References
Bibliography
[1]M. Abadi and et al. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015.
[2]R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk. SLIC superpixels compared to state-of-the-art superpixel methods. PAMI, 24(11):2274–2282, 2012.
[3]P. Anderson, B. Fernando, M. Johnson, and S. Gould. SPICE: Semantic propositional image caption evaluation. In ECCV, 2016.
[4]P. Anderson, B. Fernando, M. Johnson, and S. Gould. Guided open vocabulary image captioning with constrained beam search. In EMNLP, 2017.
[5]P. Anderson, X. He, C. Buehler, D. Teney, M. Johnson, S. Gould, and L. Zhang. Bottom-up and top-down attention for image captioning and visual question answering. In CVPR, 2018a.
[6]P. Anderson, Q. Wu, D. Teney, J. Bruce, M. Johnson, N. Sunderhauf, I. Reid, S. Gould, and A. van den Hengel. Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments. In CVPR, 2018b.
[7]S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra, C. Lawrence Zitnick, and D. Parikh. VQA: Visual question answering. In ICCV, pages 2425–2433, 2015.
[8]P. L. Bartlett, P. M. Long, G. Lugosi, and A. Tsigler. Benign overfitting in linear regression. arXiv preprint arXiv:1906.11300, 2020.
[9]Y. Ben-Shabat, C. H. Koneputugodage, and S. Gould. DiGS: Divergence guided shape implicit neural representation for unoriented point clouds. In CVPR, 2022.
[10]S. P. Boyd and L. Vandenberghe. Convex Optimization. Cambridge, 2004.
[11]J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, and Q. Zhang. JAX: composable transformations of Python+NumPy programs, 2018.
[12]N. Carion, A. Kirillov, F. Massa, G. Synnaeve, N. Usunier, and S. Zagoruyko. End-to-end object detection with transformers. In ECCV, 2020.
[13]M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin. Emerging properties in self-supervised vision transformers. In ICCV, 2021.
[14]J. Carreira and A. Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In CVPR, 2017.
[15]X. Chen and K. He. Exploring simple siamese representation learning. In CVPR, 2021.
[16]M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele. The cityscapes dataset for semantic urban scene understanding. In CVPR, 2016.
[17]A. Criminisi. Microsoft Research Cambridge (MSRC) object recognition pixel-wise labeled image database (version 2), 2004.
[18]G. Cybenko. Approximation by superpositions of a sigmoidal function. Math. Control Signal Systems 2, 1989.
[19]T. Dao, D. Y. Fu, S. Ermon, A. Rudra, and C. Ré. FlashAttention: Fast and memory-efficient exact attention with IO-awareness. In NeurIPS, 2022.
[20]A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39, 1977.
[21]J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A large-scale hierarchical image database. In CVPR, 2009.
[22]J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In ACL, 2019.
[23]A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021.
[24]J. Duchi, E. Hazan, and Y. Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 2011.
[25]J. L. Elman. Finding structure in time. Cognitive Science, 14:179–211, 1990.
[26]M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2009 (VOC2009) Results, 2009.
[27]P. F. Felzenszwalb and D. P. Huttenlocher. Efficient graph-based image segmentation. IJCV, 2004.
[28]T. Gao, X. Yao, and D. Chen. SimCSE: Simple contrastive learning of sentence embeddings. In EMNLP, 2021.
[29]R. Girshick. Fast R-CNN. In ICCV, 2015.
[30]R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014.
[31]X. Glorot and Y. Bengio. Understanding the difficulty of training deep feedforward neural networks. In International Conference on Artificial Intelligence and Statistics, 2010.
[32]I. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio. Maxout networks. In ICML, 2013.
[33]I. Goodfellow, Y. Bengio, and A. Courville. Deep Learning. MIT Press, 2016.
[34]Y. Gorishniy, I. Rubachev, V. Khrulkov, and A. Babenko. Revisiting deep learning models for tabular data. In NeurIPS, 2021.
[35]S. Gould, T. Gao, and D. Koller. Region-based segmentation and object detection. In NeurIPS, 2009.
[36]S. Gould, R. Hartley, and D. Campbell. Deep declarative networks. PAMI, 2021.
[37]J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar, et al. Bootstrap your own latent—a new approach to self-supervised learning. NeurIPS, 2020.
[38]R. Hadsell, S. Chopra, and Y. Lecun. Dimensionality reduction by learning an invariant mapping. In CVPR, 2006.
[39]R. I. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2004.
[40]K. He, X. Zhang, S. Ren, and J. Sun. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In CVPR, 2015.
[41]K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016.
[42]K. He, G. Gkioxari, P. Dollar, and R. Girshick. Mask R-CNN. In ICCV, 2017.
[43]K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, 2020.
[44]G. E. Hinton, S. Osindero, and Y. W. Teh. A fast learning algorithm for deep belief nets. Neural computation, 18(7):1527–1554, 2006.
[45]J. Ho, A. Jain, and P. Abbeel. Denoising diffusion probabilistic models. In NeurIPS, 2020.
[46]S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural Computation, 9:1735–1780, 1997.
[47]Y. Hong. Learning Language-Guided Visual Navigation. PhD thesis, Australian National University, 2023. URL https://openresearch-repository.anu.edu.au/items/d9706162-cfe2-49a6-81bd-047657268587.
[48]K. Hornik. Approximation capabilities of multilayer feedforward networks. Neural Networks, 4(2):251–257, 1991.
[49]X. Huang, A. Khetan, M. Cvitkovic, and Z. Karnin. TabTransformer: Tabular data modeling using contextual embeddings, 2020.
[50]S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML, 2015.
[51]J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei. Scaling laws for neural language models, 2020.
[52]A. Karpathy and L. Fei-Fei. Deep visual-semantic alignments for generating image descriptions. In CVPR, 2015.
[53]D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. In ICLR, 2014.
[54]D. P. Kingma and M. Welling. Auto-encoding variational bayes. In ICLR, 2014.
[55]D. P. Kingma and M. Welling. An introduction to variational autoencoders. Foundations and Trends in Machine Learning, 12(4):307–392, 2019.
[56]A. Kirillov, K. He, P. Dollar, R. Girshick, and C. Rother. Panoptic segmentation. In CVPR, 2018.
[57]A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Dollar, and R. Girshick. Segment anything. In ICCV, 2023.
[58]D. Koller and N. Friedman. Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009.
[59]R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y. Kalantidis, L.-J. Li, D. A. Shamma, M. S. Bernstein, and F.-F. Li. Visual genome: Connecting language and vision using crowdsourced dense image annotations. IJCV, 123:32–73, 2017.
[60]A. Krizhevsky. Learning multiple layers of features from tiny images. Technical report, University of Toronto, 2009.
[61]A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In NeurIPS, 2012.
[62]Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. IEEE, 1998.
[63]F.-F. Li, M. Andreeto, M. Ranzato, and P. Perona. Caltech 101. Technical report, California Institute of Technology, 2022a.
[64]H. Li, M. Nguyen, and D. Pimentel-Alarcon. Preventing collapse in contrastive learning with orthonormal prototypes (clop). arXiv preprint arXiv:2403.18699, 2024.
[65]J. Li, D. Li, C. Xiong, and S. Hoi. BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In CVPR, 2022b.
[66]T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, and C. L. Zitnick. Microsoft COCO: Common objects in context. In ECCV, 2014.
[67]W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg. SSD: Single shot multibox detector. In ECCV, 2016.
[68]J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015.
[69]A. López-Cifuentes, M. Escudero-Viñolo, J. Bescós, and Á. García-Martín. Semantic-aware scene recognition. Pattern Recognition, 2020.
[70]I. Loshchilov and F. Hutter. Decoupled weight decay regularization. In ICLR, 2019.
[71]T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. In ICLR, 2013.
[72]M. Milakov and N. Gimelshein. Online normalizer calculation for softmax. arXiv preprint arXiv:1805.02867, 2018.
[73]V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller. Playing atari with deep reinforcement learning, 2013.
[74]V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 2015.
[75]K. Murphy. Reinforcement learning: An overview, 2025. URL https://arxiv.org/abs/2412.05265.
[76]Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng. Reading digits in natural images with unsupervised feature learning. NeurIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011.
[77]A. v. d. Oord, Y. Li, and O. Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018.
[78]M. Oquab, T. Darcet, T. Moutakanni, H. Vo, M. Szafraniec, V. Khalidov, P. Fernandez, D. Haziza, F. Massa, A. El-Nouby, M. Assran, N. Ballas, W. Galuba, R. Howes, P.-Y. Huang, S.-W. Li, I. Misra, M. Rabbat, V. Sharma, G. Synnaeve, H. Xu, H. Jegou, J. Mairal, P. Labatut, A. Joulin, and P. Bojanowski. DINOv2: Learning robust visual features without supervision, 2023.
[79]G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan. Normalizing flows for probabilistic modeling and inference. JMLR, 22(1), Jan 2021. ISSN 1532-4435.
[80]A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer. Automatic differentiation in PyTorch. In NeurIPS Autodiff Workshop, 2017.
[81]J. Pennington, R. Socher, and C. Manning. GloVe: Global vectors for word representation. In EMNLP, 2014.
[82]S. J. Prince. Understanding Deep Learning. The MIT Press, 2023.
[83]A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever. Learning transferable visual models from natural language supervision. In ICML, 2021.
[84]J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. You only look once: Unified, real-time object detection. In CVPR, 2016.
[85]S. Ren, K. He, R. B. Girshick, and J. Sun. Faster R-CNN: towards real-time object detection with region proposal networks. CoRR, abs/1506.01497, 2015.
[86]R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, 2022.
[87]O. Ronneberger, P. Fischer, and T. Brox. U-Net: Convolutional networks for biomedical image segmentation. In MICCAI, 2015.
[88]F. Rosenblatt. Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Books, 1962.
[89]S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, 2002.
[90]F. Schroff, D. Kalenichenko, and J. Philbin. FaceNet: A unified embedding for face recognition and clustering. In CVPR, 2015.
[91]J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov. Proximal policy optimization algorithms. CoRR, abs/1707.06347, 2017.
[92]Z. Shao, P. Liu, Q. Wang, R. Huang, H. Li, G. Xu, H. Song, C. Yang, Y. Wu, J. Sun, et al. DeepSeekMath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024.
[93]K. Simonyan and A. Zisserman. Two-stream convolutional networks for action recognition in videos. In Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger, editors, NeurIPS, 2014.
[94]J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In NeurIPS, 2015.
[95]J. Su, Y. Lu, S. Pan, A. Murtadha, B. Wen, and Y. Liu. RoFormer: Enhanced transformer with rotary position embedding, 2023.
[96]I. Sutskever, O. Vinyals, and Q. V. Le. Sequence to sequence learning with neural networks. In NeurIPS, 2014.
[97]R. S. Sutton and A. G. Barto. Reinforcement learning: An introduction. MIT press, 2018.
[98]S. Thrun, M. Montemerlo, H. Dahlkamp, D. Stavens, A. Aron, J. Diebel, P. Fong, J. Gale, M. Halpenny, G. Hoffmann, K. Lau, C. Oakley, M. Palatucci, V. Pratt, P. Stang, S. Strohband, C. Dupont, L.-E. Jendrossek, C. Koelen, C. Markey, C. Rummel, J. van Niekerk, E. Jensen, P. Alessandrini, G. Bradski, B. Davies, S. Ettinger, A. Kaehler, A. Nefian, and P. Mahoney. Stanley: The Robot That Won the DARPA Grand Challenge, pages 1–43. Springer Berlin Heidelberg, 2007. doi: 10.1007/978-3-540-73429-1_1.
[99]J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders. Selective search for object recognition. IJCV, 104(2):154–171, 2013.
[100]A. van den Oord, O. Vinyals, and K. Kavukcuoglu. Neural discrete representation learning. In NeurIPS, 2017.
[101]H. van Hasselt, A. Guez, and D. Silver. Deep reinforcement learning with double Q-learning. In AAAI, 2015.
[102]V. N. Vapnik. Statistical Learning Theory. Wiley-Interscience, 1998.
[103]A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin. Attention is all you need. In NeurIPS, 2017.
[104]K. Weinberger and L. Saul. Distance metric learning for large margin nearest neighbor classification. JMLR, 10:207–244, 2009.
[105]B. Widrow and M. E. Hoff. Adaptive switching circuits. IRE WESCON Convention Record, 1960.
[106]X. Zhai, B. Mustafa, A. Kolesnikov, and L. Beyer. Sigmoid loss for language image pre-training. In ICCV, 2023.
[107]A. Zhang, Z. C. Lipton, M. Li, and A. J. Smola. Dive into Deep Learning. Cambridge University Press, 2023.
[108]Z. Zhong, L. Zheng, G. Kang, S. Li, and Y. Yang. Random erasing data augmentation. In AAAI, 2020.