中正大學課程大綱
課程名稱(中文)： 深度學習於電腦視覺之應用 開課單位： 資訊工程研究所(Graduate Institute of Computer Science and Information Engineering)
課程名稱(英文) Applications of Deep Learning in Computer Vision 課程代碼 4105023_01
授課教師： 盧沛怡 學分數 3
必/選修 選修 開課年級 研究所
先修科目或先備能力：
深度學習概論
課程概述：
Computer vision aims to enable computers to understand and interpret visual
information like the human visual system. With the advancements in deep learning,
computer vision has witnessed generational progress. In recent years, various deep
learning methods tailored for different computer vision applications have been
introduced. This course introduces the most widely used deep learning techniques in
computer vision. Through exploring various visual tasks such as object detection,
image segmentation, etc., students will acquire skills in applying deep learning
techniques to computer vision.
學習目標：
1. The goal of this course is to provide students with foundational theoretical
2. knowledge and practical skills in applying deep learning techniques to computer
3. vision. Students will gain an in-depth understanding of the evolution of deep
4. learning, particularly its applications in the field of computer vision. The
5. course will focus on introducing widely applied deep learning technologies and
6. exploring their applications in different visual tasks, such as object detection
7. and image segmentation. Through the course, students will learn to apply deep
8. learning to address real-world challenges in computer vision.
教科書：
Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Computer Vision: Algorithms and Applications, 2nd Edition, Richard Szeliski
Dive into deep learning, A Zhang, ZC Lipton, M Li, AJ Smola

課程大綱 分配時數 核心能力 備註
單元主題 內容綱要 講授 示範 隨堂作業 其他
Introduction 3 12345678
Machine Learning 3 12345678
Conventional Computer Vision 3 12345678
Deep Neural Networks and Convolutional Neural Networks 3 12345678
Traditional Object detection methods 3 12345678
Two-stage Object detection: Faster R-CNN family 3 12345678
One-stage Object detection: YOLO family 3 12345678
Transformer-based detection: ViT, DETR 3 12345678
Paper Study (paper presentation, final project proposal) 3 12345678
Semantic Segmentation: FCN, SegNet, UNet 3 12345678
Semantic Segmentation: FCN, SegNet, UNet 3 12345678
Object Tracking: MDNet, CNN+LSTM 3 12345678
Object Tracking: GCN 3 12345678
Anomaly detection 3 12345678
Final Project Presentation 3 12345678
Final Project Presentation 3 12345678

教育目標
1.具獨立從事學術研究或產品創新研發之人才
2.具團隊合作精神及科技整合能力，並在團隊中扮演領導、規劃、管理之角色
3.具自我挑戰與終身學習能力之人才
4.具有學術倫理、工程倫理、國際觀之人才

核心能力
1.具有資訊工程與科學領域之專業知識(Competence in computer science and computer engineering.)
2.具有創新思考、問題解決、獨立研究之能力(Be creative and be able to solve problems and to perform independent research.)
3.具有撰寫中英文專業論文及簡報之能力(Demonstrate good written, oral, and communication skills, in both Chinese and English.)
4.具策劃及執行專題研究之能力(Be able to plan and execute projects.)
5.具有溝通、協調、整合及進行跨領域團隊合作之能力(Have communication, coordination, integration skills and teamwork in multi-disciplinary settings.)
6.具有終身學習與因應資訊科技快速變遷之能力(Recognize the need for, and have the ability to engage in independent and life-long learning.)
7.認識並遵循學術與工程倫理(Understand and commit to academic and professional ethics.)
8.具國際觀及科技前瞻視野(Have international view and vision of future technology.)

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教學要點概述：
1. 教材編選(可複選)：自編簡報(ppt)教科書作者提供
2. 教學方法(可複選)：講述板書講述
3. 評量工具(可複選)：上課點名 0%, 隨堂測驗0.00%, 隨堂作業0.00%, 程式實作0%, 實習報告0%,
                       專案報告0.00%, 期中考0%, 期末考0%, 期末報告0%, 其他0%,
4. 教學資源：課程網站 教材電子檔供下載 實習網站
5. 教學相關配合事項：

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