Slides

Reading Materials

Introduction

Basic Concepts and Model Evaluation

Linear Models

Fundamentals for Convex Optimization

1.  "Part I: Theory" of the book "S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge Press, 2004".

Support Vector Machines

1.  A. Kowalczyk, Support Vector Machines Succinctly, Syncfusion, 2017

2. Demos for SVM, https://github.com/csLinZhang/CVBook/tree/main/chapter-14-SVM

Neural Networks and CNN

1.   K. He et al., Deep Residual Learning for Image Recognition, CVPR, 2016

2.   G. Huang et al., Densely Connected Convolutional Networks, CVPR, 2017

3.   J. Redmon et al., Yolo: 9000 better, faster, stronger, CVPR, 2017

4.   N. Ma et al., ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design, ECCV, 2018

5.         J. Redmon et al., YOLOv3: An Incremental Improvement, arXiv, 2018

6.         Github for YOLOv4, https://github.com/AlexeyAB/darknet

7.         J.R. Terven et al., A comprehensive review of YOLO: From YOLOv1 to YOLOv8 and beyond, arXiv:2304.0050, 2023.

8.         Glenn Jocher et al., Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models, 2026

9.         典型卷积神经网络模型结构的演进

Visual Perception Practices in Autonomous Driving

1.  Xuan Shao, Lin Zhang* et al., "MOFIS_SLAM: A multi-object semantic SLAM system with front-view, inertial and surround-view sensors for indoor parking", IEEE Trans. Circuits and Systems for Video Technology, vol. 32, no. 7, pp. 4788-4803, 2022.

2.  Tianjun Zhang, Nlong Zhao, Ying Shen, Xuan Shao, Lin Zhang*, and Yicong Zhou, “ROECS: A Robust Semi-direct Pipeline Towards Online Extrinsics Correction of the Surround-view System”, in: Proc. ACM MM, pp. 3153-3161, 2021.

3.  Lin Zhang et al., "Vision-based parking-slot detection: A DCNN-based approach and a large-scale benchmark dataset", IEEE Trans. Image Processing, vol. 27, no. 11, pp. 5350-5364, 2018.

Learning-based 3D Scene Representation

1.        MILDENHALL B, SRINIVASAN P P, TANCIK M, et al. NeRF: Representing scenes as neural radiance fields for view synthesis, Proc. European Conf. Computer Vision, 2020: 405–421.

2.        MÜLLER T, EVANS A, SCHIED C, et al. Instant neural graphics primitives with a multiresolution hash encoding, ACM Trans. Graphics, 2022, 41(4): 102:1-15.

3.         KERBL B, KOPANAS G, LEIMKÜHLER T, et al. 3D Gaussian splatting for real-time radiance field rendering, ACM Trans. Graphics, 2023, 42(4): 139:1-14.