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This paper proposes a Video Graph Transformer (VGT) model for Video Quetion Answering (VideoQA).
2015
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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 770–778 (2016)
2016
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Jang, Y., Song, Y., Yu, Y., Kim, Y., Kim, G.: Tgif-qa: Toward spatio-temporal reasoning in visual question answering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2758–2766 (2017)
2017
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Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Representation Learning (ICLR) (2017)
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Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.J., Shamma, D.A., et al.: Visual genome: Connecting language and vision using crowdsourced dense image annotations. IJCV
2017
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems (NeurIPS). vol. 30 (2017)
2017
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Xu, D., Zhao, Z., Xiao, J., Wu, F., Zhang, H., He, X., Zhuang, Y.: Video question answering via gradually refined attention over appearance and motion. In: ACM MM. pp. 1645–1653 (2017)
2017
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Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., Zhang, L.: Bottom-up and top-down attention for image captioning and visual question answering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6077–6086 (2018)
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Gao, J., Ge, R., Chen, K., Nevatia, R.: Motion-appearance co-memory networks for video question answering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6576–6585 (2018)
2018
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Krishna, R., Chami, I., Bernstein, M., Fei-Fei, L.: Referring relationships. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6867–6876 (2018)
2018
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Lei, J., Yu, L., Bansal, M., Berg, T.L.: Tvqa: Localized, compositional video question answering. In: Empirical Methods in Natural Language Processing (EMNLP) (2018)
2018
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Sharma, P., Ding, N., Goodman, S., Soricut, R.: Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In: ACL. pp. 2556–2565 (2018)
2018
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Wang, X., Gupta, A.: Videos as space-time region graphs. In: European conference on computer vision (ECCV). pp. 399–417 (2018)
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Xie, S., Sun, C., Huang, J., Tu, Z., Murphy, K.: Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification. In: European Conference on Computer Vision (ECCV). pp. 305–321 (2018)
2018
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Yu, Y., Kim, J., Kim, G.: A joint sequence fusion model for video question answering and retrieval. In: European Conference on Computer Vision (ECCV). pp. 471–487 (2018)
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Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: NAACL (2019)
2019
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Fan, C., Zhang, X., Zhang, S., Wang, W., Zhang, C., Huang, H.: Heterogeneous memory enhanced multimodal attention model for video question answering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1999–2007 (2019)
2019
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Li, X., Song, J., Gao, L., Liu, X., Huang, W., He, X., Gan, C.: Beyond rnns: Positional self-attention with co-attention for video question answering. In: AAAI Conference on Artificial Intelligence (AAAI). pp. 8658–8665 (2019)
2019
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Lu, J., Batra, D., Parikh, D., Lee, S.: Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In: Advances in neural information processing systems (NeurIPS). pp. 13–23 (2019)
2019
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Miech, A., Zhukov, D., Alayrac, J.B., Tapaswi, M., Laptev, I., Sivic, J.: Howto100m: Learning a text-video embedding by watching hundred million narrated video clips. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 2630–2640 (2019)
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Sanh, V., Debut, L., Chaumond, J., Wolf, T.: Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. Advances in neural information processing systems (NeurIPS) (2019)
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Shang, X., Di, D., Xiao, J., Cao, Y., Yang, X., Chua, T.S.: Annotating objects and relations in user-generated videos. In: Proceedings of the 2019 on International Conference on Multimedia Retrieval (ICMR). pp. 279–287 (2019)
2019
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Shang, X., Xiao, J., Di, D., Chua, T.S.: Relation understanding in videos: A grand challenge overview. In: Proceedings of the 27th ACM International Conference on Multimedia (MM). pp. 2652–2656 (2019)
2019
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Sun, C., Myers, A., Vondrick, C., Murphy, K., Schmid, C.: Videobert: A joint model for video and language representation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 7464–7473 (2019)
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Tan, H., Bansal, M.: Lxmert: Learning cross-modality encoder representations from transformers. In: EMNLP. pp. 5100–5111 (2019)
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Yi, K., Gan, C., Li, Y., Kohli, P., Wu, J., Torralba, A., Tenenbaum, J.B.: Clevrer: Collision events for video representation and reasoning. In: International Conference on Learning Representations (ICLR) (2019)
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Yun, S., Jeong, M., Kim, R., Kang, J., Kim, H.J.: Graph transformer networks. Advances in neural information processing systems (NeurIPS)
2019
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Jia, C., Yang, Y., Xia, Y., Chen, Y.T., Parekh, Z., Pham, H., Le, Q., Sung, Y.H., Li, Z., Duerig, T.: Scaling up visual and vision-language representation learning with noisy text supervision. In: ICML. pp. 4904–4916. PMLR (2021)
2021
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Kamath, A., Singh, M., LeCun, Y., Synnaeve, G., Misra, I., Carion, N.: Mdetr-modulated detection for end-to-end multi-modal understanding. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 1780–1790 (2021)
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Lei, J., Li, L., Zhou, L., Gan, Z., Berg, T.L., Bansal, M., Liu, J.: 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 (CVPR). pp. 7331–7341 (2021)
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Li, J., Selvaraju, R., Gotmare, A., Joty, S., Xiong, C., Hoi, S.C.H.: Align before fuse: Vision and language representation learning with momentum distillation. In: Advances in neural information processing systems (NeurIPS). vol. 34 (2021)
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Chen, Y.C., Li, L., Yu, L., El Kholy, A., Ahmed, F., Gan, Z., Cheng, Y., Liu, J.: Uniter: Universal image-text representation learning. In: European Conference on Computer Vision (ECCV). pp. 104–120. Springer (2020)
2020
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Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Representation Learning (ICLR) (2020)
2020
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Huang, D., Chen, P., Zeng, R., Du, Q., Tan, M., Gan, C.: Location-aware graph convolutional networks for video question answering. In: AAAI Conference on Artificial Intelligence (AAAI). vol. 34, pp. 11021–11028 (2020)
2020
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Jiang, J., Chen, Z., Lin, H., Zhao, X., Gao, Y.: Divide and conquer: Question-guided spatio-temporal contextual attention for video question answering. In: AAAI Conference on Artificial Intelligence (AAAI). vol. 34, pp. 11101–11108 (2020)
2020
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Jiang, P., Han, Y.: Reasoning with heterogeneous graph alignment for video question answering. In: AAAI Conference on Artificial Intelligence (AAAI) (2020)
2020
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Le, T.M., Le, V., Venkatesh, S., Tran, T.: Hierarchical conditional relation networks for video question answering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9972–9981 (2020)
2020
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Li, L., Chen, Y.C., Cheng, Y., Gan, Z., Yu, L., Liu, J.: Hero: Hierarchical encoder for video+ language omni-representation pre-training. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). pp. 2046–2065 (2020)
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Li, X., Yin, X., Li, C., Zhang, P., Hu, X., Zhang, L., Wang, L., Hu, H., Dong, L., Wei, F., et al.: Oscar: Object-semantics aligned pre-training for vision-language tasks. In: European Conference on Computer Vision (ECCV). pp. 121–137. Springer (2020)
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2021
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Liu, F., Liu, J., Wang, W., Lu, H.: Hair: Hierarchical visual-semantic relational reasoning for video question answering. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 1698–1707 (October 2021)
2021
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Park, J., Lee, J., Sohn, K.: 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 (CVPR). pp. 15526–15535 (2021)
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Seo, A., Kang, G.C., Park, J., Zhang, B.T.: Attend what you need: Motion-appearance synergistic networks for video question answering. In: ACL. pp. 6167–6177 (2021)
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Seo, P.H., Nagrani, A., Schmid, C.: Look before you speak: Visually contextualized utterances. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 16877–16887 (2021)
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Xiao, J., Shang, X., Yao, A., Chua, T.S.: Next-qa: Next phase of question-answering to explaining temporal actions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9777–9786 (2021)
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Yu, W., Zheng, H., Li, M., Ji, L., Wu, L., Xiao, N., Duan, N.: Learning from inside: Self-driven siamese sampling and reasoning for video question answering. Advances in neural information processing systems (NeurIPS)
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Zellers, R., Lu, X., Hessel, J., Yu, Y., Park, J.S., Cao, J., Farhadi, A., Choi, Y.: Merlot: Multimodal neural script knowledge models. In: Advances in neural information processing systems (NeurIPS). vol. 34 (2021)
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Buch, S., Eyzaguirre, C., Gaidon, A., Wu, J., Fei-Fei, L., Niebles, J.C.: Revisiting the” video” in video-language understanding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2917–2927 (2022)
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