[77] Patrick Shafto, Noah D Goodman, and Thomas L Griffiths. A rational account of pedagogical reasoning: Teaching by, and learning from, examples. Cognitive psychology, 71:55-89, 2014.
[78] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
[79] Johannes JD Singer, Katja Seeliger, Tim C Kietzmann, and Martin N Hebart. From photos to sketches-how humans and deep neural networks process objects across different levels of visual abstraction. Journal of vision, 22(2):4-4, 2022.
[80] Linda B Smith, Susan S Jones, Barbara Landau, Lisa Gershkoff-Stowe, and Larissa Samuelson. Object name learning provides on-the-job training for attention. Psychological science, 13(1):13-19, 2002.
[81] Joan G Snodgrass and Mary Vanderwart. A standardized set of 260 pictures: norms for name agreement, image agreement, familiarity, and visual complexity. Journal of experimental psychology: Human learning and memory, 6(2):174, 1980.
[82] Jifei Song, Qian Yu, Yi-Zhe Song, Tao Xiang, and Timothy M Hospedales. Deep spatial-semantic attention for fine-grained sketch-based image retrieval. In Proceedings of the IEEE international conference on computer vision, pages 5551–5560, 2017.
[83] Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta. Revisiting unreasonable effectiveness of data in deep learning era. In Proceedings of the IEEE international conference on computer vision, pages 843-852, 2017.
[84] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818-2826, 2016.
[85] Michael Henry Tessler and Noah D Goodman. The language of generalization. Psychological review, 126(3):395, 2019.
[86] Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li. Yfcc100m: The new data in multimedia research. Communications of the ACM, 59(2):64-73, 2016.
[87] Ilya O Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, et al. Mlp-mixer: An all-mlp architecture for vision. Advances in neural information processing systems, 34:24261-24272, 2021.
[88] Aditay Tripathi, Rajath R Dani, Anand Mishra, and Anirban Chakraborty. Sketch-guided object localization in natural images. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VI 16, pages 532–547. Springer, 2020.
[89] Edward R. Tufte. The Visual Display of Quantitative Information. Graphics Press, USA, 1986.
[90] Barbara Tversky. Some ways that maps and diagrams communicate. In *Spatial cognition II: Integrating abstract theories*, empirical studies, formal methods, and practical applications, pages 72–79. Springer, 2000.
[91] Yael Vinker, Ehsan Pajouheshgar, Jessica Y Bo, Roman Christian Bachmann, Amit Haim Bermano, Daniel Cohen-Or, Amir Zamir, and Ariel Shamir. Clipasso: Semantically-aware object sketching. arXiv preprint arXiv:2202.05822, 2022.
[92] Ivan Viola and Tobias Isenberg. Pondering the concept of abstraction in (illustrative) visualization. IEEE transactions on visualization and computer graphics, 24(9):2573-2588, 2017.
[93] Andrey Voynov, Kfir Aberman, and Daniel Cohen-Or. Sketch-guided text-to-image diffusion models. In ACM SIGGRAPH 2023 Conference Proceedings, pages 1–11, 2023.