MIT CSAIL 6.819/6.869: Advances in Computer Vision
Spring 2021
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Books

Computer vision:

• [Sz] Szeliski, Computer Vision: Algorithms and Applications, Springer, 2010 (online draft)
• [HZ] Hartley and Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, 2004
• [FP] Forsyth and Ponce, Computer Vision: A Modern Approach, Prentice Hall, 2002
• [Pa] Palmer, Vision Science, MIT Press, 1999

Learning:

• [GBC] Goodfellow, Bengio, Courville, Deep Learning, MIT Press, 2016
• [Mi] Mitchel, Machine Learning, McGraw-Hill, 1997
• [DHS] Duda, Hart and Stork, Pattern Classification (2nd Edition), Wiley-Interscience, 2000
• [SB] Sutton & Barto, On-line book. The classic reference to the field of reinforcement learning.

Graphical models:

• [KF] Koller and Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009

Resources

Image datasets:

• Labelme: an online annotation tool to build image databases for computer vision research
• OpenSurfaces: a large database of annotated surfaces created from real-world consumer photographs.
• ImageNet: a large-scale image dataset for visual recognition organized by WordNet hierarchy
• ADE20K Dataset: a benchmark for scene and instance segmentation, with pixelwise semantic annotations
• Places Database: a scene-centric database with 205 scene categories and 2.5 millions of labelled images
• NYU Depth Dataset v2: a RGB-D dataset of segmented indoor scenes
• Microsoft COCO: a new benchmark for image recognition, segmentation and captioning
• Flickr100M: 100 million creative commons Flickr images
• Labeled Faces in the Wild: a dataset of 13,000 labeled face photographs
• Human Pose Dataset: a benchmark for articulated human pose estimation
• YouTube Faces DB: a face video dataset for unconstrained face recognition in videos
• UCF101: an action recognition data set of realistic action videos with 101 action categories
• HMDB-51: a large human motion dataset of 51 action classes

Top computer vision conferences and papers:

• CVPR: IEEE Conference on Computer Vision and Pattern Recognition
• ICCV: International Conference on Computer Vision
• ECCV: European Conference on Computer Vision
• NIPS: Neural Information Processing Systems

Related courses:

• Introduction to Computer Vision, by Michael Black
• Learning-Based Methods in Vision, by Alyosha Efros
• Computer Vision, by Kristen Grauman
• Computer Vision, by Rob Fergus
• Introduction to Computer Vision, by Fei-Fei Li

Other resources:

• The Computer Vision Industry
• MatCovNet-CNN Toolbox in Matlab
• Pytorch
• Tensorflow