Graph-based method for human-object interactions detection

[1]  Jian Dong,et al.  Visual relationship detection based on bidirectional recurrent neural network , 2020, Multimedia Tools and Applications.

[2]  Rui Li,et al.  Multi-stream neural network fused with local information and global information for HOI detection , 2020, Applied Intelligence.

[3]  Hao Wang,et al.  Interaction behavior recognition from multiple views , 2020 .

[4]  Chao Ching Wang,et al.  Visual relationship detection based on bidirectional recurrent neural network , 2019, Multimedia Tools and Applications.

[5]  Jun Wang,et al.  Abnormal event detection with semi-supervised sparse topic model , 2019, Neural Computing and Applications.

[6]  R. Safabakhsh,et al.  A motion-aware ConvLSTM network for action recognition , 2019, Applied Intelligence.

[7]  Song-Chun Zhu,et al.  Learning Human-Object Interactions by Graph Parsing Neural Networks , 2018, ECCV.

[8]  Fumin Shen,et al.  Word-to-region attention network for visual question answering , 2018, Multimedia Tools and Applications.

[9]  Cewu Lu,et al.  Pairwise Body-Part Attention for Recognizing Human-Object Interactions , 2018, ECCV.

[10]  Xinlei Chen,et al.  Iterative Visual Reasoning Beyond Convolutions , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[11]  Li Fei-Fei,et al.  Scaling Human-Object Interaction Recognition Through Zero-Shot Learning , 2018, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[12]  Kristen Grauman,et al.  Im2Flow: Motion Hallucination from Static Images for Action Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[13]  Gang Sun,et al.  Squeeze-and-Excitation Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[14]  Xiaogang Wang,et al.  Scene Graph Generation from Objects, Phrases and Region Captions , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[15]  Ivan Laptev,et al.  Weakly-Supervised Learning of Visual Relations , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[16]  Gang Wang,et al.  Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks , 2017, IEEE Transactions on Image Processing.

[17]  Li Fei-Fei,et al.  Inferring and Executing Programs for Visual Reasoning , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[18]  Kaiming He,et al.  Detecting and Recognizing Human-Object Interactions , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[19]  Garrison W. Cottrell,et al.  Understanding Convolution for Semantic Segmentation , 2017, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[20]  Jia Deng,et al.  Learning to Detect Human-Object Interactions , 2017, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[21]  Danfei Xu,et al.  Scene Graph Generation by Iterative Message Passing , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Abhinav Gupta,et al.  The More You Know: Using Knowledge Graphs for Image Classification , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Serge J. Belongie,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Anton van den Hengel,et al.  Graph-Structured Representations for Visual Question Answering , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[26]  Michael S. Bernstein,et al.  Visual Relationship Detection with Language Priors , 2016, ECCV.

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

[28]  Silvio Savarese,et al.  Structural-RNN: Deep Learning on Spatio-Temporal Graphs , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Cordelia Schmid,et al.  P-CNN: Pose-Based CNN Features for Action Recognition , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[30]  Xinlei Chen,et al.  Mind's eye: A recurrent visual representation for image caption generation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  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.

[32]  Jitendra Malik,et al.  Visual Semantic Role Labeling , 2015, ArXiv.

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

[34]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[35]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[36]  Jian-Huang Lai,et al.  Recognising Human-Object Interaction via Exemplar Based Modelling , 2013, 2013 IEEE International Conference on Computer Vision.

[37]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[38]  Charless C. Fowlkes,et al.  Discriminative models for static human-object interactions , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[39]  Fei-Fei Li,et al.  Modeling mutual context of object and human pose in human-object interaction activities , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[40]  Larry S. Davis,et al.  Observing Human-Object Interactions: Using Spatial and Functional Compatibility for Recognition , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[41]  A. Torralba,et al.  The role of context in object recognition , 2007, Trends in Cognitive Sciences.

[42]  F. Scarselli,et al.  A new model for learning in graph domains , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[43]  C. Schmid,et al.  Vision, Perception and Multimedia Understanding Weakly Supervised Learning of Interactions between Humans and Objects Weakly Supervised Learning of Interactions between Humans and Objects , 2022 .