| PRE-REQUISITE | Prerequisites: UFUG 2601 OR UFUG 2602 |
|---|---|
| DESCRIPTION | This course aims to provide a solid understanding of modern Computer Vision. It starts with essential backgrounds in image processing and classical vision methods, then transitions to contemporary learning-based techniques. Students will master core architectures including CNNs, Transformers, and generative models like GANs and Diffusion. Advanced modules explore detection, segmentation, and learning-based 3D vision. The course emphasizes problem-solving through a practical mini-project, encouraging students to apply these algorithms to real-world needs such as biomedical analysis or AR/VR. Students will finish the course ready to conduct independent research and develop innovative vision solutions. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6424) | Tu 01:30PM - 03:20PM | Rm 147, E1 | CHEN, Yingcong | 48 | 48 | 0 | 0 | |
| LA01 (6426) | Tu 03:30PM - 04:20PM | Rm 228, E1 | CHEN, Yingcong | 48 | 48 | 0 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course covers popular topics in computer vision, which includes high-level tasks like image classification, object detection, image segmentation, and low-level tasks like image generation, image enhancement, image-to-image translation, etc. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6033) | Th 01:30PM - 04:20PM | Lecture Hall C | CHEN, Yingcong | 130 | 128 | 2 | 0 |