History: J. Mundy - Object recognition in the geometric era: A retrospective;
Camera geometry: Forsyth & Ponce Ch.1-2. Hartley & Zisserman - Ch.6;
Image procesing: End-to-end interpretable learning of non-blind image deblurring [Eboli, Sun and Ponce, ECCV 2022], Lucas-kanade reloaded: End-to-end super-resolution from raw image bursts [Lecouat, Ponce and Mairal, ICCV 2021]

Scale and affine invariant interest point detectors [Mikolajczyk and Schmid, IJCV 2004], Distinctive image features from scale-invariant keypoints [D. Lowe, IJCV 2004] (SIFT), R. Szeliski, Sections 7.1.1 (feature detectors), 7.1.2 (feature descriptors), 7.1.3 (feature matching), 7.4.2 (Hough transform), 8.1.4 (RANSAC), Video Google: Efficient visual search of videos [Sivic and Zisserman, ICCV 2003] (Bag of features)

pytorch_tutorial.ipynb

Gradient-based learning applied to document recognition [Lecun et al., IEEE 1998] (CNN), ImageNet Classification with Deep Convolutional Neural Networks [Krizhevsky et al., NeurIPS 2012] (AlexNet), Visualizing and Understanding Convolutional Networks [Zeiler and Fergus, ECCV 2014], Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks [Oquab et al., CVPR 2014] (pretraining), Very Deep Convolutional Networks for Large-Scale Visual Recognition [Simonyan and Zisserman, ICLR 2015] (VGGNet), Deep Residual Learning for Image Recognition [He et al., CVPR 2016] (ResNet), Attention is all you need [Vaswani et al., NeurIPS 2017] (Transformers), An image is worth 16x16 words: Transformers for image recognition at scale [Dosovitskiy et al., ICLR 2021] (ViT)

Rich feature hierarchies for accurate object detection and semantic segmentation [Girshick et al., CVPR 2014] (R-CNN), Fast R-CNN, [Girshick, CVPR 2015], Faster R-CNN: Towards real-time object detection with region proposal networks [Ren et al., NeurIPS 2015], You only look once: Unified, real-time object detection [Redmon et al., CVPR 2016] (YOLO), Fully convolutional networks for semantic segmentation [Long et al., CVPR 2015] (FCN), Mask R-CNN [He et al., ICCV 2017], Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields [Cao et al., CVPR 2017] (OpenPose)

Fast approx. nearest neighbors with automatic algorithm configuration [Muja and Lowe, VISAPP 2009], Video Google: Efficient visual search of videos [Sivic and Zisserman, Book chapter 2006], Object retrieval with large vocabularies and fast spatial matching [Philbin et al., CVPR 2007], Improving bag-of-features for large scale image search [Jegou et al., IJCV 2010], Aggregating local image descriptors into compact codes [Jegou et al., PAMI 2011], Efficient Diffusion on Region Manifolds [Iscen et al., CVPR 2017], NetVLAD: CNN architecture for weakly-supervised place recognition [Arandjeylovic et al., PAMI 2018], Howto100M: Learning a Text-video Embedding by Watching Hundred Million Narrated Video Clips [Miech et al. ICCV 2019].

-Generation Chapter: Probabilistic Machine Learning: Advanced Topics [Murphy 2023],
-VAEs: Auto-Encoding Variational Bayes [Kingma and Welling, ICLR 2014],
-GANs: Generative adversarial nets [Goodfellow et al., NeurIPS 2014],
-Diffusion: Denoising diffusion probabilistic models [Ho et al., NeurIPS 2020],
-Diffusion tutorial: Understanding Diffusion Models: A Unified Perspective [Luo 2022],
-CLIP: Learning Transferable Visual Models From Natural Language Supervision [Radford et al., ICML 2021],
-Stable Diffusion: High-Resolution Image Synthesis with Latent Diffusion Models [Rombach et al., CVPR 2022],
-BLIP: BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation [Li et al., ICML 2022]

Is object localization for free? - Weakly-supervised learning with convolutional neural networks [Oquab et al., CVPR 2015],
Unsupervised learning from narrated instruction videos [Alayrac et al., CVPR 2016],
Learning from Synthetic Humans [Varol et al., CVPR 2017],
Learning joint reconstruction of hands and manipulated objects [Hasson et al., CVPR 2019],
End-to-End Learning of Visual Representations from Uncurated Instructional Videos [Miech et al., CVPR 2020]

Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation [Brox and Malik, PAMI 2011],
Deepflow: Large displacement optical flow with deep matching [Weinzaepfel et al., CVPR 2013],
Learning realistic human actions from movies [Laptev et al., CVPR 2008],
Dense trajectories and motion boundary descriptors for action recognition [Wang et al., CVPR 2011],
Two-stream convolutional networks for action recognition in videos [Simonyan and Zisserman, NIPS 2014],
Learning spatiotemporal features with 3D convolutional networks [Tran et al., ICCV 2015]

1. 3D analysis: Volumetric and multi-view cnns for object classification on 3d data [Qi et al., CVPR 2016]; PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation [Qi et al., CVPR 2017]; 3D-CODED: 3D correspondences by deep deformation [Groueix et al., ECCV 2018].
2. 3D generation: A point set generation network for 3d object reconstruction from a single image [Fan et al., CVPR 2017]; AtlasNet: A papier-mâché approach to learning 3d surface generation [Groueix et al., CVPR. 2018]; Deepsdf: Learning continuous signed distance functions for shape representation [Park et al., CVPR 2019]; Nerf: Representing scenes as neural radiance fields for view synthesis [Midenhall et al., ECCV 2020].
3. Recent works Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives [Monnier et al., NeurIPS 2023]; Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans [Loiseau et al., ArXiv 2023]; Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency [Monnier et al., ECCV 2022]; 3D Gaussian Splatting for Real-Time Radiance Field Rendering [Kerbl et al., Siggraph 2023]
4. Training with synthetic data: Domain randomization for transferring deep neural networks from simulation to the real world [Tobin et al., IROS 2017]; Unbiased look at dataset bias [Torralba and Efros, CVPR 2011]; Domain-adversarial training of neural networks [Ganin et al., JMLR 2016].