Redundancy-aware Transformer for Video Question Answering
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Xiang Wang | An Zhang | Xun Yang | Yicong Li | Chun Feng | Tat-seng Chua
[1] Fuli Feng,et al. Partial Annotation-based Video Moment Retrieval via Iterative Learning , 2023, ACM Multimedia.
[2] Shengyu Zhang,et al. Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-based Active Learning , 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Meng Wang,et al. Transformer-Based Visual Grounding with Cross-Modality Interaction , 2023, ACM Transactions on Multimedia Computing, Communications, and Applications.
[4] Xirong Li,et al. Partially Relevant Video Retrieval , 2022, ACM Multimedia.
[5] Xiang Wang,et al. Equivariant and Invariant Grounding for Video Question Answering , 2022, ACM Multimedia.
[6] H. Zhang,et al. Long-term Leap Attention, Short-term Periodic Shift for Video Classification , 2022, ACM Multimedia.
[7] Shuicheng Yan,et al. Video Graph Transformer for Video Question Answering , 2022, ECCV.
[8] Xiang Wang,et al. Invariant Grounding for Video Question Answering , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Juan Carlos Niebles,et al. Revisiting the “Video” in Video-Language Understanding , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Liqing Zhang,et al. From Representation to Reasoning: Towards both Evidence and Commonsense Reasoning for Video Question-Answering , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Min Peng,et al. Multilevel Hierarchical Network with Multiscale Sampling for Video Question Answering , 2022, IJCAI.
[12] Tat-Seng Chua,et al. Video Question Answering: Datasets, Algorithms and Challenges , 2022, EMNLP.
[13] Xiangnan He,et al. Discovering Invariant Rationales for Graph Neural Networks , 2022, ICLR.
[14] Tat-Seng Chua,et al. Video Moment Retrieval With Cross-Modal Neural Architecture Search , 2022, IEEE Transactions on Image Processing.
[15] Angela Yao,et al. Video as Conditional Graph Hierarchy for Multi-Granular Question Answering , 2021, AAAI.
[16] Yu-Gang Jiang,et al. Efficient Video Transformers with Spatial-Temporal Token Selection , 2021, ECCV.
[17] Xiangnan He,et al. Selective Dependency Aggregation for Action Classification , 2021, ACM Multimedia.
[18] Tat-Seng Chua,et al. VidVRD 2021: The Third Grand Challenge on Video Relation Detection , 2021, ACM Multimedia.
[19] Xindi Shang,et al. Video Visual Relation Detection via Iterative Inference , 2021, ACM Multimedia.
[20] Tat-Seng Chua,et al. Interventional Video Relation Detection , 2021, ACM Multimedia.
[21] Guoqing Wang,et al. Progressive Graph Attention Network for Video Question Answering , 2021, ACM Multimedia.
[22] Truyen Tran,et al. Hierarchical Object-oriented Spatio-Temporal Reasoning for Video Question Answering , 2021, IJCAI.
[23] Jiwen Lu,et al. DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification , 2021, NeurIPS.
[24] Meng Wang,et al. Deconfounded Video Moment Retrieval with Causal Intervention , 2021, SIGIR.
[25] Angela Yao,et al. NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Kwanghoon Sohn,et al. Bridge to Answer: Structure-aware Graph Interaction Network for Video Question Answering , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Xirong Li,et al. Dual Encoding for Video Retrieval by Text , 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[28] Kate Saenko,et al. VA-RED2: Video Adaptive Redundancy Reduction , 2021, ICLR.
[29] Sai Srinivas Kancheti,et al. Beyond VQA: Generating Multi-word Answers and Rationales to Visual Questions , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[30] Meng Jian,et al. Weakly-Supervised Video Object Grounding by Exploring Spatio-Temporal Contexts , 2020, ACM Multimedia.
[31] Sheng Tang,et al. Visual Relation Grounding in Videos , 2020, ECCV.
[32] Jianfeng Gao,et al. DeBERTa: Decoding-enhanced BERT with Disentangled Attention , 2020, ICLR.
[33] Yahong Han,et al. Reasoning with Heterogeneous Graph Alignment for Video Question Answering , 2020, AAAI.
[34] Shiliang Pu,et al. Counterfactual Samples Synthesizing for Robust Visual Question Answering , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Truyen Tran,et al. Hierarchical Conditional Relation Networks for Video Question Answering , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Yu Cao,et al. Annotating Objects and Relations in User-Generated Videos , 2019, ICMR.
[37] Matthieu Cord,et al. RUBi: Reducing Unimodal Biases in Visual Question Answering , 2019, NeurIPS.
[38] Trevor Darrell,et al. Multimodal Explanations: Justifying Decisions and Pointing to the Evidence , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[39] Quanshi Zhang,et al. Interpreting CNNs via Decision Trees , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[40] Yueting Zhuang,et al. Video Question Answering via Gradually Refined Attention over Appearance and Motion , 2017, ACM Multimedia.
[41] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[42] Yale Song,et al. TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[43] Andrew Slavin Ross,et al. Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations , 2017, IJCAI.
[44] Li Zhang,et al. Spatially Adaptive Computation Time for Residual Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Marco Tulio Ribeiro,et al. “Why Should I Trust You?”: Explaining the Predictions of Any Classifier , 2016, NAACL.
[46] 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.
[47] Naftali Tishby,et al. Deep learning and the information bottleneck principle , 2015, 2015 IEEE Information Theory Workshop (ITW).