Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Fall Semester
Year 2026 366382 Advanced Computer Vision 1 3.0 3 ★ CHEN,YAN-ANN ECE 5 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	366382	Advanced Computer Vision	1	3.0	3	★	CHEN,YAN-ANN	ECE	5	0

Course Plan：

Instructor CHEN,YAN-ANN　Office Hours

Email chenya@ntut.edu.tw

Last Updated 2026-06-08 11:47:34

Course Objective This course takes a modular approach to explore core research directions in modern computer vision, covering visual recognition foundations, object detection and dense prediction, vision-language and generative models, representation learning and practical challenges, and real-world vision systems. Each module spans 2–3 weeks to allow students to genuinely engage with the problem context rather than survey topics at a surface level. The course is conducted entirely in English (EMI). Through lectures, guided paper reading, model experiments, and a final research project, students develop the ability to read papers, design experiments, and propose research directions relevant to ECE applications including edge computing, smart manufacturing, and AIoT perception.

Course Schedule Week Topic 1 Course Overview and Project Guidelines 2 CNN and Transfer Learning 3 Vision Transformers 4 Object Detection I: Two-stage Methods 5 Object Detection II: One-stage, DETR, Open-vocabulary 6 Dense Prediction I: Semantic and Instance Segmentation 7 Dense Prediction II: SAM and Open-vocabulary Segmentation 8 VLM I: CLIP and Image-Text Representation 9 Midterm Project Proposal 10 VLM II: Prompting, Multimodal Reasoning, VLM Applications 11 Generative Vision: Diffusion and Synthetic Data 12 Representation Learning and Self-supervised Learning 13 Practical Issues I: Data Imbalance, Small Data, Weak Supervision 14 Practical Issues II: Robustness, Occlusion, Domain Adaptation 15 Real-world Vision Systems: Edge, Manufacturing, Robot Vision 16 Project Progress Discussion / Technical Clinic 17 Final Project Presentation I 18 Final Project Presentation II + Course Wrap-up

Evaluation and grading policy Assignment 1: Classification / Transfer Learning 15% Assignment 2: Detection or Segmentation 15% Assignment 3: VLM / CLIP / Generative Vision 15% Midterm Project Proposal 15% Final Project (presentation + written report) 35% Participation 5%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
• Szeliski, Computer Vision: Algorithms and Applications, 2nd ed. (free PDF available at szeliski.org) • CS231n public lecture slides and programming assignments • Hugging Face, Ultralytics, and Anomalib official documentation • Paper PDFs for all papers provided by the instructor via the course platform

The access to curricular consultation Students are welcome to reach out via email for any questions related to course content, assignments, or the final project.

Expanding teaching and resources ● 數位科技融入 (Digital Technology Integration)
運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。
Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment.
● 數位教材資源應用 (Application of Digital Resources)
採用影音多媒體或具開放授權之資源（如OCW、開放教科書）作為主要或輔助教材。
Adopts multimedia or open-licensed resources (e.g., OCW, Open Textbooks) as primary or supplementary teaching materials.

The course corresponds to the SDGs SDG4：優質教育（Quality Education）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

Does the course incorporate AI ● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process）
● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）

Note
　

Instructor	CHEN,YAN-ANN　Office Hours
Email	chenya@ntut.edu.tw
Last Updated	2026-06-08 11:47:34
Course Objective	This course takes a modular approach to explore core research directions in modern computer vision, covering visual recognition foundations, object detection and dense prediction, vision-language and generative models, representation learning and practical challenges, and real-world vision systems. Each module spans 2–3 weeks to allow students to genuinely engage with the problem context rather than survey topics at a surface level. The course is conducted entirely in English (EMI). Through lectures, guided paper reading, model experiments, and a final research project, students develop the ability to read papers, design experiments, and propose research directions relevant to ECE applications including edge computing, smart manufacturing, and AIoT perception.
Course Schedule	Week Topic 1 Course Overview and Project Guidelines 2 CNN and Transfer Learning 3 Vision Transformers 4 Object Detection I: Two-stage Methods 5 Object Detection II: One-stage, DETR, Open-vocabulary 6 Dense Prediction I: Semantic and Instance Segmentation 7 Dense Prediction II: SAM and Open-vocabulary Segmentation 8 VLM I: CLIP and Image-Text Representation 9 Midterm Project Proposal 10 VLM II: Prompting, Multimodal Reasoning, VLM Applications 11 Generative Vision: Diffusion and Synthetic Data 12 Representation Learning and Self-supervised Learning 13 Practical Issues I: Data Imbalance, Small Data, Weak Supervision 14 Practical Issues II: Robustness, Occlusion, Domain Adaptation 15 Real-world Vision Systems: Edge, Manufacturing, Robot Vision 16 Project Progress Discussion / Technical Clinic 17 Final Project Presentation I 18 Final Project Presentation II + Course Wrap-up
Evaluation and grading policy	Assignment 1: Classification / Transfer Learning 15% Assignment 2: Detection or Segmentation 15% Assignment 3: VLM / CLIP / Generative Vision 15% Midterm Project Proposal 15% Final Project (presentation + written report) 35% Participation 5%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes • Szeliski, Computer Vision: Algorithms and Applications, 2nd ed. (free PDF available at szeliski.org) • CS231n public lecture slides and programming assignments • Hugging Face, Ultralytics, and Anomalib official documentation • Paper PDFs for all papers provided by the instructor via the course platform
The access to curricular consultation	Students are welcome to reach out via email for any questions related to course content, assignments, or the final project.
Expanding teaching and resources	● 數位科技融入 (Digital Technology Integration) 運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。 Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment. ● 數位教材資源應用 (Application of Digital Resources) 採用影音多媒體或具開放授權之資源（如OCW、開放教科書）作為主要或輔助教材。 Adopts multimedia or open-licensed resources (e.g., OCW, Open Textbooks) as primary or supplementary teaching materials.
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
Note