News
- 09/2026 We will use Google Classroom for announcements, discussions, and assignment collection. The access code will be announced during the lectures.
Information
Course description
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.
Assignments
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 Python and make use of Jupyter notebooks. For additional technical instructions on the assignments please follow this link.
Final project
The final project will represent 50% of the grade.
Collaboration policy
You can discuss the assignments and final projects with other students in the class. Discussions are encouraged and are an essential component of the academic environment. However, each student has to work out their assignment alone (including any coding, experiments or derivations) and submit their own report. For the final project, you may work alone or in a group of maximum of 2 people. If working in a group, we expect a more substantial project, and an equal contribution from each student in the group. The final project report needs to explicitly specify the contribution of each student. Both students are expected to present the project at the oral presentation and contribute equally to writing the report. The assignments and final projects will be checked to contain original material. Any uncredited reuse of material (text, code, results) will be considered as plagiarism and will result in zero points for the assignment / final project. If a plagiarism is detected, the student will be reported to MVA.
Computer vision and machine learning talks
You are welcome to attend seminars in the Imagine and Willow research groups. Please see the seminar schedules for Imagine and Willow. Typically, these are one hour research talks given by visiting speakers. Imagine talks are at Ecole des Ponts. Willow talks are at Inria, 48 Rue Barrault, 75013 (when you enter the building, tell the receptionist you are going for a seminar).
Feedback
During any point in time, during or after the semester, do not hesitate to fill this form to provide anonymous feedback about the class.
Schedule (subject to change)
Lecture time: Tuesdays 15:00-18:00Lecture room: Amphi Luton, 24 rue du Faubourg Saint-Jacques, 75014 Paris (maps)
*A few exceptions are denoted in the schedule below.* We will switch a few times to Amphi Dieulafoy, 27 rue du Faubourg Saint-Jacques, 75014 Paris (maps)
The class Google Calendar is up to date with location information.
Note: Slides are provided after each lecture.
| # | Date | Lecturer | Topic and reading materials | Slides |
|---|---|---|---|---|
| Instance-level recognition | ||||
| 1 | Sep 29 | Gül Varol Jean Ponce |
Class logistics: assignments, final projects, grading; Introduction to visual recognition; Camera geometry; Image processing |
|
| 2 | Oct 6 | Gül Varol |
Instance-level recognition: local features, correspondence, image matching
Assignment 1 (A1) out. |
|
| Practical | Oct 13 *...* | TAs | Pytorch/Kaggle/Google Cloud tutorial. Presentations by TAs about their research topics. | |
| 3 | Oct 20 *Amphi Dieulafoy* | Gül Varol | Efficient visual search
Final project (FP) topics are out at the end of the lecture. |
|
| Category-level recognition | ||||
| 4 | Oct 27 | Gül Varol | Supervised learning and deep learning; Optimization and regularization for neural networks A1 due. A2 out. |
|
| 5 | Nov 3 | Gül Varol | Neural networks for visual recognition: CNNs and image classification
A3 out. |
|
| 6 | Nov 10 | Gül Varol | Beyond CNNs: Transformers; Beyond classification: Object detection; Segmentation; Human pose estimation A2 due. |
|
| Advanced topics | ||||
| 7 | Nov 17 *Amphi Dieulafoy* | Gül Varol | Generative models; Vision & language FP proposal due. |
|
| 8 | Nov 24 | Cordelia Schmid | Human action recognition in videos
A3 due. |
|
| 9 | Dec 1 | Ivan Laptev | Vision for robotics | |
| 10 | Dec 8 *Amphi Dieulafoy* | Mathieu Aubry | 3D computer vision | |
| FP | Jan 11-12 | Gül Varol | FP presentations Presentations will be virtual. FP report due Jan 18. |
|
Resources
- 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, 2nd ed.", 2022. Online book.
- Computer Vision: Models, Learning, and Inference by Simon J.D. Prince (2012)
- Understanding Deep Learning by Simon J.D. Prince (2023)
- Deep Learning by I. Goodfellow, Y. Bengio and A. Courville (2016)
- Michael Nielsen's online book on Neural Networks and Deep Learning (2019)
- David Forsyth's Applied Machine Learning textbook draft (2019)
- Andrej Karpathy blog
- Previous editions of the course: