Advanced Visual Intelligence (AVI)
CS6XXX · Spring 2026 · Dept. of Data Science and AI, IIT Madras
Advanced Visual Intelligence
CS6XXX · Spring 2026 Dept. of Data Science and AI, IIT Madras · Dr. Ram Prabhakar
| Credits | 3-0-0-3 |
| Lecture | Mon / Wed / Fri · TBD |
| Venue | TBD |
| Office Hours | By appointment |
| Prerequisites | Linear Algebra, Probability, Python |
About
This course covers fundamental and advanced topics in computer vision and visual intelligence. Students will develop a strong understanding of both classical methods and modern deep learning approaches, with emphasis on mathematical foundations and practical implementation.
Topics include image formation, feature extraction, object detection and segmentation, generative models, video understanding, and vision-language models.
Announcements
| Date | |
|---|---|
| Jan 2026 | Course website is live. Welcome to AVI! |
Schedule
| Week | Topic | Slides | Reading | Assignment |
|---|---|---|---|---|
| 01 | Introduction to Computer Vision | Slides | Notes | — |
| 02 | Image Formation and Cameras | Slides | Ch. 2 | HW1 Out |
| 03 | Image Filtering and Edge Detection | Slides | Paper | — |
| 04 | Feature Detection and Description | Slides | Paper | HW1 Due |
| 05 | Deep Learning for Vision — CNNs | Slides | Paper | HW2 Out |
| 06 | Object Detection | Slides | Paper | — |
| 07 | Image Segmentation | Slides | Paper | HW2 Due |
| 08 | ⚡ Mid-semester Exam | — | — | — |
| 09 | Transformers for Vision (ViT) | Slides | Paper | HW3 Out |
| 10 | Generative Models — GANs, Diffusion | Slides | Paper | — |
| 11 | Video Understanding | Slides | Paper | HW3 Due |
| 12 | 3D Vision and Depth Estimation | Slides | Paper | HW4 Out |
| 13 | Vision-Language Models | Slides | Paper | — |
| 14 | Recent Topics + Guest Lecture | Slides | — | HW4 Due |
| 15 | ⚡ End-semester Exam | — | — | — |
Grading
| Component | Weight |
|---|---|
| Assignments (4 × 10%) | 40% |
| Mid-semester Exam | 25% |
| End-semester Exam | 35% |
Textbooks & References
Primary:
- Szeliski, Computer Vision: Algorithms and Applications (2nd ed., 2022) — Free PDF
- Goodfellow et al., Deep Learning (2016) — Free online
Reference:
- Prince, Understanding Deep Learning (2023) — Free PDF
- Forsyth & Ponce, Computer Vision: A Modern Approach
Policies
Attendance Not mandatory but strongly encouraged. Slides posted after each lecture.
Late Submissions 10% penalty per day. No submissions after 3 days past deadline.
Academic Integrity All work must be your own. Collaboration encouraged for concepts, not for code or answers. AI tool usage must be disclosed.
Last updated: January 2026