[32] Xu Luo, Longhui Wei, Liangjian Wen, Jinrong Yang, Lingxi Xie, Zenglin Xu, and Qi Tian. Rectifying the shortcut learning of background for few-shot learning. Advances in Neural Information Processing Systems (NeurIPS), 34:13073-13085, 2021.
[33] Xu Luo, Jing Xu, and Zenglin Xu. Channel importance matters in few-shot image classification. In International conference on machine learning, pages 14542-14559. PMLR, 2022.
[34] Chong Ma, Lin Zhao, Yuzhong Chen, David Weizhong Liu, Xi Jiang, Tuo Zhang, Xintao Hu, Dinggang Shen, Dajiang Zhu, and Tianming Liu. Rectify vit shortcut learning by visual saliency. arXiv preprint arXiv:2206.08567, 2022.
[35] Oriol metas, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al. Matching networks for one shot learning. Advances in Neural Information Processing Systems (NeurIPS), 29:3630-3638, 2016.
[36] Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel. A simple neural attentive meta-learner. International Conference on Learning Representations (ICLR), 2018.
[37] Boris Oreshkin, Pau Rodríguez López, and Alexandre Lacoste. Tadam: Task dependent adaptive metric for improved few-shot learning. Advances in Neural Information Processing Systems (NeurIPS), 31, 2018.
[38] Patrick Ott and Mark Everingham. Shared parts for deformable part-based models. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1513-1520, 2011.
[39] Sachin Ravi and Hugo Larochelle. Optimization as a model for few-shot learning. International Conference on Learning Representations (ICLR), 2017.
[40] Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B Tenenbaum, Hugo Larochelle, and Richard S Zemel. Meta-learning for semi-supervised few-shot classification. International Conference on Learning Representations (ICLR), 2018.
[41] Mattia Rigotti, Christoph Miksovic, Ioana Giurgiu, Thomas Gschwind, and Paolo Scotton. Attention-based interpretability with concept transformers. In International Conference on Learning Representations (ICLR), 2022.
[42] Amir Rosenfeld, Richard Zemel, and John K Tsotsos. The elephant in the room. arXiv preprint arXiv:1808.03305, 2018.
[43] David A Ross and Richard S Zemel. Learning parts-based representations of data. Journal of Machine Learning Research, 7(11), 2006.
[44] Aniket Roy, Anshul Shah, Ketul Shah, Prithviraj Dhar, Anoop Cherian, and Rama Chellappa. Felmi: Few shot learning with hard mixup. In Advances in Neural Information Processing Systems (NeurIPS), 2022.
[45] Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang. Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. Unsupervised discovery of parts, structure, and dynamics, 2020.
[46] Rakshith Shetty, Bernt Schiele, and Mario Fritz. Not using the car to see the sidewalk-quantifying and controlling the effects of context in classification and segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8218–8226, 2019.
[47] Jake Snell, Kevin Swersky, and Richard S Zemel. Prototypical networks for few-shot learning. Advances in Neural Information Processing Systems (NeurIPS), 2017.
[48] Mingze Sun, Weizhi Ma, and Yang Liu. Global and local feature interaction with vision transformer for few-shot image classification. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pages 4530-4534, 2022.
[49] Yihong Sun, Adam Kortylewski, and Alan Yuille. Weakly-supervised amodal instance segmentation with compositional priors. arXiv preprint arXiv:2010.13175, 2020.