DLCV - VISION & LEARNING LAB

News

2022/09/08: We've taken addition ~100 students, and the registration code has been sent out to those who are admitted. If you are not added in and would like to audit the online course, please send the request to TA (with your NTU email address).

2022/09/03: For those who'd like to register the course, we will announce the registration policy at the 1st lecture on Sept. 6th.

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 details of deep learning architectures, with a particular focus on understanding and designing learnable models for solving various vision tasks.

Goals

This course will expose students to cutting-edge research — starting from a refresher in basics of machine learning, computer vision, neural networks, to recent developments. 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	09/06	Course logistics & registration; Machine Learning 101	week 1-1 week 1-2
2	09/13	Introduction to Convolutional Neural Networks (I)	week 2
3	09/20	Introduction to Convolutional Neural Networks (II) Tutorials on Python, Github, etc. (by TAs)	week 3	HW #1 out
4	09/27	Object Detection & Segmentation; Generative Model	week 4
5	10/04	Generative Adversarial Networks, and Diffusion Model		HW #1 due
6	10/11	Transfer Learning for Visual Classification & Synthesis		HW #2 out
7	10/18	Guest Lecture (TBD)		ICIP week (?)
8	10/25	Recurrent Neural Networks		HW #2 due
9	11/01	Transformer; Vision & Language (I)		HW #3 out
10	11/08	Vision & Language (II); Few-Shot Learning (I)		CVPR due
11	11/15	N/A		HW #3 due; No class (校慶)
12	11/22	Few-Shot Learning (II); Self-Supervised Learning		HW #4 out
13	11/29	Guest Lecture (TBD)		NeurIPS week (?)
14	12/06	3D Vision
15	12/13	Federated Learning		HW #4 due
17	12/30 Fri	Presentation for Final Projects