[46] Peter Shaw, Ming-Wei Chang, Panupong Pasupat, and Kristina Toutanova. Compositional generalization and natural language variation: Can a semantic parsing approach handle both? arXiv preprint arXiv:2010.12725, 2020.
[47] James Seale Smith, Paola Cascante-Bonilla, Assaf Arbelle, Donghyun Kim, Rameswar Panda, David Cox, Diyi Yang, Zsolt Kira, Rogerio Feris, and Leonid Karlinsky. Construct-vl: Data-free continual structured vl concepts learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14994-15004, 2023.
[48] Morgan B Talbot, Rushikesh Zawar, Rohil Badkundri, Mengmi Zhang, and Gabriel Kreiman. Lifelong compositional feature replays beat image replays in stream learning. arXiv preprint arXiv:2104.02206, 2021.
[49] Dhruva Tirumala, Hyeonwoo Noh, Alexandre Galashov, Leonard Hasenclever, Arun Ahuja, Greg Wayne, Razvan Pascanu, Yee Whye Teh, and Nicolas Heess. Exploiting hierarchy for learning and transfer in kl-regularized rl. arXiv preprint arXiv:1903.07438, 2019.
[50] Tom Veniat, Ludovic Denoyer, and Marc'Aurelio Ranzato. Efficient continual learning with modular networks and task-driven priors. arXiv preprint arXiv:2012.12631, 2020.
[51] Yabin Wang, Zhiwu Huang, and Xiaopeng Hong. S-prompts learning with pre-trained transformers: An occam's razor for domain incremental learning. Advances in Neural Information Processing Systems, 35:5682-5695, 2022.
[52] Zifeng Wang, Zizhao Zhang, Sayna Ebrahimi, Ruoxi Sun, Han Zhang, Chen-Yu Lee, Xiaqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, et al. Dualprompt: Complementary prompting for rehearsal-free continual learning. In European Conference on Computer Vision, pages 631-648. Springer, 2022.
[53] Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, and Tomas Pfister. Learning to prompt for continual learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 139-149, 2022.
[54] Olivia Wiles, Sven Gowal, Florian Stimberg, Sylvestre-Alvise Rebuffi, Ira Ktena, Krishnamurthy Dj Dvijotham, and Ali Taylan Cemgil. A fine-grained analysis on distribution shift. In International Conference on Learning Representations, 2022.
[55] Keyulu Xu, Mozhi Zhang, Jingling Li, Simon Shaolei Du, Ken-Ichi Kawarabayashi, and Stefanie Jegelka. How neural networks extrapolate: From feedforward to graph neural networks. In International Conference on Learning Representations, 2021.
[56] Aron Yu and Kristen Grauman. Fine-grained visual comparisons with local learning. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 192-199, 2014.
[57] Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, and Dragomir Radev. Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3911-3921, Brussels, Belgium, October-November 2018. Association for Computational Linguistics.
[58] Aimen Zerroug, Mohit Vaishnav, Julien Colin, Sebastian Musslick, and Thomas Serre. A benchmark for compositional visual reasoning. Advances in Neural Information Processing Systems, 35:29776-29788, 2022.
[59] Mengmi Zhang, Tao Wang, Joo Hwee Lim, Gabriel Kreiman, and Jiashi Feng. Variational prototype replays for continual learning. arXiv preprint arXiv:1905.09447, 2019.
[60] Xingxuan Zhang, Linjun Zhou, Renzhe Xu, Peng Cui, Zheyan Shen, and Haoxin Liu. Nico++: Towards better benchmarking for domain generalization. arXiv preprint arXiv:2204.08040, 2022.