DLCV - VISION & LEARNING LAB

News

2025/09/05: Registration code has been sent to the waitlist student who are approved. If you prefer to audit the class, please contact TA.
2025/08/22: If you are NTU students and would like to register the class, please come to the class and add yourself to the waitlist on Sept. 3rd. The waitlist link will be provided then.

Introduction

This course examines the transformative impact of deep learning in computer vision. Students will establish a solid foundation while engaging with state-of-the-art techniques, ranging from convolutional neural networks (CNNs), Transformers, diffusion models, and 3D vision to multimodal large language models (MLLMs). The course emphasizes the design of model architectures, training methodologies, and real-world applications. Through hands-on projects and critical theoretical discussions, students will develop comprehensive expertise in designing, training, and optimizing deep learning models for complex visual tasks. Ultimately, the course prepares students for advanced research and professional careers in the rapidly evolving field of computer vision.

Goals

This course introduces students to cutting-edge research, beginning with the fundamentals of deep learning and extending to its latest advances in vision applications. Students are expected to master key concepts such as neural network architectures, training methodologies, and performance optimization strategies. A central component of the course is the final project, designed to foster critical thinking and problem-solving skills, while preparing students to contribute to frontier research or address real-world challenges. Active participation, completion of hands-on projects, and engagement in theoretical discussions will be essential for success in this course.

Syllabus

Week	Date	Topic	Course Materials	Remarks
1	09/03	Course Logistics; Intro to Neural Nets	Course-Info Week-1
2	09/10	Convolutional Neural Networks & Self-Supervised Learning	Week-2	HW #1 out
3	09/17	Image Segmentation & Object Detection	Week-3
4	09/24	Generative Models (I) - AE, VAE, & Diffusion Model (I)	Week-4	HW #1 due
5	10/01	Generative Models (II) - Diffusion Model (II), GAN	Week-5	HW #2 out
6	10/8	Recurrent Neural Networks & Transformer (I)	Week-6
7	10/15	Transformer (II); Vision & Language Models	Week-7
8	10/22	ICCV week		HW #2 due HW #3 out
9	10/29	Guest Lecture: ision Language Action Models (VLA) & Reasoning by Dr. Fu-En Yang, NVIDIA
10	11/05	Guest Lecture: Toward Efficient LLM Inference by Prof. Kai-Chiang Wu, NYCU Project Sponsor Introduction: PicCollage
11	11/12	Guest Lecture: 3D Vision by Dr. Sheng-Yu Huang, NTU/NVIDIA		HW #3 due HW #4 out
12	11/19	Guest Lecture: Linda Huang, Senior Dir., GeValyn Associates
13	11/26	Advanced Topics in DLCV; Guest Talk: Dr. Trista Chen, Microsoft		Final Project Announcement
14	12/03	Guest Lecture (TBD)		HW #4 due; NeurIPS week
15	12/10	Advanced topics in Foundation & World Models
16	12/22 Mon	Final Project Presentation		Sponsor: PicCollage