Guided Attention in CNNs for Occluded Pedestrian Detection and Re-identification

[1]  Gang Li,et al.  Learning Hierarchical Graph for Occluded Pedestrian Detection , 2020, ACM Multimedia.

[2]  Ming Yang,et al.  Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Zequn Jie,et al.  NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Xiangyu Zhang,et al.  Detection in Crowded Scenes: One Proposal, Multiple Predictions , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Yi Yang,et al.  Random Erasing Data Augmentation , 2017, AAAI.

[7]  Fahad Shahbaz Khan,et al.  Count- and Similarity-Aware R-CNN for Pedestrian Detection , 2020, ECCV.

[8]  Fahad Shahbaz Khan,et al.  Mask-Guided Attention Network for Occluded Pedestrian Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[9]  Ming Yang,et al.  Discriminative Feature Transformation for Occluded Pedestrian Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[10]  Yunhong Wang,et al.  Adaptive NMS: Refining Pedestrian Detection in a Crowd , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Wei Liu,et al.  High-Level Semantic Feature Detection: A New Perspective for Pedestrian Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Xiaoming Liu,et al.  Pedestrian Detection With Autoregressive Network Phases , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Shiliang Pu,et al.  Small-Scale Pedestrian Detection Based on Topological Line Localization and Temporal Feature Aggregation , 2018, ECCV.

[14]  Wei Liu,et al.  Learning Efficient Single-Stage Pedestrian Detectors by Asymptotic Localization Fitting , 2018, ECCV.

[15]  Chunluan Zhou,et al.  Bi-box Regression for Pedestrian Detection and Occlusion Estimation , 2018, ECCV.

[16]  Gang Wang,et al.  Graininess-Aware Deep Feature Learning for Pedestrian Detection , 2018, ECCV.

[17]  Shifeng Zhang,et al.  Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd , 2018, ECCV.

[18]  Gunhee Kim,et al.  Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[19]  Bingbing Ni,et al.  Pose Transferrable Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[20]  Kaiqi Huang,et al.  Adversarially Occluded Samples for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[21]  Jing Xu,et al.  Attention-Aware Compositional Network for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[22]  Tao Mei,et al.  Part-Aligned Bilinear Representations for Person Re-identification , 2018, ECCV.

[23]  Bernt Schiele,et al.  Towards Reaching Human Performance in Pedestrian Detection , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Gang Wang,et al.  Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[25]  Shaogang Gong,et al.  Harmonious Attention Network for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[26]  Haiqing Li,et al.  Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-free Approach , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[27]  M. Saquib Sarfraz,et al.  A Pose-Sensitive Embedding for Person Re-identification with Expanded Cross Neighborhood Re-ranking , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[28]  Yuning Jiang,et al.  Repulsion Loss: Detecting Pedestrians in a Crowd , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[29]  Yi Yang,et al.  A Discriminatively Learned CNN Embedding for Person Reidentification , 2016, ACM Trans. Multim. Comput. Commun. Appl..

[30]  Shuicheng Yan,et al.  Scale-Aware Fast R-CNN for Pedestrian Detection , 2015, IEEE Transactions on Multimedia.

[31]  Ming Tang,et al.  PCN: Part and Context Information for Pedestrian Detection with CNNs , 2018, BMVC.

[32]  Chunluan Zhou,et al.  Multi-label Learning of Part Detectors for Heavily Occluded Pedestrian Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[33]  Shiliang Zhang,et al.  Pose-Driven Deep Convolutional Model for Person Re-identification , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[34]  Xiaoming Liu,et al.  Illuminating Pedestrians via Simultaneous Detection and Segmentation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[35]  Bolei Zhou,et al.  Network Dissection: Quantifying Interpretability of Deep Visual Representations , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Bernt Schiele,et al.  CityPersons: A Diverse Dataset for Pedestrian Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[37]  Liang Zheng,et al.  Re-ranking Person Re-identification with k-Reciprocal Encoding , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Yi Yang,et al.  Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[39]  Jungwon Lee,et al.  Fused DNN: A Deep Neural Network Fusion Approach to Fast and Robust Pedestrian Detection , 2016, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

[40]  Davide Modolo,et al.  Do Semantic Parts Emerge in Convolutional Neural Networks? , 2016, International Journal of Computer Vision.

[41]  Shuicheng Yan,et al.  End-to-End Comparative Attention Networks for Person Re-Identification , 2016, IEEE Transactions on Image Processing.

[42]  Qi Tian,et al.  Person Re-identification in the Wild , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[43]  Xiaogang Wang,et al.  Joint Detection and Identification Feature Learning for Person Search , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[44]  Yi Yang,et al.  Person Re-identification: Past, Present and Future , 2016, ArXiv.

[45]  Qi Tian,et al.  MARS: A Video Benchmark for Large-Scale Person Re-Identification , 2016, ECCV.

[46]  Francesco Solera,et al.  Performance Measures and a Data Set for Multi-target, Multi-camera Tracking , 2016, ECCV Workshops.

[47]  Gang Wang,et al.  A Siamese Long Short-Term Memory Architecture for Human Re-identification , 2016, ECCV.

[48]  Rogério Schmidt Feris,et al.  A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection , 2016, ECCV.

[49]  Liang Lin,et al.  Is Faster R-CNN Doing Well for Pedestrian Detection? , 2016, ECCV.

[50]  Nanning Zheng,et al.  Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[51]  Bernt Schiele,et al.  DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model , 2016, ECCV.

[52]  Xiaogang Wang,et al.  Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[53]  Sebastian Ramos,et al.  The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[54]  Jia Deng,et al.  Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.

[55]  B. Schiele,et al.  How Far are We from Solving Pedestrian Detection? , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[56]  Kavita Bala,et al.  Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[57]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[58]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[59]  Xiang Li,et al.  Partial Person Re-Identification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[60]  Qi Tian,et al.  Scalable Person Re-identification: A Benchmark , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[61]  Xiaogang Wang,et al.  Deep Learning Strong Parts for Pedestrian Detection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[62]  Liang Lin,et al.  Deep feature learning with relative distance comparison for person re-identification , 2015, Pattern Recognit..

[63]  Michael Jones,et al.  An improved deep learning architecture for person re-identification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[64]  Andrew Zisserman,et al.  Spatial Transformer Networks , 2015, NIPS.

[65]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[66]  Bernt Schiele,et al.  Taking a deeper look at pedestrians , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[67]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[68]  Xiaogang Wang,et al.  Pedestrian detection aided by deep learning semantic tasks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[69]  Joachim Denzler,et al.  Part Detector Discovery in Deep Convolutional Neural Networks , 2014, ACCV.

[70]  Bernt Schiele,et al.  Ten Years of Pedestrian Detection, What Have We Learned? , 2014, ECCV Workshops.

[71]  Shengcai Liao,et al.  Deep Metric Learning for Person Re-identification , 2014, 2014 22nd International Conference on Pattern Recognition.

[72]  Anton van den Hengel,et al.  Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features , 2014, ECCV.

[73]  Xiaogang Wang,et al.  DeepReID: Deep Filter Pairing Neural Network for Person Re-identification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[74]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[75]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[76]  Luc Van Gool,et al.  Handling Occlusions with Franken-Classifiers , 2013, 2013 IEEE International Conference on Computer Vision.

[77]  Xiaogang Wang,et al.  Joint Deep Learning for Pedestrian Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[78]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[79]  Xiaogang Wang,et al.  A discriminative deep model for pedestrian detection with occlusion handling , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[80]  Pietro Perona,et al.  Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[81]  Dariu Gavrila,et al.  Multi-cue pedestrian classification with partial occlusion handling , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[82]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[83]  Shaogang Gong,et al.  Associating Groups of People , 2009, BMVC.

[84]  Luc Van Gool,et al.  A mobile vision system for robust multi-person tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.