References

★ marks especially influential, highly-cited papers.

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• Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. (47)
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• Daugman, J. G. (1985). Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters. (16)
• Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). ImageNet: A Large-Scale Hierarchical Image Database. ★ (35)
• Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ★ (46)
• Duda, R. O., & Hart, P. E. (1972). Use of the Hough Transformation to Detect Lines and Curves in Pictures. (12)
• Farnebäck, G. (2003). Two-Frame Motion Estimation Based on Polynomial Expansion. (20)
• Fischler, M. A., & Bolles, R. C. (1981). Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. ★ (22)
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• Harris, C., & Stephens, M. (1988). A Combined Corner and Edge Detector. (19)
• He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. ★ (35)
• He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. (32)
• He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask R-CNN. ★ (42)
• Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. (57)
• Horn, B. K. P., & Schunck, B. G. (1981). Determining Optical Flow. ★ (20)
• Hough, P. V. C. (1962). Method and Means for Recognizing Complex Patterns. (12)
• Hubel, D. H., & Wiesel, T. N. (1959). Receptive Fields of Single Neurones in the Cat's Striate Cortex. (16)
• Ioffe, S., & Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. (35)
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• Kingma, D. P., & Ba, J. (2014). Adam: A Method for Stochastic Optimization. ★ (32)
• Kirillov, A., Mintun, E., Ravi, N., et al. (2023). Segment Anything. (51)
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. ★ (35)
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• Lowe, D. G. (2004). Distinctive Image Features from Scale-Invariant Keypoints. ★ (19)
• Lucas, B. D., & Kanade, T. (1981). An Iterative Image Registration Technique with an Application to Stereo Vision. (20)
• Marcelja, S. (1980). Mathematical Description of the Responses of Simple Cortical Cells. (16)
• Marr, D., & Hildreth, E. (1980). Theory of Edge Detection. (13)
• Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020). NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. (58)
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• Otsu, N. (1979). A Threshold Selection Method from Gray-Level Histograms. ★ (3)
• Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning Transferable Visual Models From Natural Language Supervision. ★ (49)
• Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. ★ (40)
• Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. ★ (40)
• Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. ★ (41)
• Rowley, H. A., Baluja, S., & Kanade, T. (1996). Neural Network-Based Face Detection. (39)
• Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. (38)
• Shi, J., & Tomasi, C. (1994). Good Features to Track. (19)
• Simonyan, K., Vedaldi, A., & Zisserman, A. (2013). Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. (38)
• Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. ★ (35)
• Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., & Ganguli, S. (2015). Deep Unsupervised Learning using Nonequilibrium Thermodynamics. (57)
• Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. ★ (34)
• Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going Deeper with Convolutions. ★ (35)
• Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All You Need. (45)
• Viola, P., & Jones, M. (2001). Rapid Object Detection Using a Boosted Cascade of Simple Features. (39)
• Wang, J., Chen, M., Karaev, N., Vedaldi, A., Rupprecht, C., & Novotny, D. (2025). VGGT: Visual Geometry Grounded Transformer. (56)
• Wen, B., Trepte, M., Aribido, J., Kautz, J., Gallo, O., & Birchfield, S. (2025). FoundationStereo: Zero-Shot Stereo Matching. (55)
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• Yao, Y., Luo, Z., Li, S., Zhang, J., Ren, Y., Zhou, L., Fang, T., & Quan, L. (2020). BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks. (28)
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• Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. (2019). On the Continuity of Rotation Representations in Neural Networks. (56)