{"type":"ScholarlyArticle","title":"Shap-Mix: Shapley Value Guided Mixing for Long-Tailed Skeleton Based Action Recognition","authors":[{"name":"Jiahang Zhang","slug":"jiahang-zhang","given":"Jiahang","family":"Zhang","affiliation":"Wangxuan Institute of Computer Technology , Peking University"},{"name":"Lilang Lin","slug":"lilang-lin","given":"Lilang","family":"Lin","affiliation":"Wangxuan Institute of Computer Technology , Peking University"},{"name":"Jiaying Liu","slug":"jiaying-liu","given":"Jiaying","family":"Liu","affiliation":"Wangxuan Institute of Computer Technology , Peking University"}],"abstract":"In real-world scenarios, human actions often fall into a long-tailed distribution. It makes the existing skeleton-based action recognition works, which are mostly designed based on balanced datasets, suffer from a sharp performance degradation. Recently, many efforts have been made to image/video long-tailed learning. However, directly applying them to skeleton data can be sub-optimal due to the lack of consideration of the crucial spatial-temporal motion patterns, especially for some modality-specific methodologies such as data augmentation. To this end, considering the crucial role of the body parts in the spatially concentrated human actions, we attend to the mixing augmentations and propose a novel method, Shap-Mix, which improves long-tailed learning by mining representative motion patterns for tail categories. Specifically, we first develop an effective spatial-temporal mixing strategy for the skeleton to boost representation quality. Then, the employed saliency guidance method is presented, consisting of the saliency estimation based on Shapley value and a tail-aware mixing policy. It preserves the salient motion parts of minority classes in mixed data, explicitly establishing the relationships between crucial body structure cues and high-level semantics. Extensive experiments on three large-scale skeleton datasets show our remarkable performance improvement under both long-tailed and balanced settings. Our project is publicly available at: https://jhang2020.github.io/Projects/Shap-Mix/Shap-Mix.html.","doi":"10.24963/ijcai.2024/187","url":"https://ijcai.org/proceedings/2024/187","pdf":"https://ijcai.org/proceedings/2024/0187.pdf","markdown":"https://ijcai.org/proceedings/2024/187.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/187","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":187,"pages":{"first":1688,"last":1696},"keywords":["Computer Vision: CV: Action and behavior recognition"],"videos":[],"references":[{"position":1,"raw":"[Alshammari et al., 2022] Shaden Alshammari, Yuxiong Wang, Deva Ramanan, and Shu Kong. Long-tailed recognition via weight balancing. In IEEE CVPR, 2022.","title":"Long-tailed recognition via weight balancing","authors":"Shaden Alshammari, Yuxiong Wang, Deva Ramanan, Shu Kong","year":"2022","venue":"IEEE CVPR","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cao et al., 2019] Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma. Learning imbalanced datasets with label-distribution-aware margin loss. NeurIPS, 2019.","title":"Learning imbalanced datasets with label-distribution-aware margin loss","authors":"Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, Tengyu Ma","year":"2019","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2021a] Yuxin Chen, Ziqi Zhang, Chunfeng Yuan, Bing Li, Ying Deng, and Weiming Hu. Channel-wise topology refinement graph convolution for skeleton-based action recogni- tion. In ICCV, 2021.","title":"Channel-wise topology refinement graph convolution for skeleton-based action recognition","authors":"Chen","year":"2021","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2021b] Zhan Chen, Sicheng Li, Bing Yang, Qinghan Li, and Hong Liu. Multi-scale spatial temporal graph convolu- tional network for skeleton-based action recognition. In AAAI, 2021.","title":"Multi-scale spatial temporal graph convolutional network for skeleton-based action recognition","authors":"Chen","year":"2021","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen et al., 2022] Xiaohua Chen, Yucan Zhou, Dayan Wu, Wan- qian Zhang, Yu Zhou, Bo Li, and Weiping Wang. Imagine by reasoning: A reasoning-based implicit semantic data augmenta- tion for long-tailed classification. In AAAI, 2022.","title":"Imagine by reasoning: A reasoning-based implicit semantic data augmentation for long-tailed classification","authors":"Xiaohua Chen, Yucan Zhou, Dayan Wu, Wanqian Zhang, Yu Zhou, Bo Li, Weiping Wang","year":"2022","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cheng et al., 2020] Ke Cheng, Yifan Zhang, Xiangyu He, Weihan Chen, Jian Cheng, and Hanqing Lu. Skeleton-based action recog- nition with shift graph convolutional network. In IEEE CVPR, 2020.","title":"Skeleton-based action recognition with shift graph convolutional network","authors":"Ke Cheng, Yifan Zhang, Xiangyu He, Weihan Chen, Jian Cheng, Hanqing Lu","year":"2020","venue":"IEEE CVPR","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chi et al., 2022] Hyung-gun Chi, Myoung Hoon Ha, Seunggeun Chi, Sang Wan Lee, Qixing Huang, and Karthik Ramani. In- fogcn: Representation learning for human skeleton-based action recognition. In IEEE CVPR, 2022.","title":"Infogcn: Representation learning for human skeleton-based action recognition","authors":"Hyung-Gun Chi, Myoung Hoon Ha, Seunggeun Chi, Sang Wan Lee, Qixing Huang, Karthik Ramani","year":"2022","venue":"IEEE CVPR","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chou et al., 2020] Hsin-Ping Chou, Shih-Chieh Chang, Jia-Yu Pan, Wei Wei, and Da-Cheng Juan. Remix: rebalanced mixup. 2020.","title":"Remix: rebalanced mixup","authors":"Hsin-Ping Chou, Shih-Chieh Chang, Jia-Yu Pan, Wei Wei, Da-Cheng Juan","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Chu et al., 2020] Peng Chu, Xiao Bian, Shaopeng Liu, and Haibin Ling. Feature space augmentation for long-tailed data. In ECCV, 2020.","title":"Feature space augmentation for long-tailed data","authors":"Peng Chu, Xiao Bian, Shaopeng Liu, Haibin Ling","year":"2020","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Cui et al., 2019] Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie. Class-balanced loss based on effective num- ber of samples. In IEEE CVPR, 2019.","title":"Class-balanced loss based on effective number of samples","authors":"Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, Serge Belongie","year":"2019","venue":"IEEE CVPR","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Cui et al., 2021] Jiequan Cui, Zhisheng Zhong, Shu Liu, Bei Yu, and Jiaya Jia. Parametric contrastive learning. In ICCV, 2021.","title":"Parametric contrastive learning","authors":"Jiequan Cui, Zhisheng Zhong, Shu Liu, Bei Yu, Jiaya Jia","year":"2021","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Damen et al., 2022] Dima Damen, Hazel Doughty, Gio- vanni Maria Farinella, Antonino Furnari, Jian Ma, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. Rescaling egocentric vision: Collection, pipeline and challenges for epic-kitchens-100. IJCV, 130:33-55, 2022.","title":"Rescaling egocentric vision: Collection, pipeline and challenges for epic-kitchens-100","authors":"Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Antonino Furnari, Jian Ma, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, Michael Wray","year":"2022","venue":"IJCV","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Deng and Papadimitriou, 1994] Xiaotie Deng and Christos H Pa- padimitriou. On the complexity of cooperative solution concepts. Mathematics of operations research, 19(2):257-266, 1994.","title":"Xiaotie Deng and Christos H Papadimitriou. On the complexity of cooperative solution concepts","authors":"Papadimitriou Deng","year":"1994","venue":"Mathematics of operations research","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Du et al., 2023] Fei Du, Peng Yang, Qi Jia, Fengtao Nan, Xiaot- ing Chen, and Yun Yang. Global and local mixture consistency cumulative learning for long-tailed visual recognitions. In IEEE CVPR, 2023.","title":"Global and local mixture consistency cumulative learning for long-tailed visual recognitions","authors":"Fei Du, Peng Yang, Qi Jia, Fengtao Nan, Xiaoting Chen, Yun Yang","year":"2023","venue":"IEEE CVPR","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Duan et al., 2022] Haodong Duan, Yue Zhao, Kai Chen, Dahua Lin, and Bo Dai. Revisiting skeleton-based action recognition. In IEEE CVPR, 2022.","title":"Revisiting skeleton-based action recognition","authors":"Duan","year":"2022","venue":"IEEE CVPR","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Han et al., 2005] Hui Han, Wen-Yuan Wang, and Bing-Huan Mao. Borderline-smote: a new over-sampling method in imbalanced data sets learning. In International Conference on Intelligent Computing, 2005.","title":"Borderline-smote: a new over-sampling method in imbalanced data sets learning","authors":"Hui Han, Wen-Yuan Wang, Bing-Huan Mao","year":"2005","venue":"International Conference on Intelligent Computing","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Hart, 2016] S Hart. A bibliography of cooperative games: Value theory,[e-source], 2016.","title":"A bibliography of cooperative games: Value theory","authors":"Hart","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Hsieh et al., 2021] Ting-I Hsieh, Esther Robb, Hwann-Tzong Chen, and Jia-Bin Huang. Droploss for long-tail instance seg- mentation. In AAAI, 2021.","title":"Droploss for long-tail instance segmentation","authors":"Ting-I Hsieh, Esther Robb, Hwann-Tzong Chen, Jia-Bin Huang","year":"2021","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Hua et al., 2023] Yilei Hua, Wenhan Wu, Ce Zheng, Aidong Lu, Mengyuan Liu, Chen Chen, and Shiqian Wu. Part aware con- trastive learning for self-supervised action recognition. arXiv preprint arXiv:2305.00666, 2023.","title":"Part aware contrastive learning for self-supervised action recognition","authors":"Hua","year":"2023","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2023/95"},{"position":20,"raw":"[Kang et al., 2019] Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis. Decoupling representation and classifier for long-tailed recogni- tion. arXiv preprint arXiv:1910.09217, 2019.","title":"Decoupling representation and classifier for long-tailed recognition","authors":"Kang","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Kay et al., 2017] Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Vi- ola, Tim Green, Trevor Back, Paul Natsev, et al. The kinetics human action video dataset. arXiv preprint arXiv:1705.06950, 2017.","title":"The kinetics human action video dataset","authors":"Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Ke et al., 2017] Qiuhong Ke, Mohammed Bennamoun, Senjian An, Ferdous Sohel, and Farid Boussaid. A new representation of skeleton sequences for 3d action recognition. In IEEE CVPR, 2017.","title":"A new representation of skeleton sequences for 3d action recognition","authors":"Ke","year":"2017","venue":"IEEE CVPR","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Lee et al., 2023a] Jungho Lee, Minhyeok Lee, Suhwan Cho, Sungmin Woo, Sungjun Jang, and Sangyoun Lee. Leveraging spatio-temporal dependency for skeleton-based action recogni- tion. In ICCV, 2023.","title":"Leveraging spatio-temporal dependency for skeleton-based action recognition","authors":"Lee","year":"2023","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Lee et al., 2023b] Jungho Lee, Minhyeok Lee, Dogyoon Lee, and Sangyoun Lee. Hierarchically decomposed graph convolutional networks for skeleton-based action recognition. In ICCV, 2023.","title":"Hierarchically decomposed graph convolutional networks for skeleton-based action recognition","authors":"Lee","year":"2023","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Lin et al., 2017] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaim- ing He, and Piotr Dollár. Focal loss for dense object detection. In ICCV, 2017.","title":"Focal loss for dense object detection","authors":"Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollár","year":"2017","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Liu et al., 2017] Mengyuan Liu, Hong Liu, and Chen Chen. En- hanced skeleton visualization for view invariant human action recognition. Pattern Recognition, 68:346-362, 2017.","title":"Enhanced skeleton visualization for view invariant human action recognition","authors":"Liu","year":"2017","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Liu et al., 2019] Jun Liu, Amir Shahroudy, Mauricio Perez, Gang Wang, Ling-Yu Duan, and Alex C Kot. Ntu rgb+ d 120: A large- scale benchmark for 3d human activity understanding. IEEE TPAMI, 42(10):2684-2701, 2019.","title":"Ntu rgb+ d 120: A largescale benchmark for 3d human activity understanding","authors":"Liu","year":"2019","venue":"IEEE TPAMI","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Liu et al., 2020a] Meng Liu, Hongyang Gao, and Shuiwang Ji. To- wards deeper graph neural networks. In ACM SIGKDD, 2020.","title":"Towards deeper graph neural networks","authors":"Liu","year":"2020","venue":"ACM SIGKDD","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Liu et al., 2020b] Ziyu Liu, Hongwen Zhang, Zhenghao Chen, Zhiyong Wang, and Wanli Ouyang. Disentangling and unify- ing graph convolutions for skeleton-based action recognition. In IEEE CVPR, 2020.","title":"Disentangling and unifying graph convolutions for skeleton-based action recognition","authors":"Liu","year":"2020","venue":"IEEE CVPR","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Liu et al., 2022] Zicheng Liu, Siyuan Li, Di Wu, Zihan Liu, Zhiyuan Chen, Lirong Wu, and Stan Z Li. Automix: Unveiling the power of mixup for stronger classifiers. In ECCV, 2022.","title":"Automix: Unveiling the power of mixup for stronger classifiers","authors":"Zicheng Liu, Siyuan Li, Di Wu, Zihan Liu, Zhiyuan Chen, Lirong Wu, Stan Z Li","year":"2022","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Liu et al., 2023] Hongda Liu, Yunlong Wang, Min Ren, Junxing Hu, Zhengquan Luo, Guangqi Hou, and Zhenan Sun. Bal- anced representation learning for long-tailed skeleton-based ac- tion recognition. arXiv preprint arXiv:2308.14024, 2023.","title":"Balanced representation learning for long-tailed skeleton-based action recognition","authors":"Hongda Liu, Yunlong Wang, Min Ren, Junxing Hu, Zhengquan Luo, Guangqi Hou, Zhenan Sun","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Park et al., 2021] Seulki Park, Jongin Lim, Younghan Jeon, and Jin Young Choi. Influence-balanced loss for imbalanced visual classification. In ICCV, 2021.","title":"Influence-balanced loss for imbalanced visual classification","authors":"Park","year":"2021","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Plizzari et al., 2021] Chiara Plizzari, Marco Cannici, and Matteo Matteucci. Skeleton-based action recognition via spatial and tem- poral transformer networks. CVIU, 208:103219, 2021.","title":"Skeleton-based action recognition via spatial and temporal transformer networks","authors":"Chiara Plizzari, Marco Cannici, Matteo Matteucci","year":"2021","venue":"CVIU","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Qin et al., 2020] Jie Qin, Jiemin Fang, Qian Zhang, Wenyu Liu, Xingang Wang, and Xinggang Wang. Resizemix: Mixing data with preserved object information and true labels. arXiv preprint arXiv:2012.11101, 2020.","title":"Resizemix: Mixing data with preserved object information and true labels","authors":"Jie Qin, Jiemin Fang, Qian Zhang, Wenyu Liu, Xingang Wang, Xinggang Wang","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Ren et al., 2020] Jiawei Ren, Cunjun Yu, Xiao Ma, Haiyu Zhao, Shuai Yi, et al. Balanced meta-softmax for long-tailed visual recognition. NeurIPS, 2020.","title":"Balanced meta-softmax for long-tailed visual recognition","authors":"Cunjun Jiawei Ren, Xiao Yu, Haiyu Ma, Shuai Zhao, Yi","year":"2020","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Selvaraju et al., 2017] Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In ICCV, 2017.","title":"Grad-cam: Visual explanations from deep networks via gradient-based localization","authors":"Michael Ramprasaath R Selvaraju, Abhishek Cogswell, Ramakrishna Das, Devi Vedantam, Dhruv Parikh, Batra","year":"2017","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Shahroudy et al., 2016] Amir Shahroudy, Jun Liu, Tian-Tsong Ng, and Gang Wang. Ntu rgb+ d: A large scale dataset for 3d human activity analysis. In IEEE CVPR, 2016.","title":"Ntu rgb+ d: A large scale dataset for 3d human activity analysis","authors":"Amir Shahroudy, Jun Liu, Tian-Tsong Ng, Gang Wang","year":"2016","venue":"IEEE CVPR","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Shapley and others, 1953] Lloyd S Shapley et al. A value for n- person games. 1953.","title":"A value for nperson games","authors":"S Lloyd, Shapley","year":"1953","venue":null,"doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Shen et al., 2016] Li Shen, Zhouchen Lin, and Qingming Huang. Relay backpropagation for effective learning of deep convolu- tional neural networks. In ECCV, 2016.","title":"Relay backpropagation for effective learning of deep convolutional neural networks","authors":"Li Shen, Zhouchen Lin, Qingming Huang","year":"2016","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Shi et al., 2019] Lei Shi, Yifan Zhang, Jian Cheng, and Hanqing Lu. Two-stream adaptive graph convolutional networks for skeleton-based action recognition. In IEEE CVPR, 2019.","title":"Two-stream adaptive graph convolutional networks for skeleton-based action recognition","authors":"Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu","year":"2019","venue":"IEEE CVPR","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Shi et al., 2020] Lei Shi, Yifan Zhang, Jian Cheng, and Hanqing Lu. Decoupled spatial-temporal attention network for skeleton- based action recognition, 2020.","title":"Decoupled spatial-temporal attention network for skeletonbased action recognition","authors":"Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Song et al., 2017] Sijie Song, Cuiling Lan, Junliang Xing, Wenjun Zeng, and Jiaying Liu. An end-to-end spatio-temporal attention model for human action recognition from skeleton data. In AAAI, 2017.","title":"An end-to-end spatio-temporal attention model for human action recognition from skeleton data","authors":"Sijie Song, Cuiling Lan, Junliang Xing, Wenjun Zeng, Jiaying Liu","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Song et al., 2018a] Sijie Song, Cuiling Lan, Junliang Xing, Wen- jun Zeng, and Jiaying Liu. Skeleton-indexed deep multi-modal feature learning for high performance human action recognition. In IEEE ICME, 2018.","title":"Skeleton-indexed deep multi-modal feature learning for high performance human action recognition","authors":"Song","year":"2018","venue":"IEEE ICME","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Song et al., 2018b] Sijie Song, Cuiling Lan, Junliang Xing, Wen- jun Zeng, and Jiaying Liu. Spatio-temporal attention-based lstm networks for 3d action recognition and detection. IEEE TIP, pages 3459-3471, 2018.","title":"Spatio-temporal attention-based lstm networks for 3d action recognition and detection","authors":"Song","year":"2018","venue":"IEEE TIP","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Song et al., 2022] Yi-Fan Song, Zhang Zhang, Caifeng Shan, and Liang Wang. Constructing stronger and faster baselines for skeleton-based action recognition. IEEE TPAMI, 45(2):1474- 1488, 2022.","title":"Constructing stronger and faster baselines for skeleton-based action recognition","authors":"Yi-Fan Song, Zhang Zhang, Caifeng Shan, Liang Wang","year":"2022","venue":"IEEE TPAMI","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Tan et al., 2020] Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, and Junjie Yan. Equalization loss for long-tailed object recognition. In IEEE CVPR, 2020.","title":"Equalization loss for long-tailed object recognition","authors":"Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, Junjie Yan","year":"2020","venue":"IEEE CVPR","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Van Hulse et al., 2007] Jason Van Hulse, Taghi M Khoshgoftaar, and Amri Napolitano. Experimental perspectives on learning from imbalanced data. In ICML, 2007.","title":"Experimental perspectives on learning from imbalanced data","authors":"Van Hulse","year":"2007","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Walawalkar et al., 2020] Devesh Walawalkar, Zhiqiang Shen, Zechun Liu, and Marios Savvides. Attentive cutmix: An en- hanced data augmentation approach for deep learning based im- age classification. arXiv preprint arXiv:2003.13048, 2020.","title":"Attentive cutmix: An enhanced data augmentation approach for deep learning based image classification","authors":"Devesh Walawalkar, Zhiqiang Shen, Zechun Liu, Marios Savvides","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Wang et al., 2021] Xudong Wang, Long Lian, Zhongqi Miao, Zi- wei Liu, and Stella Yu. Long-tailed recognition by routing di- verse distribution-aware experts. In ICLR, 2021.","title":"Long-tailed recognition by routing diverse distribution-aware experts","authors":"Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, Stella Yu","year":"2021","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Xu et al., 2022] Kailin Xu, Fanfan Ye, Qiaoyong Zhong, and Di Xie. Topology-aware convolutional neural network for effi- cient skeleton-based action recognition. In AAAI, 2022.","title":"Topology-aware convolutional neural network for efficient skeleton-based action recognition","authors":"Kailin Xu, Fanfan Ye, Qiaoyong Zhong, Di Xie","year":"2022","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Yan et al., 2018] Sijie Yan, Yuanjun Xiong, and Dahua Lin. Spa- tial temporal graph convolutional networks for skeleton-based ac- tion recognition. In AAAI, pages 7444-7452, 2018.","title":"Spatial temporal graph convolutional networks for skeleton-based action recognition","authors":"Sijie Yan, Yuanjun Xiong, Dahua Lin","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Yang et al., 2023] Yuheng Yang, Haipeng Chen, Zhenguang Liu, Yingda Lyu, Beibei Zhang, Shuang Wu, Zhibo Wang, and Kui Ren. Action recognition with multi-stream motion mod- eling and mutual information maximization. arXiv preprint arXiv:2306.07576, 2023.","title":"Action recognition with multi-stream motion modeling and mutual information maximization","authors":"Yuheng Yang, Haipeng Chen, Zhenguang Liu, Yingda Lyu, Beibei Zhang, Shuang Wu, Zhibo Wang, Kui Ren","year":"2023","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2023/184"},{"position":53,"raw":"[Yun et al., 2019] Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo. Cutmix: Regularization strategy to train strong classifiers with localizable features. In ICCV, 2019.","title":"Cutmix: Regularization strategy to train strong classifiers with localizable features","authors":"Yun","year":"2019","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Zhan et al., 2022] Chen Zhan, Liu Hong, Guo Tianyu, Chen Zhengyan, Song Pinhao, and Tang Hao. Contrastive learn- ing from spatio-temporal mixed skeleton sequences for self- supervised skeleton-based action recognition. In arXiv: 2207.03065, 2022.","title":"Contrastive learning from spatio-temporal mixed skeleton sequences for selfsupervised skeleton-based action recognition","authors":"Chen Zhan, Liu Hong, Guo Tianyu, Chen Zhengyan, Song Pinhao, Tang Hao","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":55,"raw":"[Zhang et al., 2017] Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization, 2017.","title":"mixup: Beyond empirical risk minimization","authors":"Hongyi Zhang, Moustapha Cisse, David Yann N Dauphin, Lopez-Paz","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":56,"raw":"[Zhang et al., 2020] Pengfei Zhang, Cuiling Lan, Wenjun Zeng, Junliang Xing, Jianru Xue, and Nanning Zheng. Semantics- guided neural networks for efficient skeleton-based human action recognition. In IEEE CVPR, 2020.","title":"Semanticsguided neural networks for efficient skeleton-based human action recognition","authors":"Pengfei Zhang, Cuiling Lan, Wenjun Zeng, Junliang Xing, Jianru Xue, Nanning Zheng","year":"2020","venue":"IEEE CVPR","doi":null,"url":null,"internal_url":null},{"position":57,"raw":"[Zhang et al., 2021] Xing Zhang, Zuxuan Wu, Zejia Weng, Huazhu Fu, Jingjing Chen, Yu-Gang Jiang, and Larry S Davis. Videolt: Large-scale long-tailed video recognition. In ICCV, 2021.","title":"Videolt: Large-scale long-tailed video recognition","authors":"Xing Zhang, Zuxuan Wu, Zejia Weng, Huazhu Fu, Jingjing Chen, Yu-Gang Jiang, Larry S Davis","year":"2021","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":58,"raw":"[Zhang et al., 2023] Jiahang Zhang, Lilang Lin, and Jiaying Liu. Hierarchical consistent contrastive learning for skeleton-based action recognition with growing augmentations. In AAAI, 2023.","title":"Hierarchical consistent contrastive learning for skeleton-based action recognition with growing augmentations","authors":"Zhang","year":"2023","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":59,"raw":"[Zhou et al., 2023] Huanyu Zhou, Qingjie Liu, and Yunhong Wang. Learning discriminative representations for skeleton based action recognition. In IEEE CVPR, 2023.","title":"Learning discriminative representations for skeleton based action recognition","authors":"Huanyu Zhou, Qingjie Liu, Yunhong Wang","year":"2023","venue":"IEEE CVPR","doi":null,"url":null,"internal_url":null},{"position":60,"raw":"[Zhu et al., 2022] Jianggang Zhu, Zheng Wang, Jingjing Chen, Yi- Ping Phoebe Chen, and Yu-Gang Jiang. Balanced contrastive learning for long-tailed visual recognition. In IEEE CVPR, 2022.","title":"Balanced contrastive learning for long-tailed visual recognition","authors":"Jianggang Zhu, Zheng Wang, Jingjing Chen, Yi-Ping Phoebe Chen, Yu-Gang Jiang","year":"2022","venue":"IEEE CVPR","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}