Fetching the paper…
Reading the bibliography…
This paper addresses the task of detecting and recognizing human-object interactions (HOI) in images and videos.
Elman, J.L.: Finding structure in time. Cognitive science (1990)
1990
Earlier work this paper cites.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation (1997)
1997
Earlier work this paper cites.
Gupta, A., Davis, L.S.: Objects in action: An approach for combining action understanding and object perception. In: CVPR (2007)
2007
Earlier work this paper cites.
Gupta, A., Kembhavi, A., Davis, L.S.: Observing human-object interactions: Using spatial and functional compatibility for recognition. PAMI (2009)
2009
Earlier work this paper cites.
Yao, B., Fei-Fei, L.: Grouplet: A structured image representation for recognizing human and object interactions. In: CVPR (2010)
2010
Earlier work this paper cites.
Yao, B., Fei-Fei, L.: Modeling mutual context of object and human pose in human-object interaction activities. In: CVPR (2010)
2010
Earlier work this paper cites.
Delaitre, V., Sivic, J., Laptev, I.: Learning person-object interactions for action recognition in still images. In: NIPS (2011)
2011
Earlier work this paper cites.
Yao, B., Jiang, X., Khosla, A., Lin, A.L., Guibas, L., Fei-Fei, L.: Human action recognition by learning bases of action attributes and parts. In: ICCV (2011)
2011
Earlier work this paper cites.
Desai, C., Ramanan, D.: Detecting actions, poses, and objects with relational phraselets. In: ECCV (2012)
2012
Earlier work this paper cites.
Hu, J.F., Zheng, W.S., Lai, J., Gong, S., Xiang, T.: Recognising human-object interaction via exemplar based modelling. In: ICCV (2013)
2013
Earlier work this paper cites.
Koppula, H.S., Gupta, R., Saxena, A.: Learning human activities and object affordances from RGB-D videos. The International Journal of Robotics Research (2013)
2013
Earlier work this paper cites.
Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder–decoder approaches. Syntax, Semantics and Structure in Statistical Translation p. 103 (2014)
2014
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: ECCV (2014)
2014
Earlier work this paper cites.
Tompson, J.J., Jain, A., LeCun, Y., Bregler, C.: Joint training of a convolutional network and a graphical model for human pose estimation. In: NIPS (2014)
2014
Earlier work this paper cites.
Chao, Y.W., Wang, Z., He, Y., Wang, J., Deng, J.: HICO: A benchmark for recognizing human-object interactions in images. In: ICCV (2015)
2015
Earlier work this paper cites.
Chen, L.C., Schwing, A., Yuille, A., Urtasun, R.: Learning deep structured models. In: ICML (2015)
2015
Earlier work this paper cites.
Girshick, R.: Fast R-CNN. In: ICCV (2015)
2015
Earlier work this paper cites.
Gupta, S., Malik, J.: Visual semantic role labeling. arXiv preprint arXiv:1505.04474 (2015)
2015
Cited alongside, same era.
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. In: NIPS (2015)
2015
Cited alongside, same era.
Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.c.: Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In: NIPS (2015)
2015
Cited alongside, same era.
2015
Cited alongside, same era.
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. PAMI (2016)
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: Deformable convolutional networks. In: ICCV (2017)
2017
Later among the works it cites.
Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: ICML (2017)
2017
Later among the works it cites.
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)
2017
Later among the works it cites.
Li, R., Tapaswi, M., Liao, R., Jia, J., Urtasun, R., Fidler, S.: Situation recognition with graph neural networks. In: ICCV (2017)
2017
Later among the works it cites.
Liang, X., Lin, L., Shen, X., Feng, J., Yan, S., Xing, E.P.: Interpretable structure-evolving lstm. In: ICCV (2017)
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: NIPS (2016)
2016
Cited alongside, same era.
Jain, A., Zamir, A.R., Savarese, S., Saxena, A.: Structural-RNN: Deep learning on spatio-temporal graphs. In: CVPR (2016)
2016
Cited alongside, same era.
Koppula, H.S., Saxena, A.: Anticipating human activities using object affordances for reactive robotic response. PAMI (2016)
2016
Cited alongside, same era.
Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.: Gated graph sequence neural networks. In: ICLR (2016)
2016
Cited alongside, same era.
Liang, X., Shen, X., Feng, J., Lin, L., Yan, S.: Semantic object parsing with graph lstm. In: ECCV (2016)
2016
Cited alongside, same era.
Mallya, A., Lazebnik, S.: Learning models for actions and person-object interactions with transfer to question answering. In: ECCV (2016)
2016
Cited alongside, same era.
Marino, K., Salakhutdinov, R., Gupta, A.: The more you know: Using knowledge graphs for image classification. In: CVPR (2016)
2016
Cited alongside, same era.
Park, S., Nie, X., Zhu, S.C.: Attribute and-or grammar for joint parsing of human pose, parts and attributes. PAMI (2017)
2017
Later among the works it cites.
Qi, S., Huang, S., Wei, P., Zhu, S.C.: Predicting human activities using stochastic grammar. In: ICCV (2017)
2017
Later among the works it cites.
Simonovsky, M., Komodakis, N.: Dynamic edge-conditioned filters in convolutional neural networks on graphs. CVPR (2017)
2017
Later among the works it cites.
Teney, D., Liu, L., Hengel, A.v.d.: Graph-structured representations for visual question answering. In: CVPR (2017)
2017
Later among the works it cites.
Xu, D., Zhu, Y., Choy, C.B., Fei-Fei, L.: Scene graph generation by iterative message passing. In: ICCV (2017)
2017
Later among the works it cites.
Yuan, Y., Liang, X., Wang, X., Yeung, D.Y., Gupta, A.: Temporal dynamic graph LSTM for action-driven video object detection. In: ICCV (2017)
2017
Later among the works it cites.
Chao, Y.W., Liu, Y., Liu, X., Zeng, H., Deng, J.: Learning to detect human-object interactions (2018)
2018
Closest in time.
Fang, H.S., Xu, Y., Wang, W., Zhu, S.C.: Learning pose grammar to encode human body configuration for 3d pose estimation. In: AAAI (2018)
2018
Closest in time.
Gkioxari, G., Girshick, R., Dollár, P., He, K.: Detecting and recognizing human-object interactions. In: CVPR (2018)
2018
Closest in time.
Qi, S., Jia, B., Zhu, S.C.: Generalized earley parser: Bridging symbolic grammars and sequence data for future prediction. In: ICML (2018)
2018
Closest in time.
Shen, L., Yeung, S., Hoffman, J., Mori, G., Fei-Fei, L.: Scaling human-object interaction recognition through zero-shot learning (2018)
2018
Closest in time.
Wang, W., Xu, Y., Shen, J., Zhu, S.C.: Attentive fashion grammar network for fashion landmark detection and clothing category classification. In: CVPR (2018)
2018
Closest in time.