Rother, 2006, International Journal of Computer Vision.

A Bayesian Estimation of Building Shape Using MCMC

Roberto Cipolla, Philip H. S. Torr, Anthony R. Dick, 2002, ECCV.

VideoTrace: rapid interactive scene modelling from video

Philip H. S. Torr, Anton van den Hengel, Thorsten Thormählen, 2007, ACM Trans. Graph..

Transflow Learning: Repurposing Flow Models Without Retraining pdf

Philip H. S. Torr, Atilim Gunecs Baydin, Andrew Gambardella, 2019, ArXiv.

Robust Higher Order Potentials for Enforcing Label Consistency

Pushmeet Kohli, Philip H. S. Torr, Lubor Ladicky, 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

Adversarial Metric Attack for Person Re-identification pdf

Song Bai, Yingwei Li, Yuyin Zhou, 2019, ArXiv.

Multiview Stereo via Volumetric Graph-Cuts and Occlusion Robust Photo-Consistency

Roberto Cipolla, Philip H. S. Torr, George Vogiatzis, 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Improved Initialization and Gaussian Mixture Pairwise Terms for Dense Random Fields with Mean-field Inference

Vibhav Vineet, Jonathan Warrell, Philip H. S. Torr, 2012, BMVC.

A shape hierarchy for 3D modelling from video

Philip H. S. Torr, Anton van den Hengel, Thorsten Thormählen, 2007, GRAPHITE '07.

Model Selection for Two View Geometry: A Review

Philip H. S. Torr, P. Torr, 1999, Shape, Contour and Grouping in Computer Vision.

Efficient Linear Programming for Dense CRFs

Philip H. S. Torr, Mathieu Salzmann, Rudy Bunel, 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Performance characterization of fundamental matrix estimation under image degradation

Andrew Zisserman, Philip H. S. Torr, Andrew Zisserman, 1997, Machine Vision and Applications.

Exact inference in multi-label CRFs with higher order cliques

Pushmeet Kohli, Philip H. S. Torr, Srikumar Ramalingam, 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

Hijacking Malaria Simulators with Probabilistic Programming pdf

Yee Whye Teh, Philip H. S. Torr, Tom Rainforth, 2019, ArXiv.

Value Propagation Networks pdf

Pushmeet Kohli, Philip H. S. Torr, Nicolas Usunier, 2018, ICLR.

Combining Appearance and Structure from Motion Features for Road Scene Understanding

Philip H. S. Torr, Paul Sturgess, Lubor Ladicky, 2009, BMVC.

Probabilistic Object Reconstruction with Online Global Model Correction

Philip H. S. Torr, Victor Adrian Prisacariu, Stuart Golodetz, 2017, 2017 International Conference on 3D Vision (3DV).

Efficient Minimization of Higher Order Submodular Functions using Monotonic Boolean Functions pdf

Philip H. S. Torr, Lubor Ladicky, Srikumar Ramalingam, 2011, Discret. Appl. Math..

Very High Frame Rate Volumetric Integration of Depth Images on Mobile Devices

Olaf Kähler, Philip H. S. Torr, David W. Murray, 2015, IEEE Transactions on Visualization and Computer Graphics.

Meta-Learning Deep Visual Words for Fast Video Object Segmentation pdf

Philip H. S. Torr, Harkirat Singh Behl, Mohammad Najafi, 2018, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Regression-Based Human Motion Capture From Voxel Data

Philip H. S. Torr, Matthieu Bray, Yunda Sun, 2006, BMVC.

An Empirical Study of Detection-Based Video Instance Segmentation

Philip H. S. Torr, Qiang Wang, Yi He, 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

Prototypical Priors: From Improving Classification to Zero-Shot Learning pdf

Philip H. S. Torr, Bernardino Romera-Paredes, Sadeep Jayasumana, 2015, BMVC.

Real-time depth processing for embedded platforms

Philip H. S. Torr, Oscar Rahnama, Aleksej Makarov, 2017, Commercial + Scientific Sensing and Imaging.

Heterogeneous wireless system testbed for remote image processing in automated vehicles

Peter Ball, Philip H. S. Torr, Fabio Cuzzolin, 2016, 2016 10th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP).

Incremental dense multi-modal 3D scene reconstruction

Patrick Pérez, Vibhav Vineet, Philip H. S. Torr, 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Etalumis: bringing probabilistic programming to scientific simulators at scale

Prabhat, Gilles Louppe, Frank D. Wood, 2019, SC.

Three Birds One Stone: A Unified Framework for Salient Object Segmentation, Edge Detection and Skeleton Extraction pdf

Jiangjiang Liu, Ali Borji, Philip H. S. Torr, 2018, ArXiv.

Learn to Interpret Atari Agents pdf

Zhao Yang, Song Bai, Li Zhang, 2018, ArXiv.

Deep Fully-Connected Part-Based Models for Human Pose Estimation

Philip H. S. Torr, Stuart Golodetz, Anurag Arnab, 2018, ACML.

Automatic 3D Modelling of Architecture

Roberto Cipolla, Philip H. S. Torr, Anthony R. Dick, 2000, BMVC.

Gaussian Process Latent Variable Models for Human Pose Estimation

Neil D. Lawrence, Philip H. S. Torr, Carl Henrik Ek, 2007, MLMI.

Inference Methods for CRFs with Co-occurrence Statistics

Pushmeet Kohli, Philip H. S. Torr, Lubor Ladicky, 2012, International Journal of Computer Vision.

Hand detection using multiple proposals

Andrew Zisserman, Philip H. S. Torr, Arpit Mittal, 2011, BMVC.

Invariant fitting of two view geometry

Andrew W. Fitzgibbon, Philip H. S. Torr, 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

Shimon Whiteson, Pushmeet Kohli, Philip H. S. Torr, 2017, ICML.

Estimating 3D hand pose using hierarchical multi-label classification

Björn Stenger, Roberto Cipolla, Philip H. S. Torr, 2007, Image Vis. Comput..

Data Dependent Randomized Smoothing pdf

Bernard Ghanem, Philip H. S. Torr, Motasem Alfarra, 2020, ArXiv.

A Semi-supervised Deep Generative Model for Human Body Analysis

Philip H. S. Torr, Arnab Ghosh, Ondrej Miksik, 2018, ECCV Workshops.

Robust Detection of Degenerate Configurations while Estimating the Fundamental Matrix

Andrew Zisserman, Philip H. S. Torr, Stephen J. Maybank, 1998, Comput. Vis. Image Underst..

Interactive Image Segmentation Using an Adaptive GMMRF Model

Patrick Pérez, Andrew Blake, Carsten Rother, 2004, ECCV.

Combining Single View Recognition and Multiple View Stereo for Architectural Scenes

Roberto Cipolla, Philip H. S. Torr, Anthony R. Dick, 2001, ICCV.

SNIP: Single-shot Network Pruning based on Connection Sensitivity pdf

Philip H. S. Torr, Thalaiyasingam Ajanthan, Namhoon Lee, 2018, ICLR.

Multi-view stereo via volumetric graph-cuts

Roberto Cipolla, Philip H. S. Torr, George Vogiatzis, 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

Struck: Structured Output Tracking with Kernels

Vibhav Vineet, Philip H. S. Torr, Ming-Ming Cheng, 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

An Invariant Large Margin Nearest Neighbour Classifier

Andrew Zisserman, Philip H. S. Torr, M. Pawan Kumar, 2007, 2007 IEEE 11th International Conference on Computer Vision.

Real-Time RGB-D Camera Pose Estimation in Novel Scenes Using a Relocalisation Cascade

Luigi di Stefano, Philip H. S. Torr, Victor Adrian Prisacariu, 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Fast Human Pose Detection Using Randomized Hierarchical Cascades of Rejectors

Philip H. S. Torr, Carlos Orrite-Uruñuela, Grégory Rogez, 2012, International Journal of Computer Vision.

Efficient Likelihood Learning of a Generic CNN-CRF Model for Semantic Segmentation pdf

Carsten Rother, Walter Forkel, Philip H. S. Torr, 2015, ArXiv.

Live Collaborative Large-Scale Dense 3D Reconstruction Using Consumer-Grade Hardware

Philip H. S. Torr, David W. Murray, Victor Adrian Prisacariu, 2018, 2018 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct).

A Conditional Deep Generative Model of People in Natural Images

Philip H. S. Torr, Arnab Ghosh, N. Siddharth, 2019, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).

Adaptive Neural Compilation

Pushmeet Kohli, Philip H. S. Torr, Rudy Bunel, 2016, NIPS.

Pose estimation and tracking using multivariate regression

Björn Stenger, Roberto Cipolla, Philip H. S. Torr, 2008, Pattern Recognit. Lett..

Weakly- and Semi-Supervised Panoptic Segmentation pdf

Philip H. S. Torr, Anurag Arnab, Qizhu Li, 2022 .

Geometrically Constrained Structure from Motion: Points on Planes

Richard Szeliski, Philip H. S. Torr, R. Szeliski, 1998, SMILE.

Incremental Tube Construction for Human Action Detection

Suman Saha, Philip H. S. Torr, Harkirat Singh Behl, 2017, BMVC.

Meta-learning with differentiable closed-form solvers pdf

Luca Bertinetto, Andrea Vedaldi, Philip H. S. Torr, 2018, ICLR.

An Analysis of Convex Relaxations for MAP Estimation

Vladimir Kolmogorov, Philip H. S. Torr, Pawan Kumar Mudigonda, 2007, NIPS.

Statistical detection of independent movement from a moving camera

Philip H. S. Torr, David W. Murray, D. W. Murray, 1993, Image Vis. Comput..

Joint Object-Material Category Segmentation from Audio-Visual Cues pdf

Philip H. S. Torr, Shahram Izadi, Stuart Golodetz, 2016, BMVC.

Reconstructing relief surfaces

Roberto Cipolla, Steven M. Seitz, Philip H. S. Torr, 2008, Image and Vision Computing.

Analyzing Convex Relaxations for MAP Estimation

Vladimir Kolmogorov, Philip H. S. Torr, M. Pawan Kumar, 2009 .

Distributed Non-convex ADMM-based inference in large-scale random fields

Patrick Pérez, Vibhav Vineet, Philip H. S. Torr, 2014, BMVC.

The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results

Zhenyu He, Michael Felsberg, Ming Tang, 2016, ECCV Workshops.

Gaze manipulation for one-to-one teleconferencing

Andrew Blake, Antonio Criminisi, Philip H. S. Torr, 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

Solving Markov Random Fields using Semi Definite Programming

Philip H. S. Torr, P. Torr, 2003, AISTATS.

Shape context and chamfer matching in cluttered scenes

Björn Stenger, Roberto Cipolla, Philip H. S. Torr, 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

What, Where and How Many? Combining Object Detectors and CRFs

Philip H. S. Torr, Paul Sturgess, Lubor Ladicky, 2010, ECCV.

Scalable FPGA Median Filtering using Multiple Efficient Passes

Philip H. S. Torr, Stuart Golodetz, Oscar Rahnama, 2020, FPGA.

Learn To Pay Attention pdf

Philip H. S. Torr, Nicholas A. Lord, Namhoon Lee, 2018, ICLR.

Simultaneous Segmentation and Pose Estimation of Humans Using Dynamic Graph Cuts

Pushmeet Kohli, Philip H. S. Torr, Matthieu Bray, 2008, International Journal of Computer Vision.

Multivariate Relevance Vector Machines for Tracking

Björn Stenger, Roberto Cipolla, Philip H. S. Torr, 2006, ECCV.

Few-shot Action Recognition via Improved Attention with Self-supervision pdf

Hongdong Li, Xiaojuan Qi, Philip H. S. Torr, 2020, ArXiv.

Dynamic Hybrid Algorithms for MAP Inference in Discrete MRFs

Pushmeet Kohli, Philip H. S. Torr, Karteek Alahari, 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

A tiered move-making algorithm for general pairwise MRFs

Vibhav Vineet, Jonathan Warrell, Philip H. S. Torr, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

Fields of Experts for Image-based Rendering

Andrew W. Fitzgibbon, Ian D. Reid, Philip H. S. Torr, 2006, BMVC.

MLESAC: A New Robust Estimator with Application to Estimating Image Geometry

Andrew Zisserman, Philip H. S. Torr, Andrew Zisserman, 2000, Comput. Vis. Image Underst..

FLIPDIAL: A Generative Model for Two-Way Visual Dialogue

Philip H. S. Torr, Puneet Kumar Dokania, N. Siddharth, 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Efficient discriminative learning of parametric nearest neighbor classifiers

Philip H. S. Torr, Ziming Zhang, Sunando Sengupta, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

Continual Learning in Low-rank Orthogonal Subspaces pdf

Philip H. S. Torr, Naeemullah Khan, Puneet K. Dokania, 2020, NeurIPS.

Long-term Tracking in the Wild: A Benchmark pdf

Arnold W. M. Smeulders, Ran Tao, Luca Bertinetto, 2018, ECCV.

An integrated Bayesian approach to layer extraction from image sequences

Richard Szeliski, P. Anandan, Philip H. S. Torr, 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

Associative hierarchical CRFs for object class image segmentation

Pushmeet Kohli, Philip H. S. Torr, Lubor Ladicky, 2009, 2009 IEEE 12th International Conference on Computer Vision.

Robust detection of degenerate configurations for the fundamental matrix

Andrew Zisserman, Philip H. S. Torr, Stephen J. Maybank, 1995, Proceedings of IEEE International Conference on Computer Vision.

Locally Linear Support Vector Machines

Philip H. S. Torr, Lubor Ladicky, L. Ladicky, 2011, ICML.

Online Real-Time Multiple Spatiotemporal Action Localisation and Prediction

Suman Saha, Philip H. S. Torr, Fabio Cuzzolin, 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

STEER : Simple Temporal Regularization For Neural ODE

Philip H. S. Torr, Harkirat Singh Behl, Vinay Namboodiri, 2020, NeurIPS.

Random forests versus Neural Networks — What's best for camera localization?

Eric Brachmann, Carsten Rother, Philip H. S. Torr, 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

NAPSAC: high noise, high dimensional model parameterisation - it's in the bag

John Mark Bishop, Slawomir J. Nasuto, Philip H. S. Torr, 2002 .

Cooperating Motion Processes

Andrew Zisserman, Philip H. S. Torr, David W. Murray, 1991, BMVC.

Straight to Shapes: Real-Time Detection of Encoded Shapes

Philip H. S. Torr, Stuart Golodetz, Michael Sapienza, 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Knowing who to listen to: Prioritizing experts from a diverse ensemble for attribute personalization

Philip H. S. Torr, Bernardino Romera-Paredes, Devi Parikh, 2016, 2016 IEEE International Conference on Image Processing (ICIP).

Ambiguity Modeling in Latent Spaces

Neil D. Lawrence, Philip H. S. Torr, Grégory Rogez, 2008, MLMI.

Learning Discriminative Space–Time Action Parts from Weakly Labelled Videos

Philip H. S. Torr, Fabio Cuzzolin, Michael Sapienza, 2013, International Journal of Computer Vision.

Likelihood Models For Template Matching using the PDF Projection Theorem

Roberto Cipolla, Philip H. S. Torr, Arasanathan Thayananthan, 2004, BMVC.

Feature sampling and partitioning for visual vocabulary generation on large action classification datasets pdf

Philip H. S. Torr, Fabio Cuzzolin, Michael Sapienza, 2014, ArXiv.

Rapid Interactive Modelling from Video with Graph Cuts

Philip H. S. Torr, Anton van den Hengel, Thorsten Thormählen, 2006, Eurographics.

Unifying Training and Inference for Panoptic Segmentation

Xiaojuan Qi, Philip H. S. Torr, Qizhu Li, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Discrete minimum ratio curves and surfaces

Philip H. S. Torr, Fred Nicolls, F. Nicolls, 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

Revisiting Deep Structured Models for Pixel-Level Labeling with Gradient-Based Inference

Philip H. S. Torr, Fredrik Kahl, Anurag Arnab, 2018, SIAM J. Imaging Sci..

Domain Partitioning Network pdf

Philip H. S. Torr, András Horváth, Viveka Kulharia, 2019, ArXiv.

Measuring Uncertainty in Graph Cut Solutions - Efficiently Computing Min-marginal Energies Using Dynamic Graph Cuts

Pushmeet Kohli, Philip H. S. Torr, Pushmeet Kohli, 2006, ECCV.

On partial optimality in multi-label MRFs

Vladimir Kolmogorov, Pushmeet Kohli, Carsten Rother, 2008, ICML '08.

HFS: Hierarchical Feature Selection for Efficient Image Segmentation

Shi-Min Hu, Zhuowen Tu, Yun Liu, 2016, ECCV.

Bayesian Model Estimation and Selection for Epipolar Geometry and Generic Manifold Fitting

Philip H. S. Torr, P. Torr, 2002, International Journal of Computer Vision.

Fast Online Object Tracking and Segmentation: A Unifying Approach

Qiang Wang, Luca Bertinetto, Philip H. S. Torr, 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Learning to Adapt for Stereo

Luigi di Stefano, Philip H. S. Torr, Oscar Rahnama, 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Global stereo reconstruction under second order smoothness priors

Andrew W. Fitzgibbon, Ian D. Reid, Philip H. S. Torr, 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

An embarrassingly simple approach to zero-shot learning

Philip H. S. Torr, Bernardino Romera-Paredes, Bernardino Romera-Paredes, 2015, ICML.

Mesh Based Semantic Modelling for Indoor and Outdoor Scenes

Ali Shahrokni, Jonathan Warrell, Philip H. S. Torr, 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

Joint Training of Generic CNN-CRF Models with Stochastic Optimization

Carsten Rother, Philip H. S. Torr, Bogdan Savchynskyy, 2016, ACCV.

Re-Ranking via Metric Fusion for Object Retrieval and Person Re-Identification

Longin Jan Latecki, Song Bai, Philip H. S. Torr, 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

The Semantic Paintbrush: Interactive 3D Mapping and Recognition in Large Outdoor Spaces

Patrick Pérez, Matthias Nießner, Vibhav Vineet, 2015, CHI.

Exploiting uncertainty in regression forests for accurate camera relocalization

Andrew W. Fitzgibbon, Matthias Nießner, Philip H. S. Torr, 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence Learning pdf

Yansong Tang, Philip H. S. Torr, Zhenyu Jiang, 2021, ArXiv.

Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning

Philip H. S. Torr, Piotr Koniusz, Songlei Jian, 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Multi-shot Temporal Event Localization: a Benchmark

Song Bai, Xiang Bai, Philip H. S. Torr, 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

SECI-GAN: Semantic and Edge Completion for dynamic objects removal

Philip H. S. Torr, Matteo Matteucci, Andrea Romanoni, 2021, 2020 25th International Conference on Pattern Recognition (ICPR).

Combating Adversaries with Anti-Adversaries pdf

Bernard Ghanem, Philip H. S. Torr, Juan C. P'erez, 2021, AAAI.

ANCER: Anisotropic Certification via Sample-wise Volume Maximization pdf

Bernard Ghanem, Francisco Eiras, Philip H. S. Torr, 2021, ArXiv.

Shape-Tailored Deep Neural Networks pdf

Philip H. S. Torr, Ganesh Sundaramoorthi, Naeemullah Khan, 2021, ArXiv.

Mirror Descent View for Neural Network Quantization

Philip H. S. Torr, Puneet K. Dokania, Richard Hartley, 2019, AISTATS.

KL Guided Domain Adaptation pdf

Philip H. S. Torr, Atilim Gunecs Baydin, Yarin Gal, 2021, ArXiv.

Deep learning for predicting COVID-19 malignant progression

Song Bai, Xiang Bai, Philip H. S. Torr, 2021, Medical Image Analysis.

Detecting and Quantifying Malicious Activity with Simulation-based Inference pdf

Philip H. S. Torr, Atilim Gunecs Baydin, Bogdan State, 2021, ArXiv.

Scalable FPGA Median Filtering via a Directional Median Cascade

Philip H. S. Torr, Stuart Golodetz, Oscar Rahnama, 2021, 2021 IEEE 29th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM).

Capturing Label Characteristics in VAEs

Philip H. S. Torr, Tom Rainforth, N. Siddharth, 2021, ICLR.

DeformRS: Certifying Input Deformations with Randomized Smoothing pdf

Bernard Ghanem, Philip H. S. Torr, Motasem Alfarra, 2021, AAAI.

Solving Energies with Higher Order Cliques

Philip H. S. Torr, Pushmeet Kohli, M. P. Kumar, 2007 .

What , Where & How Many ? Combining Object Detectors and CRFs

Philip H. S. Torr, Alahari Karteek, L. Ladicky, 2010 .

Salient Object Detection and Segmentation

Philip H. S. Torr, N. Mitra, Ming-Ming Cheng, 2013 .

The Visual Object Tracking VOT2016 Challenge Results

Zhenyu He, Henry Medeiros, Michael Felsberg, 2016, ECCV Workshops.

The Importance of Estimating Object Extent when Tracking with Correlation Filters

Philip H. S. Torr, Luca Bertinetto, Jack Valmadre, 2015 .

LP-relaxation of Binarized Energy Minimization ( Version 1 . 50 )

Philip H. S. Torr, C. Rother, Pushmeet Kohli, 2008 .

Recurrent Instance Segmentation pdf

Philip H. S. Torr, Bernardino Romera-Paredes, Philip Hilaire Sean Torr, 2015, ECCV.

Graph Cuts for Minimizing Robust Higher Order Potentials

Pushmeet Kohli, Philip H. S. Torr, Philip H. S. Torr, 2008 .

The Visual Object Tracking VOT2015 Challenge Results

Zhe, Jin, Philip H. S. Torr, 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control

Philip H. S. Torr, S. Whiteson, Wendelin Böhmer, 2020, ArXiv.

Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation

Philip H. S. Torr, C. Rother, F. Kahl, 2017 .

Environment-aware Pedestrian Trajectory Prediction for Autonomous Driving

Philip H. S. Torr, Li Fei-Fei, S. Savarese, 2020 .

Interactive Sketch & Fill: Multiclass Sketch-to-Image Translation

Alexei A. Efros, Philip H. S. Torr, E. Shechtman, 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

The Eighth Visual Object Tracking VOT2020 Challenge Results

Philip H. S. Torr, Seyed Mojtaba Marvasti-Zadeh, Felix Järemo Lawin, 2020, ECCV Workshops.

The Visual Object Tracking VOT2017 Challenge Results

Li, Michael Felsberg, Fahad Shahbaz Khan, 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

The Visual Object Tracking VOT2016 Challenge Results

Gao, Philip H. S. Torr, Noor M. Al-Shakarji, 2016, ECCV Workshops.

Proximal Mean-field for Neural Network Quantization Supplementary Material

Philip H. S. Torr, R. Hartley, P. Dokania, 2019 .

Learning to Hash Naturally Sorts

Philip H. S. Torr, Yuming Shen, Jiaguo Yu, 2022, IJCAI.

Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation: Combining Probabilistic Graphical Models with Deep Learning for Structured Prediction

Carsten Rother, Philip H. S. Torr, Bogdan Savchynskyy, 2018, IEEE Signal Processing Magazine.

Towards the Semantic Weak Generalization Problem in Generative Zero-Shot Learning: Ante-hoc and Post-hoc

Philip H. S. Torr, Yuming Shen, Haofeng Zhang, 2022, ArXiv.

Zero-Shot Logit Adjustment

Philip H. S. Torr, Yuming Shen, Haofeng Zhang, 2022, ArXiv.

Staple: Complementary Learners for Real-Time Tracking

Philip H. S. Torr, Luca Bertinetto, Jack Valmadre, 2015, Computer Vision and Pattern Recognition.

SemanticPaint: interactive segmentation and learning of 3D worlds

Philip H. S. Torr, Julien P. C. Valentin, D. W. Murray, 2015, SIGGRAPH Emerging Technologies.

The StarCraft Multi-Agent Challenge Extended Abstract

Philip H. S. Torr, Jakob N. Foerster, Tim G. J. Rudner, 2019 .

Unifying Training and Inference for Panoptic Segmentation

Xiaojuan Qi, Philip H. S. Torr, Qizhu Li, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Philip H. S. Torr, Viveka Kulharia, Thalaiyasingam Ajanthan, 2018 .

Zero-Shot Logit Adjustment

Philip H. S. Torr, Yuming Shen, Haofeng Zhang, 2022, IJCAI.

Data Dependent Randomized Smoothing pdf

Bernard Ghanem, Philip H. S. Torr, Motasem Alfarra, 2020, UAI.

Efficient Continuous Relaxations for Dense CRF Supplementary Materials

Philip H. S. Torr, Pushmeet Kohli, M. P. Kumar, 2016 .

On using Focal Loss for Neural Network Calibration

Philip H. S. Torr, P. Dokania, S. Golodetz, 2020 .

Towards Solving Inefficiency of Self-supervised Representation Learning

Philip H. S. Torr, Liang Lin, Guangrun Wang, 2021 .

Earn to P Ay a Ttention

Philip H. S. Torr, Nicholas A. Lord, Namhoon Lee, 2018 .

S UBMODULAR OPTIMZATION VIA REINFORCEMENT LEARNING

Philip H. S. Torr, R. Cornish, 2016 .

YouMVOS: An Actor-centric Multi-shot Video Object Segmentation Dataset

Philip H. S. Torr, H. Pfister, J. Tompkin, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

The Ninth Visual Object Tracking VOT2021 Challenge Results

Yong Wang, Ling Shao, Michael Felsberg, 2021, 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).

Catastrophic overfitting is a bug but also a feature

Philip H. S. Torr, Pau de Jorge, P. Frossard, 2022, ArXiv.

How robust are pre-trained models to distribution shift?

Philip H. S. Torr, Julia E. Vogt, Amartya Sanyal, 2022, ArXiv.

Semantic-Aware Auto-Encoders for Self-supervised Representation Learning

Philip H. S. Torr, Guangrun Wang, Liang Lin, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Audio-Visual Classification to Inform Semantic Scene Segmentation

Philip H. S. Torr, Kevin M. Judd, 2016 .

Adversarial Masking for Self-Supervised Learning

Philip H. S. Torr, Adam R. Kosiorek, N. Siddharth, 2022, ICML.

Sample-dependent Adaptive Temperature Scaling for Improved Calibration

Philip H. S. Torr, P. Dokania, Thomas Joy, 2022, arXiv.org.

An Impartial Take to the CNN vs Transformer Robustness Contest

Philip H. S. Torr, P. Dokania, Francesco Pinto, 2022, ECCV.

Occluded Video Instance Segmentation: A Benchmark

Philip H. S. Torr, Serge J. Belongie, A. Yuille, 2021, International Journal of Computer Vision.

SiamMask: A Framework for Fast Online Object Tracking and Segmentation

Philip H. S. Torr, Luca Bertinetto, Weiming Hu, 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Sample-dependent Adaptive Temperature Scaling for Improved Calibration

Philip H. S. Torr, P. Dokania, Thomas Joy, 2022, AAAI.

Illusionary Attacks on Sequential Decision Makers and Countermeasures

Philip H. S. Torr, João F. Henriques, Jakob N. Foerster, 2022, ArXiv.

Communicating via Markov Decision Processes

Philip H. S. Torr, J. Z. Kolter, Luisa M. Zintgraf, 2021, International Conference on Machine Learning.

Adversarial Examples on Segmentation Models Can be Easy to Transfer pdf

Philip H. S. Torr, Volker Tresp, Hengshuang Zhao, 2021, ArXiv.

SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness

Philip H. S. Torr, Hengshuang Zhao, Volker Tresp, 2022, 2207.12391.

How robust are pre-trained models to distribution shift?

Philip H. S. Torr, Julia E. Vogt, Amartya Sanyal, 2022, ArXiv.

On the Robustness of Quality Measures for GANs

Philip H. S. Torr, Juan C. P'erez, Peter Wonka, 2022, ECCV.

Catastrophic overfitting is a bug but also a feature

Philip H. S. Torr, Pau de Jorge, P. Frossard, 2022, ArXiv.

Make Some Noise: Reliable and Efficient Single-Step Adversarial Training

Philip H. S. Torr, Pau de Jorge, Grégory Rogez, 2022, ArXiv.

Robustness via Deep Low-Rank Representations

Philip H. S. Torr, Varun Kanade, P. Dokania, 2018, 1804.07090.

ANCER: Anisotropic Certification via Sample-wise Volume Maximization

Philip H. S. Torr, M. P. Kumar, Bernard Ghanem, 2021, Trans. Mach. Learn. Res..

Learn what matters: cross-domain imitation learning with task-relevant embeddings

Philip H. S. Torr, João F. Henriques, Tim Franzmeyer, 2022, NeurIPS.

Three Birds One Stone: A General Architecture for Salient Object Segmentation, Edge Detection and Skeleton Extraction

Philip H. S. Torr, Ming-Ming Cheng, A. Borji, 2018, 1803.09860.

La rge -s ca le Bina ry Qua dra tic Optimiza tion Us ing S e mide finite Re la xa tion a nd Applica tions

Philip H. S. Torr, Chunhua Shen, Peng Wang, 2016 .

Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning

Philip H. S. Torr, Gui-Song Xia, S. Bai, 2022, 2210.12971.

Dynamic Graph Message Passing Networks

Philip H. S. Torr, Anurag Arnab, Xiangyang Xue, 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Communicating via Markov Decision Processes

Philip H. S. Torr, J. Z. Kolter, Luisa M. Zintgraf, 2021, ICML.

What, Where & How Many? Combining Object Detectors and CRFs

Philip H. S. Torr, Alahari Karteek, Paul Sturgess, 2010 .

Raising the Bar on the Evaluation of Out-of-Distribution Detection

Philip H. S. Torr, P. Dokania, Bor-Chun Chen, 2022, 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).

Large-Scale Unsupervised Semantic Segmentation

Philip H. S. Torr, Ming-Hsuan Yang, Junwei Han, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models pdf

Philip H. S. Torr, Philip H.S. Torr, Brooks Paige, 2020, ICLR.

Struck: Structured output tracking with kernels

Philip H. S. Torr, Amir Saffari, Sam Hare, 2011, 2011 International Conference on Computer Vision.

The Intriguing Effects of Focal Loss on the Calibration of Deep Neural Networks

Philip H. S. Torr, Philip H. S. Torr, Jishnu Mukhoti, 2019 .

PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer

Philip H. S. Torr, Guoying Zhao, Hengshuang Zhao, 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Supplementary Materials: ”TransMix: Attend to Mix for Vision Transformers”

Philip H. S. Torr, A. Yuille, Shuyang Sun, 2022 .

Is synthetic data from generative models ready for image recognition?

Philip H. S. Torr, Xiaojuan Qi, Ruifei He, 2022, ArXiv.

Traditional Classification Neural Networks are Good Generators: They are Competitive with DDPMs and GANs

Philip H. S. Torr, Guangrun Wang, 2022, ArXiv.

Language Matters: A Weakly Supervised Vision-Language Pre-training Approach for Scene Text Detection and Spotting

Philip H. S. Torr, Shijian Lu, Yu Hao, 2022, ECCV.

Structure-Preserving 3D Garment Modeling with Neural Sewing Machines

Philip H. S. Torr, Xiaodan Liang, Guangrun Wang, 2022, NeurIPS.

Open World Entity Segmentation

Philip H. S. Torr, Jiaya Jia, Hengshuang Zhao, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Learning Multimodal VAEs through Mutual Supervision

Philip H. S. Torr, Sebastian M. Schmon, Tom Rainforth, 2021, ICLR.

The Visual Object Tracking VOT2014 Challenge Results

Joost van de Weijer, Philip H. S. Torr, João F. Henriques, 2014, ECCV Workshops.

Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning

Philip H. S. Torr, Jishnu Mukhoti, S. Lim, 2022, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Struck: Structured output tracking with kernels

Philip H. S. Torr, Amir Saffari, Sam Hare, 2011, 2011 International Conference on Computer Vision.

Not Just Pretty Pictures: Text-to-Image Generators Enable Interpretable Interventions for Robust Representations

Philip H. S. Torr, Francesco Pinto, Jian Yuan, 2022, ArXiv.

LUMix: Improving Mixup by Better Modelling Label Uncertainty

Philip H. S. Torr, A. Yuille, Ruifei He, 2022, ArXiv.

How robust is unsupervised representation learning to distribution shift?

Philip H. S. Torr, Julia E. Vogt, Amartya Sanyal, 2022, ICLR.

Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer

Philip H. S. Torr, Piotr Koniusz, Hongguang Zhang, 2020, ACCV.

Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models pdf

Philip H. S. Torr, Philip H.S. Torr, Brooks Paige, 2020, ICLR.

Universal Adaptive Data Augmentation

Philip H. S. Torr, Hengshuang Zhao, Xiaogang Xu, 2022, 2207.06658.

Collaborative Quantization Embeddings for Intra-Subject Prostate MR Image Registration

Philip H. S. Torr, G. Sonn, M. Rusu, 2022, MICCAI.

Deep learning for predicting COVID-19 malignant progression

Philip H. S. Torr, Yu Zhou, X. Bai, 2020, Medical Image Analysis.

CODE: Coherence Based Decision Boundaries for Feature Correspondence

Minh N. Do, Sai-Kit Yeung, Fan Wang, 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models

Philip H. S. Torr, Philip H.S. Torr, Brooks Paige, 2019, NeurIPS.

STEER : Simple Temporal Regularization For Neural ODEs pdf

Philip H. S. Torr, Philip H. S. Torr, Harkirat Singh Behl, 2020, ArXiv.

Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class Incremental Learning

Philip H. S. Torr, Zhouchen Lin, Xiangtai Li, 2023, ICLR.

LAVT: Language-Aware Vision Transformer for Referring Image Segmentation

Philip H. S. Torr, Hengshuang Zhao, Zhao Yang, 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Dual Graph Convolutional Network for Semantic Segmentation pdf

Philip H. S. Torr, Philip H.S. Torr, Li Zhang, 2019, BMVC.

You Never Cluster Alone pdf

Ling Shao, Jie Qin, Philip H. S. Torr, 2021, NeurIPS.

Simulation-Based Inference for Global Health Decisions pdf

Philip H. S. Torr, Atilim Gunecs Baydin, Ara Darzi, 2020, ArXiv.

Dynamic Graph Message Passing Networks

Dan Xu, Philip H. S. Torr, Philip H.S. Torr, 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Deeply Explain CNN Via Hierarchical Decomposition

Philip H. S. Torr, Philip Torr, Liang Wang, 2022, International Journal of Computer Vision.

Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Tao Xiang, Philip H. S. Torr, Philip H.S. Torr, 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Traditional Classification Neural Networks are Good Generators: They are Competitive with DDPMs and GANs

Philip H. S. Torr, Guangrun Wang, 2022, ArXiv.

Controllable Text-to-Image Generation

Philip H. S. Torr, Thomas Lukasiewicz, Xiaojuan Qi, 2019, NeurIPS.

Holistically-Attracted Wireframe Parsing

Gui-Song Xia, Philip H. S. Torr, Philip H.S. Torr, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Occluded Video Instance Segmentation pdf

Song Bai, Xiang Bai, Jiyang Qi, 2021, ArXiv.

Not Just Pretty Pictures: Text-to-Image Generators Enable Interpretable Interventions for Robust Representations

Philip H. S. Torr, Francesco Pinto, Jian Yuan, 2022, ArXiv.

Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation

Philip H. S. Torr, N. Sebe, Dan Xu, 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Stable Rank Normalization for Improved Generalization in Neural Networks and GANs pdf

Philip H. S. Torr, Philip H.S. Torr, Puneet K. Dokania, 2019, ICLR.

Etalumis

Prabhat, Philip H. S. Torr, K. Cranmer, 2019, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis.

Straight to Shapes++: Real-time Instance Segmentation Made More Accurate pdf

Philip H. S. Torr, Philip H.S. Torr, Stuart Golodetz, 2019, ArXiv.

Illusory Attacks: Detectability Matters in Adversarial Attacks on Sequential Decision-Makers

Philip H. S. Torr, João F. Henriques, Adel Bibi, 2022, 2207.10170.

Progressive Skeletonization: Trimming more fat from a network at initialization pdf

Philip H. S. Torr, Harkirat Singh Behl, Philip H.S. Torr, 2020, ICLR.

Gradients without Backpropagation

Barak A. Pearlmutter, Philip H. S. Torr, Frank Wood, 2022, ArXiv.

Gradient Matching for Domain Generalization

Philip H. S. Torr, Jeffrey S. Seely, Nicolas Usunier, 2021, ICLR.

Clustering Generative Adversarial Networks for Story Visualization

Philip H. S. Torr, Thomas Lukasiewicz, Bowen Li, 2022, ACM Multimedia.

Memory-Driven Text-to-Image Generation

Philip H. S. Torr, Thomas Lukasiewicz, Bowen Li, 2022, BMVC.

Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation pdf

Thomas Lukasiewicz, Xiaojuan Qi, Philip H. S. Torr, 2020, NeurIPS.

Edge Guided GANs with Semantic Preserving for Semantic Image Synthesis pdf

Nicu Sebe, Hao Tang, Xiaojuan Qi, 2020, ArXiv.

ManiGAN: Text-Guided Image Manipulation

Thomas Lukasiewicz, Xiaojuan Qi, Philip H. S. Torr, 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

ImageSpirit: Verbal Guided Image Parsing

Vibhav Vineet, Jonathan Warrell, Philip H. S. Torr, 2013, ACM Trans. Graph..

Vision Transformer with Progressive Sampling pdf

Dahua Lin, Philip H. S. Torr, Zhanghui Kuang, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

Make Some Noise: Reliable and Efficient Single-Step Adversarial Training

Philip H. S. Torr, Pau de Jorge, Grégory Rogez, 2022, NeurIPS.

How benign is benign overfitting? pdf

Varun Kanade, Philip H. S. Torr, Philip H.S. Torr, 2020, ICLR.

Towards Adversarial Evaluations for Inexact Machine Unlearning

Philip H. S. Torr, P. Kumaraguru, Amartya Sanyal, 2022, 2201.06640.

Certifying Ensembles: A General Certification Theory with S-Lipschitzness

Philip H. S. Torr, Amartya Sanyal, Adel Bibi, 2023, ICML.

Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge? pdf

Shimon Whiteson, Philip H. S. Torr, Philip H.S. Torr, 2020, ArXiv.

On Tiny Episodic Memories in Continual Learning

Marc'Aurelio Ranzato, Marcus Rohrbach, Philip H. S. Torr, 2019 .

Overcoming the Convex Barrier for Simplex Inputs

Philip H. S. Torr, Harkirat Singh Behl, M. P. Kumar, 2021, NeurIPS.

Branch and Bound for Piecewise Linear Neural Network Verification

Pushmeet Kohli, Philip H. S. Torr, Philip H.S. Torr, 2019, J. Mach. Learn. Res..

Visual Parser: Representing Part-whole Hierarchies with Transformers pdf

Song Bai, Philip H. S. Torr, Philip Torr, 2021, ArXiv.

Separable Flow: Learning Motion Cost Volumes for Optical Flow Estimation

Philip H. S. Torr, Oliver J. Woodford, V. Prisacariu, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

Calibrating Deep Neural Networks using Focal Loss

Philip H. S. Torr, P. Dokania, S. Golodetz, 2020, NeurIPS.

Anchor Diffusion for Unsupervised Video Object Segmentation

Luca Bertinetto, Philip H. S. Torr, Philip H.S. Torr, 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

Deterministic Neural Networks with Inductive Biases Capture Epistemic and Aleatoric Uncertainty

Philip H. S. Torr, Philip H.S. Torr, Jishnu Mukhoti, 2021 .

Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty pdf

Philip H. S. Torr, Philip H.S. Torr, Jishnu Mukhoti, 2021, ArXiv.

Deep Deterministic Uncertainty: A New Simple Baseline

Philip H. S. Torr, Joost R. van Amersfoort, Y. Gal, 2021, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

On Pitfalls in OoD Detection: Predictive Entropy Considered Harmful

Philip H. S. Torr, Joost R. van Amersfoort, Y. Gal, 2021 .

Using Hindsight to Anchor Past Knowledge in Continual Learning pdf

Albert Gordo, David Lopez-Paz, Philip H. S. Torr, 2019, AAAI.

Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning

Philip H. S. Torr, Vincent Y. F. Tan, Jiashi Feng, 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Continual Learning in Low-rank Orthogonal Subspaces

Philip H. S. Torr, P. Dokania, Naeemullah Khan, 2020, NeurIPS.

Staple: Complementary Learners for Real-Time Tracking

Luca Bertinetto, Philip H. S. Torr, Jack Valmadre, 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Siam R-CNN: Visual Tracking by Re-Detection

Philip H. S. Torr, B. Leibe, P. Voigtlaender, 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Influencer Backdoor Attack on Semantic Segmentation

Philip H. S. Torr, Hengshuang Zhao, Jindong Gu, 2023, ArXiv.

Deep Deterministic Uncertainty for Semantic Segmentation pdf

Philip H. S. Torr, Philip H.S. Torr, Yarin Gal, 2021, ArXiv.

RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustness

Philip H. S. Torr, P. Dokania, Francesco Pinto, 2022, ArXiv.

Online Continual Learning Without the Storage Constraint

Philip H. S. Torr, V. Koltun, Ozan Sener, 2023, ArXiv.

Alpha MAML: Adaptive Model-Agnostic Meta-Learning pdf

Philip H. S. Torr, Harkirat Singh Behl, Atilim Gunecs Baydin, 2019, ArXiv.

Global Texture Enhancement for Fake Face Detection in the Wild

Philip H. S. Torr, Jiaya Jia, Xiaojuan Qi, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right?

Philip H. S. Torr, Bernard Ghanem, Adel Bibi, 2023, 2023 IEEE/CVF International Conference on Computer Vision (ICCV).

Real-Time Evaluation in Online Continual Learning: A New Paradigm

Philip H. S. Torr, Bernard Ghanem, Adel Bibi, 2023, ArXiv.

Language Matters: A Weakly Supervised Pre-training Approach for Scene Text Detection and Spotting

Philip H. S. Torr, S. Bai, Shijian Lu, 2022, ArXiv.

Is synthetic data from generative models ready for image recognition?

Philip H. S. Torr, Xiaojuan Qi, Ruifei He, 2022, ICLR.

Do Different Tracking Tasks Require Different Appearance Models? pdf

Luca Bertinetto, Philip H. S. Torr, Philip H.S. Torr, 2021, NeurIPS.

Don't FREAK Out: A Frequency-Inspired Approach to Detecting Backdoor Poisoned Samples in DNNs

Philip H. S. Torr, Bernard Ghanem, Adel Bibi, 2023, 2303.13211.

HOTA: A Higher Order Metric for Evaluating Multi-object Tracking pdf

Philip H. S. Torr, Bastian Leibe, Laura Leal-Taixe, 2020, International Journal of Computer Vision.

MOSE: A New Dataset for Video Object Segmentation in Complex Scenes

Philip H. S. Torr, S. Bai, Chang Liu, 2023, 2023 IEEE/CVF International Conference on Computer Vision (ICCV).

RobustART: Benchmarking Robustness on Architecture Design and Training Techniques pdf

Yan Wang, Dawn Song, Dacheng Tao, 2021, ArXiv.

Box2Seg: Attention Weighted Loss and Discriminative Feature Learning for Weakly Supervised Segmentation

Ambrish Tyagi, Philip H. S. Torr, Viveka Kulharia, 2020, ECCV.

Domain-invariant Stereo Matching Networks pdf

Ruigang Yang, Feihu Zhang, Xiaojuan Qi, 2019, ECCV.

Cross-Modal Deep Face Normals With Deactivable Skip Connections

Philip H. S. Torr, Philip H. S. Torr, Edmond Boyer, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Instance Segmentation of LiDAR Point Clouds

Ruigang Yang, Feihu Zhang, Philip H. S. Torr, 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal Estimation

Renjie Liao, Raquel Urtasun, Xiaojuan Qi, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Deep FusionNet for Point Cloud Semantic Segmentation

Feihu Zhang, Philip H. S. Torr, Jin Fang, 2020, ECCV.

Struck: Structured output tracking with kernels

Philip H. S. Torr, Amir Saffari, Sam Hare, 2011, 2011 International Conference on Computer Vision.

PhysFormer++: Facial Video-Based Physiological Measurement with SlowFast Temporal Difference Transformer

Philip H. S. Torr, Guoying Zhao, Hengshuang Zhao, 2023, International Journal of Computer Vision.

Patch-Based Separable Transformer for Visual Recognition

Philip H. S. Torr, Hengshuang Zhao, Shuyang Sun, 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

BNV-Fusion: Dense 3D Reconstruction using Bi-level Neural Volume Fusion

Philip H. S. Torr, V. Prisacariu, Yansong Tang, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

SemanticPaint: Interactive 3D Labeling and Learning at your Fingertips

Philip H. S. Torr, Julien P. C. Valentin, Pushmeet Kohli, 2015, ACM Trans. Graph..

Solving Inefficiency of Self-supervised Representation Learning pdf

Liang Lin, Philip H. S. Torr, Guangrun Wang, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

Adversarial Metric Attack and Defense for Person Re-Identification

Yingwei Li, Yuyin Zhou, Philip H. S. Torr, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

AttentionGAN: Unpaired Image-to-Image Translation Using Attention-Guided Generative Adversarial Networks

Nicu Sebe, Dan Xu, Philip H. S. Torr, 2019, IEEE Transactions on Neural Networks and Learning Systems.

Interactive Sketch & Fill: Multiclass Sketch-to-Image Translation

Alexei A. Efros, Philip H. S. Torr, Richard Zhang, 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

Video Instance Segmentation 2019: A Winning Approach for Combined Detection, Segmentation, Classification and Tracking.

Philip H. S. Torr, Bastian Leibe, Philip Torr, 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

TransMix: Attend to Mix for Vision Transformers pdf

Song Bai, Alan Yuille, Philip H. S. Torr, 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Towards Robust Prompts on Vision-Language Models

Philip H. S. Torr, Xuezhi Wang, A. Beirami, 2023, ArXiv.

Cloth Interactive Transformer for Virtual Try-On pdf

Ling Shao, Philip H. S. Torr, Philip H.S. Torr, 2021, ArXiv.

XingGAN for Person Image Generation pdf

Nicu Sebe, Philip H. S. Torr, Philip H.S. Torr, 2020, ECCV.

Bipartite Graph Reasoning GANs for Person Image Generation pdf

Nicu Sebe, Philip H. S. Torr, Philip H.S. Torr, 2020, BMVC.

Multi-Channel Attention Selection GANs for Guided Image-to-Image Translation

Philip H. S. Torr, Jason J. Corso, N. Sebe, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Local and Global GANs with Semantic-Aware Upsampling for Image Generation

Philip H. S. Torr, N. Sebe, Hao Tang, 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Let's Take This Online: Adapting Scene Coordinate Regression Network Predictions for Online RGB-D Camera Relocalisation

Luca Bertinetto, Philip H. S. Torr, Jishnu Mukhoti, 2019, 2019 International Conference on 3D Vision (3DV).

Unsupervised Contrastive Domain Adaptation for Semantic Segmentation

Philip H. S. Torr, Stephan R. Richter, V. Koltun, 2022, ArXiv.

Real-Time Evaluation in Online Continual Learning: A New Hope

Philip H. S. Torr, Bernard Ghanem, Adel Bibi, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge pdf

Song Bai, Jiyang Qi, Xinggang Wang, 2021, NeurIPS Datasets and Benchmarks.

FedSR: A Simple and Effective Domain Generalization Method for Federated Learning

Philip H. S. Torr, A. Nguyen, S. Lim, 2022, NeurIPS.

SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness

Philip H. S. Torr, Hengshuang Zhao, Volker Tresp, 2022, ECCV.

Capturing Label Characteristics in VAEs

Philip H. S. Torr, Sebastian M. Schmon, Tom Rainforth, 2020, ICLR.

Learning Regional Attraction for Line Segment Detection

Liangpei Zhang, Gui-Song Xia, Philip H. S. Torr, 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

SemanticPaint: A Framework for the Interactive Segmentation of 3D Scenes

Philip H. S. Torr, Julien P. C. Valentin, D. W. Murray, 2015, ArXiv.

Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models

Philip H. S. Torr, Volker Tresp, Yunpu Ma, 2023, ArXiv.

General Adversarial Defense via Pixel Level and Feature Level Distribution Alignment

Philip H. S. Torr, Hengshuang Zhao, Jiaya Jia, 2020 .

Fairness in AI and Its Long-Term Implications on Society

Philip H. S. Torr, Timothy M. Hospedales, Ondrej Bohdal, 2023, ArXiv.

Language Model Tokenizers Introduce Unfairness Between Languages

Philip H. S. Torr, Emanuele La Malfa, Adel Bibi, 2023, ArXiv.

Provably Correct Physics-Informed Neural Networks

Philip H. S. Torr, M. P. Kumar, Krishnamurthy Dvijotham, 2023, ArXiv.

Image-to-Image Translation with Text Guidance pdf

Thomas Lukasiewicz, Xiaojuan Qi, Philip H. S. Torr, 2020, BMVC.

Amortized Rejection Sampling in Universal Probabilistic Programming

Frank D. Wood, Philip H. S. Torr, Y. Teh, 2019, AISTATS.

KL Guided Domain Adaptation

Philip H. S. Torr, Y. Gal, A. Nguyen, 2021, ICLR.

Learning Discriminative Space–Time Action Parts from Weakly Labelled Videos

Philip H. S. Torr, Fabio Cuzzolin, Michael Sapienza, 2014, International Journal of Computer Vision.

Illusory Attacks: Detectability Matters in Adversarial Attacks on Sequential Decision-Makers

Philip H. S. Torr, João F. Henriques, Adel Bibi, 2022, 2207.10170.

Sequential Optimization for Efficient High-Quality Object Proposal Generation

Venkatesh Saligrama, Philip H. S. Torr, Ziming Zhang, 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Graph Inductive Biases in Transformers without Message Passing

Philip H. S. Torr, P. Dokania, Liheng Ma, 2023, ICML.

Reliable Evaluation of Adversarial Transferability

Philip H. S. Torr, Wenqian Yu, Jindong Gu, 2023, ArXiv.

Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection pdf

Steven McDonagh, Philip H. S. Torr, Sarah Parisot, 2020, ECCV.

Supplementary Material: Dynamic Graph Message Passing Networks

Philip H. S. Torr, Anurag Arnab, Dan Xu, 2020 .

Implicit Communication as Minimum Entropy Coupling pdf

Shimon Whiteson, Philip H. S. Torr, Samuel Sokota, 2021, ArXiv.

Computationally Budgeted Continual Learning: What Does Matter?

Philip H. S. Torr, Bernard Ghanem, P. Dokania, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation

Philip H. S. Torr, Andrew G. Howard, Liang-Chieh Chen, 2023, NeurIPS.

Weakly- and Semi-Supervised Panoptic Segmentation

Philip H. S. Torr, Anurag Arnab, Qizhu Li, 2018, ECCV.

Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants

Philip H. S. Torr, Zhouchen Lin, Bernard Ghanem, 2023, ArXiv.

MP2: A Momentum Contrast Approach for Recommendation with Pointwise and Pairwise Learning

Philip H. S. Torr, Mingming Gong, Yuchen Guo, 2022, SIGIR.

Semantics-Aware Dynamic Localization and Refinement for Referring Image Segmentation

Philip H. S. Torr, Hengshuang Zhao, Kai Chen, 2023, AAAI.

Point Transformer

Philip H. S. Torr, Jiaya Jia, V. Koltun, 2020, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

MobileBrick: Building LEGO for 3D Reconstruction on Mobile Devices

Philip H. S. Torr, V. Prisacariu, Jiawang Bian, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

DGPose: Deep Generative Models for Human Body Analysis

Philip H. S. Torr, Rodrigo de Bem, N. Siddharth, 2018, International Journal of Computer Vision.

A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models

Philip H. S. Torr, Shuo Chen, Volker Tresp, 2023, ArXiv.

SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness

Philip H. S. Torr, Hengshuang Zhao, Volker Tresp, 2022, ECCV.

OxfordTVG-HIC: Can Machine Make Humorous Captions from Images?

Philip H. S. Torr, Mohamed Elhoseiny, Shuyang Sun, 2023, ArXiv.

On the Robustness of Semantic Segmentation Models to Adversarial Attacks

Philip H. S. Torr, Anurag Arnab, O. Mikšík, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Reliability in Semantic Segmentation: Are we on the Right Track?

Philip H. S. Torr, Pau de Jorge, Grégory Rogez, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

AutoSimulate: (Quickly) Learning Synthetic Data Generation

Vibhav Vineet, Philip H. S. Torr, Harkirat Singh Behl, 2020, ECCV.

STEER : Simple Temporal Regularization For Neural ODEs

Philip H. S. Torr, Harkirat Singh Behl, Emilien Dupont, 2020, NeurIPS.

The Visual Object Tracking VOT2014 Challenge Results

Joost van de Weijer, Philip H. S. Torr, João F. Henriques, 2014, ECCV Workshops.

Scaling the Convex Barrier with Sparse Dual Algorithms

Philip H. S. Torr, Harkirat Singh Behl, Philip H.S. Torr, 2021, ArXiv.

Linear Complexity Self-Attention with 3 rd Order Polynomials

Philip H. S. Torr, Grigorios G. Chrysos, S. Zafeiriou, 2023 .

Deeply Supervised Salient Object Detection with Short Connections

Philip H. S. Torr, Z. Tu, Ming-Ming Cheng, 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

ROAM: A Rich Object Appearance Model with Application to Rotoscoping

Philip H. S. Torr, P. Pérez, P. Bouthemy, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Fine-tuning can cripple your foundation model; preserving features may be the solution

Philip H. S. Torr, Y. Gal, P. Dokania, 2023, ArXiv.

Ieee Transaction on Pattern Analysis and Machine Intelligence 1 Object Proposal Generation Using Two-stage Cascade Svms

Philip H. S. Torr, Ziming Zhang, 2022 .

Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition pdf

Pushmeet Kohli, Philip H. S. Torr, Philip H.S. Torr, 2021, ArXiv.

Lagrangian Decomposition for Neural Network Verification

Pushmeet Kohli, Philip H. S. Torr, Philip H.S. Torr, 2020, UAI.

Fairness in AI and Its Long-Term Implications on Society

Philip H. S. Torr, Timothy M. Hospedales, Ondrej Bohdal, 2023, ArXiv.

Cloth Interactive Transformer for Virtual Try-On

Philip H. S. Torr, Bin Ren, Runwei Ding, 2021, ACM Transactions on Multimedia Computing, Communications, and Applications.

SemanticPaint

Philip H. S. Torr, Julien P. C. Valentin, Pushmeet Kohli, 2015, ACM Transactions on Graphics.

TIPI: Test Time Adaptation with Transformation Invariance

Philip H. S. Torr, A. Nguyen, S. Lim, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments

Philip H. S. Torr, Jiaya Jia, Hengshuang Zhao, 2022, ArXiv.

Bipartite Graph Reasoning GANs for Person Pose and Facial Image Synthesis

Philip H. S. Torr, N. Sebe, Hao Tang, 2022, International Journal of Computer Vision.

Deconstructed Generation-Based Zero-Shot Model

Philip H. S. Torr, Yuming Shen, Haofeng Zhang, 2022, AAAI.

Deeply Explain CNN Via Hierarchical Decomposition

Philip H. S. Torr, Mingg-Ming Cheng, Liang Wang, 2022, International Journal of Computer Vision.

Language Model Tokenizers Introduce Unfairness Between Languages

Philip H. S. Torr, Emanuele La Malfa, Adel Bibi, 2023, NeurIPS.

The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results

Philip H. S. Torr, Noor M. Al-Shakarji, Rafael Martin Nieto, 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS pdf

Philip H. S. Torr, Philip H.S. Torr, Martin Strohmeier, 2021, ArXiv.

Recurrent Instance Segmentation

Philip H. S. Torr, Bernardino Romera-Paredes, 2015, ECCV.

LAVT: Language-Aware Vision Transformer for Referring Image Segmentation

Philip H. S. Torr, Hengshuang Zhao, Zhao Yang, 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Point Transformer — Supplementary Material

Philip H. S. Torr, Jiaya Jia, V. Koltun, 2021 .

Language Matters: A Weakly Supervised Vision-Language Pre-training Approach for Scene Text Detection and Spotting

Philip H. S. Torr, Shijian Lu, Yu Hao, 2022, ECCV.

Occluded Video Instance Segmentation: A Benchmark

Song Bai, Jiyang Qi, Xinggang Wang, 2021, International Journal of Computer Vision.

Metric Attack and Defense for Person Re-identification

Philip H. S. Torr, S. Bai, Yuyin Zhou, 2019, 1901.10650.

Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning

Philip H. S. Torr, L. Zhang, Nan Xue, 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union

Philip H. S. Torr, T. Tuytelaars, L. Gool, 2023, ArXiv.

Universal Adaptive Data Augmentation

Philip H. S. Torr, Hengshuang Zhao, Xiaogang Xu, 2022, IJCAI.

From Categories to Classifier: Name-Only Continual Learning by Exploring the Web

Philip H. S. Torr, Adel Bibi, S. Lim, 2023, ArXiv.

Lessons from reinforcement learning for biological representations of space

Philip H. S. Torr, Nantas Nardelli, A. Glennerster, 2019, Vision Research.

Adversarial Metric Attack and Defense for Person Re-Identification

Philip H. S. Torr, S. Bai, Yuyin Zhou, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Scaling the Convex Barrier with Sparse Dual Algorithms pdf

Philip H. S. Torr, Harkirat Singh Behl, Philip H.S. Torr, 2021, ArXiv.

Placing Objects in Context via Inpainting for Out-of-distribution Segmentation

Philip H. S. Torr, Pau de Jorge, Grégory Rogez, 2024, ArXiv.

for Object Tracking

Philip H. S. Torr, A. Vedaldi, Luca Bertinetto, 2022 .

Secret Collusion Among Generative AI Agents

Philip H. S. Torr, C. S. D. Witt, Vijay Bolina, 2024, ArXiv.

Using Mixup as a Regularizer Can Surprisingly Improve Accuracy & Out-of-Distribution Robustness

Philip H. S. Torr, P. Dokania, Francesco Pinto, 2022, NeurIPS.

When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitations

Philip H. S. Torr, Aleksandar Petrov, Adel Bibi, 2023, ArXiv.

Label Delay in Online Continual Learning

Philip H. S. Torr, M. Elhoseiny, Wenxuan Zhang, 2023, 2312.00923.

RanDumb: A Simple Approach that Questions the Efficacy of Continual Representation Learning

Philip H. S. Torr, P. Kumaraguru, Ozan Sener, 2024, 2402.08823.

Online Continual Learning Without the Storage Constraint

Philip H. S. Torr, V. Koltun, Ozan Sener, 2023, ArXiv.

Edge Guided GANs with Semantic Preserving for Semantic Image Synthesis

Philip H. S. Torr, N. Sebe, Xiaojuan Qi, 2020, ArXiv.

Ieee Transaction on Pattern Analysis and Machine Intelligence 1 Sequential Optimization for Efficient High-quality Object Proposal Generation

Philip H. S. Torr, Xi Chen, Ming-Ming Cheng, 2022 .

Measuring Uncertainty in Graph Cut Solutions - Efficiently Computing Min-marginal Energies Using Dynamic Graph Cuts

Philip H. S. Torr, Pushmeet Kohli, 2006, ECCV.

R ELATING BY C ONTRASTING : A D ATA - EFFICIENT F RAMEWORK FOR M ULTIMODAL DGMs

Philip H. S. Torr, Brooks Paige, Yuge Shi, 2021 .

Rethinking Semi-Supervised Learning in VAEs

Philip H. S. Torr, Sebastian M. Schmon, Tom Rainforth, 2020, ArXiv.

Task-Agnostic Robust Representation Learning

Philip H. S. Torr, A. Nguyen, S. Lim, 2022, ArXiv.

Diversified Dynamic Routing for Vision Tasks

Philip H. S. Torr, Yanwei Li, Adel Bibi, 2022, ECCV Workshops.

On Batch Normalisation for Approximate Bayesian Inference pdf

Philip H. S. Torr, Philip H.S. Torr, Jishnu Mukhoti, 2020, ArXiv.

Learning to Sample the Most Useful Training Patches from Images pdf

Liang Chen, Philip H. S. Torr, Gregory Slabaugh, 2020, ArXiv.