[88] Zhilin Zhao and Longbing Cao. Dual representation learning for out-of-distribution detection. Transactions on Machine Learning Research, 2023.
[89] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In IEEE Conference on Computer Vision and Pattern Recognition, 2016.
[90] Xiaolong Wang and Abhinav Gupta. Videos as space-time region graphs. In European Conference on Computer Vision, 2018.
[91] Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation networks. In IEEE Conference on Computer Vision and Pattern Recognition, 2018.
[92] Fengchun Qiao, Long Zhao, and Xi Peng. Learning to learn single domain generalization. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.
[93] Fabio Maria Carlucci, Antonio D'Innocente, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi. Domain generalization by solving jigsaw puzzles. In IEEE Conference on Computer Vision and Pattern Recognition, 2019.
[94] Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. UCF101: A dataset of 101 human actions classes from videos in the wild. CoRR, abs/1212.0402, 2012.
[95] Hildegard Kuehne, Hueihan Jhuang, Estíbaliz Garrote, Tomaso A. Poggio, and Thomas Serre. HMDB: A large video database for human motion recognition. In IEEE International Conference on Computer Vision, 2011.
[96] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. Scaling egocentric vision: The EPIC-KITCHENS dataset. In European Conference on Computer Vision, 2018.
[97] Joanna Materzynska, Guillaume Berger, Ingo Bax, and Roland Memisevic. The jester dataset: A large-scale video dataset of human gestures. In IEEE/CVF International Conference on Computer Vision Workshops, 2019.
[98] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2009.
[99] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems, 2019.
[100] Dejing Xu, Jun Xiao, Zhou Zhao, Jian Shao, Di Xie, and Yueting Zhuang. Self-supervised spatiotemporal learning via video clip order prediction. In IEEE Conference on Computer Vision and Pattern Recognition, 2019.
[101] Jianfei Yang, Xiangyu Peng, Kai Wang, Zheng Zhu, Jiashi Feng, Lihua Xie, and Yang You. Divide to Adapt: Mitigating confirmation bias for domain adaptation of black-box predictors. In International Conference on Learning Representations, 2023.
[102] Ziyi Zhang, Weikai Chen, Hui Cheng, Zhen Li, Siyuan Li, Liang Lin, and Guanbin Li. Divide and Contrast: Source-free domain adaptation via adaptive contrastive learning. In Advances in Neural Information Processing Systems, 2022.
[103] Yipeng Gao, Kun-Yu Lin, Junkai Yan, Yaowei Wang, and Wei-Shi Zheng. AsyFOD: An asymmetric adaptation paradigm for few-shot domain adaptive object detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2023.
[104] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-SNE. Journal of Machine Learning Research, 9(11), 2008.
[105] David L. Davies and Donald W. Bouldin. A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2):224-227, 1979.
[106] 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. International Journal of Computer Vision, 128(2):336-359, 2020.