Room: ENS Ulm Salle UV aile Rataud, 45 rue d'Ulm
Class time: Tuesday 16:15-19:15
List of received reports:
Automated object recognition -- and more generally scene analysis -- from photographs and videos is the grand challenge of computer vision. This course presents the image, object, and scene models, as well as the methods and algorithms, used today to address this challenge.
There will be three programming assignments representing 50% (10% + 20% + 20%) of the grade. The supporting materials for the programming assignments and final projects will be in Matlab.
The final project will represent 50% of the grade. Suggested topics for final projects will be added here. See examples from the last year.
Computer vision and machine learning talks
You are welcome to attend seminars in the Willow group. Please see the current seminar schedule. Typically, these are one hour research talks given by visiting speakers. The talks are at 23 avenue d'Italie. Ring the bell to get into the building, then take the elevator to the 5th floor.
Topic and reading materials.
Introduction (J. Ponce);
Instance-level recognition I. - Camera geometry (J. Ponce)
Class logistics, assignments, final projects (I. Laptev and J. Sivic)
Background materials: History: J. Mundy - Object recognition in the geometric era: A retrospective.; Camera geometry: Forsyth&Ponce Ch.1-2. Hartley&Zisserman - Ch.6
Instance-level recognition II. - Local invariant features (1.5hrs, C. Schmid); Correspondence and image matching (1.5hrs, I. Laptev)
Materials: Mikolajczyk & Schmid, Scale and affine invariant interest point detectors, IJCV 2004; D. Lowe, Distinctive image features from scale-invariant keypoints, IJCV 2004, R. Szeliski (pdf), Sections 4.1, 4.1.1 and 4.1.2 from Chapter 4: Feature detection and matching;R. Szeliski (pdf), Sections 4.1.3 (feature matching) and 6.1 (feature-based alignment);
Assignment: Assignment 1 out.
Instance-level recognition III. - Efficient visual search (1.5hrs, J. Sivic);
Materials: Muja & Lowe, Fast approx. nearest neighbors with automatic algorithm configuration, VISAPP'09; Sivic & Zisserman, Video Google: Efficient visual search of videos (chapter from this book), Philbin et al., Object retrieval with large vocabularies and fast spatial matching, CVPR'07.
Instance-level recognition IV. - Very large scale image indexing (1.5hrs, J. Sivic)
Materials: Jegou et al., Improving bag-of-features for large scale image search, IJCV 2010; Jegou et al., Aggregating local image descriptors into compact codes, PAMI 2011;
Sparse coding and dictionary learning for image analysis (1.5hrs, J. Ponce)
Materials: Bach, Mairal, Ponce, Sapiro, Tutorial on sparse coding and dictionary learning for image analysis, at CVPR'10.
Bag-of-feature models for category-level recognition (1.5hrs, C. Schmid)
Materials: Csurka et al., Visual categorization with bags of keypoints, 2004
Assignments: Assignment 1 due.
Neural networks; Optimization methods (N. Le Roux)
2. For more details on neural networks you can watch the video lectures by Hugo Larochelle. The website also includes links to useful reading materials such as “Practical Recommendations for Gradient-Based Training of Deep Architectures” by Y. Bengio.
3. The draft of the book on deep learning by Y. Bengio
Assignments: Topic suggestions for the final project are out.
Convolutional neural networks for visual recognition (J. Sivic and I. Laptev)
Y. LeCun et al., Gradient-based learning applied to document recognition, Proceedings of the IEEE 86(11): 2278–2324, 1998.
M.D. Zeiler, R. Fergus, Visualizing and Understanding Convolutional Networks, ECCV 2014.
M. Oquab et al., Learning and Transferring Mid-Level Image Representations
using Convolutional Neural Networks, CVPR 2014
Assignments: Assignment 2 due.
No lecture - public holiday
Structured models for category-level localization and pose estimation (J. Sivic)
Felzenszwalb et al., A Discriminatively Trained, Multiscale, Deformable Part Model, CVPR’08; Pascal VOC Challenge; Yang and Ramanan, Articulated Human Detection with Flexible Mixtures of Parts, PAMI’13. P. Felzenszwalb and D. Huttenlocher, Distance Transforms of Sampled Functions.
Assignments: Assignment 3 due. Final project proposal due (Nov 21).
Motion and human actions I. (I. Laptev)
Materials: Laptev et al., Learning realistic human actions from movies, CVPR’08; Want et al., Dense trajectories and motion boundary descriptors for action recognition, CVPR’11.
Motion and human actions II. (C. Schmid)
Scenes, Objects and 3D reasoning (I. Laptev, J. Sivic)
Materials: A. Oliva and A. Torralba: Modeling the shape of the scene: A holistic representation of the spatial envelope, IJCV 2001; J. Xiao et al.: Sun database: Large-scale scene recognition from abbey to zoo, CVPR 2010; D.Hoiem et al.: Putting Objects in Perspective, CVPR 2006; C. Desai et al.: Discriminative models for multi-class object layout, CVPR 2009; N. Kumar et al.: Attribute and simile classifiers for face verification, ICCV 2009.
Final project presentations and evaluation (I. Laptev, J. Sivic)
Tue Dec 16 presentations are at the standard class location and time (16:15-19:15 ENS Ulm). Fri Dec 19 (14:00-17:00) presentations are at INRIA, 23 Av. d’Italie, 75013. See presentation schedule.
D.A. Forsyth and J. Ponce, "Computer Vision: A Modern Approach", Prentice-Hall, 2nd edition, 2011
J. Ponce, M. Hebert, C. Schmid and A. Zisserman "Toward Category-Level Object Recognition", Lecture Notes in Computer Science 4170, Springer-Verlag, 2007
O. Faugeras, Q.T. Luong, and T. Papadopoulo, "Geometry of Multiple Images", MIT Press, 2001.
R. Hartley and A. Zisserman, "Multiple View Geometry in Computer Vision", Cambridge University Press, 2004.
J. Koenderink, "Solid Shape", MIT Press, 1990
R. Szeliski, "Computer Vision: Algorithms and Applications", 2009. A draft of a new book, which can be downloaded online.