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Existing visual question answering methods often suffer from cross-modal spurious correlations and oversimplified event-level reasoning processes that fail to capture event temporality, causality, and dynamics spanning over the video.
C. Fan, X. Zhang, S. Zhang, W. Wang, C. Zhang, and H. Huang, “Heterogeneous memory enhanced multimodal attention model for video question answering,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 1999–2007
2007
Earlier work this paper cites.
D. Chen and W. B. Dolan, “Collecting highly parallel data for paraphrase evaluation,” in Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies , 2011, pp. 190–200
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems , vol. 25, pp. 1097–1105, 2012
2012
Earlier work this paper cites.
E. Bareinboim and J. Pearl, “Controlling selection bias in causal inference,” in Artificial Intelligence and Statistics . PMLR, 2012, pp. 100–108
2012
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems , vol. 27, 2014
2014
Earlier work this paper cites.
J. Pennington, R. Socher, and C. D. Manning, “Glove: Global vectors for word representation,” in Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) , 2014, pp. 1532–1543
2014
Earlier work this paper cites.
S. Sukhbaatar, A. Szlam, J. Weston, and R. Fergus, “End-to-end memory networks,” Advances in Neural Information Processing Systems , vol. 2015, pp. 2440–2448, 2015
2015
Earlier work this paper cites.
S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra, C. L. Zitnick, and D. Parikh, “Vqa: Visual question answering,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 2425–2433
2015
Earlier work this paper cites.
M. Ren, R. Kiros, and R. Zemel, “Exploring models and data for image question answering,” Advances in neural information processing systems , vol. 28, pp. 2953–2961, 2015
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
J. Pearl, M. Glymour, and N. P. Jewell, Causal inference in statistics: A primer . John Wiley & Sons, 2016
2016
Earlier work this paper cites.
Z. Yang, X. He, J. Gao, L. Deng, and A. Smola, “Stacked attention networks for image question answering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 21–29
2016
Earlier work this paper cites.
J. Xu, T. Mei, T. Yao, and Y. Rui, “Msr-vtt: A large video description dataset for bridging video and language,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 5288–5296
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
R. Krishna, K. Hata, F. Ren, L. Fei-Fei, and J. Carlos Niebles, “Dense-captioning events in videos,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 706–715
2017
Earlier work this paper cites.
A. Das, S. Kottur, K. Gupta, A. Singh, D. Yadav, J. M. Moura, D. Parikh, and D. Batra, “Visual dialog,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 326–335
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
D. Xu, Z. Zhao, J. Xiao, F. Wu, H. Zhang, X. He, and Y. Zhuang, “Video question answering via gradually refined attention over appearance and motion,” in Proceedings of the 25th ACM international conference on Multimedia , 2017, pp. 1645–1653
2017
Earlier work this paper cites.
Y. Jang, Y. Song, Y. Yu, Y. Kim, and G. Kim, “Tgif-qa: Toward spatio-temporal reasoning in visual question answering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2758–2766
2017
Earlier work this paper cites.
J. Carreira and A. Zisserman, “Quo vadis, action recognition? a new model and the kinetics dataset,” in proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 6299–6308
2017
Earlier work this paper cites.
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1492–1500
2017
Earlier work this paper cites.
B. Zhou, A. Andonian, A. Oliva, and A. Torralba, “Temporal relational reasoning in videos,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 803–818
2018
Earlier work this paper cites.
P. Anderson, Q. Wu, D. Teney, J. Bruce, M. Johnson, N. Sünderhauf, I. Reid, S. Gould, and A. Van Den Hengel, “Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3674–3683
2018
Earlier work this paper cites.
P. Anderson, X. He, C. Buehler, D. Teney, M. Johnson, S. Gould, and L. Zhang, “Bottom-up and top-down attention for image captioning and visual question answering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 6077–6086
2018
Earlier work this paper cites.
Y. Liu, Z. Lu, J. Li, T. Yang, and C. Yao, “Global temporal representation based cnns for infrared action recognition,” IEEE Signal Processing Letters , vol. 25, no. 6, pp. 848–852, 2018
2018
Earlier work this paper cites.
Y. Liu, Z. Lu, J. Li, and T. Yang, “Hierarchically learned view-invariant representations for cross-view action recognition,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 29, no. 8, pp. 2416–2430, 2018
2018
Earlier work this paper cites.
C.-Y. Ma, A. Kadav, I. Melvin, Z. Kira, G. AlRegib, and H. P. Graf, “Attend and interact: Higher-order object interactions for video understanding,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 6790–6800
2018
Earlier work this paper cites.
G. Stanovsky, J. Michael, L. Zettlemoyer, and I. Dagan, “Supervised open information extraction,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) , 2018, pp. 885–895
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
J. Pearl and D. Mackenzie, The book of why: the new science of cause and effect . Basic books, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
J. Gao, R. Ge, K. Chen, and R. Nevatia, “Motion-appearance co-memory networks for video question answering,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 6576–6585
2018
Earlier work this paper cites.
M. I. H. Chowdhury, K. Nguyen, S. Sridharan, and C. Fookes, “Hierarchical relational attention for video question answering,” in 2018 25th IEEE International Conference on Image Processing (ICIP) . IEEE, 2018, pp. 599–603
2018
Cited alongside, same era.
R. Zellers, Y. Bisk, A. Farhadi, and Y. Choi, “From recognition to cognition: Visual commonsense reasoning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 6720–6731
2019
Cited alongside, same era.
X. Li, J. Song, L. Gao, X. Liu, W. Huang, X. He, and C. Gan, “Beyond rnns: Positional self-attention with co-attention for video question answering,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, no. 01, 2019, pp. 8658–8665
2019
Cited alongside, same era.
Y. Jang, Y. Song, C. D. Kim, Y. Yu, Y. Kim, and G. Kim, “Video question answering with spatio-temporal reasoning,” International Journal of Computer Vision , vol. 127, no. 10, pp. 1385–1412, 2019
2019
J. Park, J. Lee, and K. Sohn, “Bridge to answer: Structure-aware graph interaction network for video question answering,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 15 526–15 535
2021
Later among the works it cites.
Y. Niu, K. Tang, H. Zhang, Z. Lu, X.-S. Hua, and J.-R. Wen, “Counterfactual vqa: A cause-effect look at language bias,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 12 700–12 710
2021
Later among the works it cites.
X. Yang, H. Zhang, G. Qi, and J. Cai, “Causal attention for vision-language tasks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 9847–9857
2021
Later among the works it cites.
T. Wang, C. Zhou, Q. Sun, and H. Zhang, “Causal attention for unbiased visual recognition,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 3091–3100
2021
Later among the works it cites.
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Cited alongside, same era.
Y. Liu, Z. Lu, J. Li, T. Yang, and C. Yao, “Deep image-to-video adaptation and fusion networks for action recognition,” IEEE Transactions on Image Processing , vol. 29, pp. 3168–3182, 2019
2019
Cited alongside, same era.
R. Girdhar, J. Carreira, C. Doersch, and A. Zisserman, “Video action transformer network,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 244–253
2019
Cited alongside, same era.
H. Huang, L. Zhou, W. Zhang, J. J. Corso, and C. Xu, “Dynamic graph modules for modeling object-object interactions in activity recognition,” in British Machine Vision Conference , 2019
2019
Cited alongside, same era.
Y. Goyal, Z. Wu, J. Ernst, D. Batra, D. Parikh, and S. Lee, “Counterfactual visual explanations,” in International Conference on Machine Learning . PMLR, 2019, pp. 2376–2384
2019
Cited alongside, same era.
L. Chen, H. Zhang, J. Xiao, X. He, S. Pu, and S.-F. Chang, “Counterfactual critic multi-agent training for scene graph generation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 4613–4623
2019
Cited alongside, same era.
Z. Fang, S. Kong, C. Fowlkes, and Y. Yang, “Modularized textual grounding for counterfactual resilience,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 6378–6388
2019
Cited alongside, same era.
A. Kanehira, K. Takemoto, S. Inayoshi, and T. Harada, “Multimodal explanations by predicting counterfactuality in videos,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 8594–8602
2019
Cited alongside, same era.
K. Hara, H. Kataoka, and Y. Satoh, “Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2018, pp. 6546–6555
2019
Cited alongside, same era.
J. Lei, L. Li, L. Zhou, Z. Gan, T. L. Berg, M. Bansal, and J. Liu, “Less is more: Clipbert for video-and-language learning via sparse sampling,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 7331–7341
2021
Later among the works it cites.
F. Liu, J. Liu, W. Wang, and H. Lu, “Hair: Hierarchical visual-semantic relational reasoning for video question answering,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 1698–1707
2021
Later among the works it cites.
L. Xu, H. Huang, and J. Liu, “Sutd-trafficqa: A question answering benchmark and an efficient network for video reasoning over traffic events,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 9878–9888
2021
Later among the works it cites.
J. Pan, S. Chen, M. Z. Shou, Y. Liu, J. Shao, and H. Li, “Actor-context-actor relation network for spatio-temporal action localization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 464–474
2021
Later among the works it cites.
G. Nan, R. Qiao, Y. Xiao, J. Liu, S. Leng, H. Zhang, and W. Lu, “Interventional video grounding with dual contrastive learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 2765–2775
2021
Later among the works it cites.
X. Yang, H. Zhang, and J. Cai, “Deconfounded image captioning: A causal retrospect,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021
2021
Later among the works it cites.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 10 012–10 022
2021
Later among the works it cites.
N. Kim, S. J. Ha, and J.-W. Kang, “Video question answering using language-guided deep compressed-domain video feature,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 1708–1717
2021
Later among the works it cites.
A. Seo, G.-C. Kang, J. Park, and B.-T. Zhang, “Attend what you need: Motion-appearance synergistic networks for video question answering,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing , 2021, pp. 6167–6177
2021
Later among the works it cites.
J. Wang, B. Bao, and C. Xu, “Dualvgr: A dual-visual graph reasoning unit for video question answering,” IEEE Transactions on Multimedia , 2021
2021
Later among the works it cites.
M. Gu, Z. Zhao, W. Jin, R. Hong, and F. Wu, “Graph-based multi-interaction network for video question answering,” IEEE Transactions on Image Processing , vol. 30, pp. 2758–2770, 2021
2021
Later among the works it cites.
F. Liu, J. Liu, R. Hong, and H. Lu, “Question-guided erasing-based spatiotemporal attention learning for video question answering,” IEEE Transactions on Neural Networks and Learning Systems , 2021
2021
Later among the works it cites.
W. Jin, Z. Zhao, X. Cao, J. Zhu, X. He, and Y. Zhuang, “Adaptive spatio-temporal graph enhanced vision-language representation for video qa,” IEEE Transactions on Image Processing , vol. 30, pp. 5477–5489, 2021
2021
Later among the works it cites.
Y. Liu, K. Wang, L. Liu, H. Lan, and L. Lin, “Tcgl: Temporal contrastive graph for self-supervised video representation learning,” IEEE Transactions on Image Processing , vol. 31, pp. 1978–1993, 2022
2022
Closest in time.
S. Buch, C. Eyzaguirre, A. Gaidon, J. Wu, L. Fei-Fei, and J. C. Niebles, “Revisiting the” video” in video-language understanding,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 2917–2927
2022
Closest in time.
Y. Li, X. Wang, J. Xiao, W. Ji, and T.-S. Chua, “Invariant grounding for video question answering,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 2928–2937
2022
Closest in time.
Y. Zhu, Y. Zhang, L. Liu, Y. Liu, G. Li, M. Mao, and L. Lin, “Hybrid-order representation learning for electricity theft detection,” IEEE Transactions on Industrial Informatics , vol. 19, no. 2, pp. 1248–1259, 2022
2022
Closest in time.
W. Wang, J. Gao, and C. Xu, “Weakly-supervised video object grounding via causal intervention,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022
2022
Closest in time.
Y. Liu, Y.-S. Wei, H. Yan, G.-B. Li, and L. Lin, “Causal reasoning meets visual representation learning: A prospective study,” Machine Intelligence Research , pp. 1–27, 2022
2022
Closest in time.
Z. Liu, J. Ning, Y. Cao, Y. Wei, Z. Zhang, S. Lin, and H. Hu, “Video swin transformer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022, pp. 3202–3211
2022
Closest in time.
Z. Liu, H. Hu, Y. Lin, Z. Yao, Z. Xie, Y. Wei, J. Ning, Y. Cao, Z. Zhang, L. Dong et al. , “Swin transformer v2: Scaling up capacity and resolution,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 12 009–12 019
2022
Closest in time.
Y. Liu, X. Zhang, F. Huang, B. Zhang, and Z. Li, “Cross-attentional spatio-temporal semantic graph networks for video question answering,” IEEE Transactions on Image Processing , vol. 31, pp. 1684–1696, 2022
2022
Closest in time.
L. Gao, Y. Lei, P. Zeng, J. Song, M. Wang, and H. T. Shen, “Hierarchical representation network with auxiliary tasks for video captioning and video question answering,” IEEE Transactions on Image Processing , 2022
2022
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio, “Show, attend and tell: Neural image caption generation with visual attention,” in International conference on machine learning , 2015, pp. 2048–2057
2057
Closest in time.