[17] Andrew Hollingworth. Scene and position specificity in visual memory for objects. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(1):58, 2006.
[18] Ronghang Hu, Piotr Dálár, Kaiming He, Trevor Darrell, and Ross Girshick. Learning to segment every thing. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4233-4241, 2018.
[19] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. arXiv preprint arXiv:2304.02643, 2023.
[20] Nikolaus Kriegeskorte, Marieke Mur, and Peter A Bandettini. Representational similarity analysis-connecting the branches of systems neuroscience. Frontiers in systems neuroscience, page 4, 2008.
[21] David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville. Out-of-distribution generalization via risk extrapolation (rex). In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 5815-5826. PMLR, 18-24 Jul 2021. URL https://proceedings.mlr.press/v139/krueger21a.html.
[22] Xin Lai, Zhuotao Tian, Yukang Chen, Yanwei Li, Yuhui Yuan, Shu Liu, and Jiaya Jia. Lisa: Reasoning segmentation via large language model. arXiv preprint arXiv:2308.00692, 2023.
[23] Tim Lauer and Melissa L-H Võ. The ingredients of scenes that affect object search and perception. Human perception of visual information: Psychological and computational perspectives, pages 1-32, 2022.
[24] Tim Lauer, Verena Willenbockel, Laura Maffongelli, and Melissa L-H Võ. The influence of scene and object orientation on the scene consistency effect. Behavioural Brain Research, 394: 112812, 2020.
[25] Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy Hospedales. Learning to generalize: Meta-learning for domain generalization. In Proceedings of the AAAI conference on artificial intelligence, 2018.
[26] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dóllár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014.
[27] Yong Lin, Hanze Dong, Hao Wang, and Tong Zhang. Bayesian invariant risk minimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16021-16030, 2022.
[28] Jack W Lindsey and Elias B Issa. Factorized visual representations in the primate visual system and deep neural networks. bioRxiv, pages 2023-04, 2023.
[29] Xiao Liu, Ankur Sikarwar, Joo Hwee Lim, Gabriel Kreiman, Zenglin Shi, and Mengmi Zhang. Reason from context with self-supervised learning. arXiv preprint arXiv:2211.12817, 2022.
[30] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012-10022, 2021.
[31] Oge Marques, Elan Barenholtz, and Vincent Charvillat. Context modeling in computer vision: techniques, implications, and applications. Multimedia Tools and Applications, 51:303-339, 2011.
[32] Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro. Exploring generalization in deep learning. Advances in neural information processing systems, 30, 2017.