查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

115-1 366382 高等計算機視覺 1 3.0 3 ★ 陳彥安電子所 5 0 　

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
115-1	366382	高等計算機視覺	1	3.0	3	★	陳彥安	電子所	5	0

教學大綱與進度：

教師姓名陳彥安　教師諮商時間(Office Hours)

Email chenya@ntut.edu.tw

最後更新時間 2026-06-08 11:47:34

課程大綱 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.

課程進度 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

評量方式與標準 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%

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：是
• 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

課程諮詢管道 Students are welcome to reach out via email for any questions related to course content, assignments, or the final project.

延伸教學與資源 ● 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.

課程對應SDGs指標 SDG4：優質教育（Quality Education）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

課程是否導入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）

備註　

教師姓名	陳彥安　教師諮商時間(Office Hours)
Email	chenya@ntut.edu.tw
最後更新時間	2026-06-08 11:47:34
課程大綱	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.
課程進度	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
評量方式與標準	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%
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：是 • 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
課程諮詢管道	Students are welcome to reach out via email for any questions related to course content, assignments, or the final project.
延伸教學與資源	● 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.
課程對應SDGs指標	SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
課程是否導入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）
備註