News
2023/09/08: We have email the permission code (>60) to those who are additionally selected to be registered. If you change your mind and do not need to register the course anymore, please notify the TA by the end of this week. If you are not able to register but would like to audit the course via NTU COOL, please send your request to the TA directly.
2023/08/01: For those who'd like to register the course but are not in, we will announce the registration policy at the 1st lecture on Sept. 5th.
2023/08/01: For those who'd like to register the course but are not in, we will announce the registration policy at the 1st lecture on Sept. 5th.
Introduction
Computer vision has become ubiquitous in our society, with a variety of applications in image/video search and understanding, medicine, drones, and self-driving cars. As the core to many of the above applications, visual analysis such as image classification, segmentation, localization and detection would be among the well-known problems in computer vision. Recent developments in neural networks (a.k.a. deep learning) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into the deep learning models, with particular focuses on understanding and designing such models for solving various computer vision tasks.
Goals
This course will expose students to cutting-edge research — starting from fundamentals of deep learning to its recent advances such as generative AI models. Each topic will begin with instructor lectures to present context and background material, followed by discussions and homework assignments, allowing the students to develop hand-on experiences on deep learning techniques for solving practical computer vision problems.
Syllabus
Week |
Date |
Topic |
Course Materials |
Remarks |
1 |
09/05 |
Course Logistics & Registration; Intro to Neural Nets |
||
2 |
09/12 |
Convolutional Neural Networks & Training Techniques |
||
3 |
09/19 |
Extensions of CNN & Self-Supervised Learning; Image Segmentation |
HW #1 out |
|
4 |
09/26 |
Generative Models (I) - AE, VAE & GAN |
||
5 |
10/03 |
Guest Lecture (Dr. Jun-Cheng Chen, Academia Sinica) |
ICCV week |
|
6 |
10/10 |
No Class |
Double Tenth Day; |
|
7 |
10/17 |
Generative Models (II) - GAN & Diffusion Model; Transfer Learning |
HW #1 due & HW #2 out |
|
8 |
10/24 |
Recurrent Neural Networks & Transformer |
||
9 |
10/31 |
Vision Transformer; Vision & Language (I) - Large Language Models |
||
10 |
11/07 |
Vision & Language (II) - Image Captioning & Visual Question Answering |
HW #2 due & HW #3 out |
|
11 |
11/14 |
Guest Lecture (TBA) |
CVPR week |
|
12 |
11/21 |
Multimodal Learning; Parameter-Efficient Finetuning |
||
13 |
11/28 |
3D Vision |
HW #3 due & HW #4 out Final Projects out |
|
14 |
12/05 |
Federated Learning & Advanced Topics |
||
15 |
12/12 |
TBA |
NeurIPS week HW #4 due |
|
17 |
12/28 Thur |
Final Project Presentation |
Contacts
Teaching Assistants
I-Jieh Liu
MK-514 TA Hours: Thu. 13.20 ~ 14.10 |
Zi-Ting Chou
MK-514 TA Hours: Thu. 13.20 ~ 14.10 |
Bin-Shih Wu
MK-514 TA Hours: Wed. 13.20 ~ 14.10 |
Jr-Jen Chen
MK-514 TA Hours: Fri. 11.20 ~ 12.10 |
Wei-Yuan Cheng
MK-514 TA Hours: Wed. 13.20 ~ 14.10 |
Yu-Chien Liao
MK-514 TA Hours: Fri. 11.20 ~ 12.10 |
Hsueh-Han Yang
MK-514 TA Hours: Mon. 13.20 ~ 14.10 |
Hsi-Che Lin
MK-514 TA Hours: Mon. 13.20 ~ 14.10 |