[1] CarveKit. https://github.com/OPHoperHPO/image-background-remove-tool/. 8, 21
[2] Image database tid2013: Peculiarities, results and perspectives. Signal Processing: Image Communication, 30:57-77, 2015. 3
[3] Daniel E. Acuna, Max Berniker, Hugo L. Fernandes, and Konrad P. Kording. Using psychophysics to ask if the brain samples or maximizes. Journal of Vision, 15(3):7-7, 03 2015. 3
[4] Dan Amir and Yair Weiss. Understanding and simplifying perceptual distances. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12226-12235, 2021. 3
[5] Shir Amir, Yossi Gandelsman, Shai Bagon, and Tali Dekel. Deep vit features as dense visual descriptors. arXiv preprint arXiv:2112.05814, 2(3):4, 2021. 3
[6] Shekoofeh Azizi, Simon Kornblith, Chitwan Sahara, Mohammad Norouzi, and David J. Fleet. Synthetic data from diffusion models improves imagenet classification. ArXiv, abs/2304.08466, 2023. 3
[7] Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, and Boris Katz. Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019. 10, 23
[8] Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. Food-101-mining discriminative components with random forests. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VI 13, pages 446-461. Springer, 2014. 3
[9] Tim Brooks, Aleksander Holynski, and Alexei A. Efros. Instructpix2pix: Learning to follow image editing instructions, 2023. 3
[10] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9650-9660, 2021. 2, 3, 5, 6
[11] Patrick Cavanagh. The cognitive impenetrability of cognition. Behavioral and Brain Sciences, 22(3):370-371, 1999. 3
[12] Caroline Chan, Frédo Durand, and Phillip Isola. Learning to generate line drawings that convey geometry and semantics. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7915-7925, 2022. 2, 3, 4, 7
[13] Huiwen Chang, Han Zhang, Jarred Barber, AJ Maschinot, Jose Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Murphy, William T Freeman, Michael Rubinstein, et al. Muse: Text-to-image generation via masked generative transformers. arXiv preprint arXiv:2301.00704, 2023. 3
[14] Gal Chechik, Varun Sharma, Uri Shalit, and Samy Bengio. Large scale online learning of image similarity through ranking. Journal of Machine Learning Research, 11(3), 2010. 6
[15] Hila Chefer, Shir Gur, and Lior Wolf. Transformer interpretability beyond attention visualization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 782-791, 2021. 26
[16] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 3
[17] Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, and Jenia Jitsev. Reproducible scaling laws for contrastive language-image learning. ArXiv, abs/2212.07143, 2022. 5, 6
[18] Katherine Crowson. k-diffusion. https://github.com/crowsonkb/k-diffusion, 2022. 19
[19] Michael R.W. Dawson. Cognitive Impenetrability, pages 1-3. Springer International Publishing, Cham, 2017. 3
[20] Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, et al. Scaling vision transformers to 22 billion parameters. arXiv preprint arXiv:2302.05442, 2023. 3
[21] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. IEEE, 2009. 3, 19