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ReadMe🧐

Tiu MoAbout 15 min

ReadMe🧐

介绍

该文档项目为 Happy Learning 小组的科研入门资料。
ReadMe仅包含基础内容，可作为索引使用，详细内容请见对应文档。
项目地址: http://10.16.104.13:1805/happy-learning/tohappylearningopen in new window

小组老大

冷佳旭 https://faculty.cqupt.edu.cn/lengjiaxu/zh_CN/index.htmopen in new window

使用指南

平台使用指南请见 http://10.16.104.13:1805/root/users-guideopen in new window

此 README 文档仅作为索引, 主要内容为入门基础知识和各研究方向简介，详细笔记请见 1.科研方向 文件夹。

文档总览

[toc]

新生入门

  1. 首先学习 Andrew Ng 的 Machine Learning 在线课程，要做课后作业(MATLAB)；
  2. 其次学习 Andrew Ng 的 Deep Learning 在线课程，要做课后作业（Python+tensorflow），这个过程可能需要花3-5天学习python的基础语法；
  3. 学习CS231n在线课程，并完成大作业。
  4. 深度学习基础练习及经典模型 链接入口🔗, 点击进入🔗
  5. https://mofanpy.com/

科研基础

  1. 基础课程：本硕期间重点掌握 《高等数学》 《线性代数》 《概率论与数理统计》 《矩阵论》 《凸优化》 课程，这里的掌握是要理解它们的本质、作用及物理几何意义，而非应付考试。在学习时抛弃国内出版的死板教材，搜索国内外优秀书籍、公开课、视频进行学习。
    例如，《线性代数》推荐《线性代数的本质》系列视频open in new windowhttps://www.bilibili.com/video/BV1ys411472Eopen in new window，以及Gilbert Strang 的麻省理工大学公开课视频https://www.bilibili.com/video/BV1zx411g7gqopen in new window。
  2. 机器学习：推荐南京大学周志华老师的西瓜书《机器学习》，华为诺亚方舟实验室主任李航老师的《统计学习方法》，斯坦福大学Andrew NG（吴恩达）老师的机器学习课程视频https://www.bilibili.com/video/BV164411b7dxopen in new window。
  3. 深度学习：推荐Google研究科学家Ian Goodfellow（Generative Adversarial Nets提出者）的花书《Deep Learning》（有中文译本人民邮电出版社），斯坦福大学李飞飞（ImageNet的提出者）老师的cs231n计算机视觉课程https://www.bilibili.com/video/BV1nJ411z7feopen in new window。
  4. 科研环境：操作系统Linux，主要编程语言Python，主要深度学习框架Pytorch。因此，要掌握Linux的使用方法与常用命令，熟练使用Python语言编程，熟练使用Pytorch深度学习框架搭建网络与深度学习算法。
    a. Linux 学习：https://www.runoob.com/linux/linux-tutorial.htmlopen in new window；
    b. Python 学习：https://www.runoob.com/python3/python3-tutorial.htmlopen in new window；
    c. Python 第三方库 Pillow（处理图像）学习：https://pillow-cn.readthedocs.io/zh_CN/latest/open in new window；
    d. Python 第三方库 Numpy（CPU处理多维张量）学习：https://www.runoob.com/numpy/numpy-tutorial.htmlopen in new window；
    e. 深度学习框架Pytorch（使用Python编程，GPU处理多维张量，提供深度学习API）学习：https://pytorch.apachecn.org/docs/1.4/open in new window。

科研工具

  1. 科学上网：一个可用稳定的上谷歌（翻墙）工具是必须的，推荐：https://geckoiplc.org/register?aff=nxkYWh33open in new window，使用方法该网站有教程
  2. 检索论文：GoogleScholar：https://scholar.google.com/open in new window（需翻墙）
  3. 下载论文：
    a. IEEE：https://ieeexplore.ieee.org/Xplore/home.jspopen in new window；
    b. ScienceDirect：https://www.sciencedirect.com/open in new window；
    c. SpringerLink：https://link.springer.com/open in new window；
    d. arXiv：https://arxiv.org/open in new window占坑网站，最新的文章会发表在上面占坑，但是良莠不齐，免费下载，翻墙会快；
    e. Sci-Hub：https://www.sci-hub.ren/open in new window不在学校内又想下载IEEE等版权收费文章？Sci-Hub是神器，网站宣言...to remove all the barriers in the way of science，地址可能会变，因为一直被告。
  4. 管理论文：Mendely：https://www.mendeley.com/open in new window；Endnotes：https://www.endnote.com/open in new window；Zotero：https://www.zotero.org/open in new window；自己选择。
  5. 撰写论文：LaTex环境安装：http://tug.org/texlive/open in new window；按着网站指导来。也可以使用在线的LaTex编辑工具如Overleaf：https://www.overleaf.com/loginopen in new window。
  6. 论文作图：PPT、Visio(Windows)、OmniGraffle(Mac)； 直接保存成PDF就是矢量图，使用Adobe Acrobat将图片多余部分裁剪。
  7. 代码编辑：VSCode（Visual Studio Code）：https://code.visualstudio.com/open in new window； 你想要的代码编辑功能都能通过VSCode+插件实现。 常用VSCode插件：LaTex Workshop（用VSCode写论文，前提得先装好LaTex环境）、Remote-SSH（用VSCode直接浏览编辑远程服务器上的代码和图片）、One Dark Pro（让VSCode更好看）、Bracket Pair Colorizer 2（让代码每一级括号带不同的颜色，括号再也不会不成对报错了）、Code Spell Checker（检查你的拼写是否有错误）、indent-rainbow（不同级的缩进带不同颜色，对Python来说很有用）、Python（可以直接在VSCode调试Python程序，前提得先装好Python环境）。
  8. 服务器后台运行管理工具tmux 链接入口🔗, 点击进入🔗。

科研方向

✧ 行人检测（Person Detection）-> 高峰 (博士)

论文汇总：论文汇总链接，例如GitHub上的 Awesome 系列

➢ 推荐综述

[1] From Handcrafted to Deep Features for Pedestrian Detection: A Survey (TPAMI2021) 涵盖了2020年及以前从传统到深度学习的行人检测所有知识。但是，2020Detr横空出世,2021突飞猛进，2022基于Detr思想的第一篇行人检测顶会问世,个人觉得这是里程碑式的作品。因为自Faster-RCNN问世以来，行人检测领域百分之80以上的文章都是Faster-RCNN的变种。未来可能Detr替代Faster-RCNN，成为行人检测领域的Baseline。目前DETR相关资料非常少，这部分知识需要自己想办法。

➢ 优秀团队 / 学术大佬

■ 【旷视团队】
[1] Detection in Crowded Scenes: One Proposal, Multiple Predictions. CVPR2020
[2] End-to-End Object Detection with Fully Convolutional Network. CVPR2021
[3] Progressive End-to-End Object Detection in Crowded Scenes. CVPR2022
■ 【Peize Sun】【香港大学在读博士】
[1] Sparse r-cnn: End-to-end object detection with learnable proposals. CVPR2021
[2] What Makes for End-to-End Object Detection? ICML2021

➢ 经典项目

○ 上述五篇论文都开源了代码，地址在论文中。第一篇论文开源的代码中包含了纯pytorch实现的Faster-RCNN和Retinanet非常适合初学者入门基于CNN的目标检测算法。
○ 我上传了一个简易版的DETR纯pytorch实现在我的科研方向文件夹中，可以很好的学习DETR原理。
○ 旷视团队的第三篇论文是Peize Sun第一篇论文的改进，通过阅读代码可以从一个角度了解目前研究者改进Detr算法的思路。

✧ 行人重识别（Person Re-identification）-> 汪海涛

论文汇总: https://github.com/bismex/Awesome-person-re-identificationopen in new window，该 repo 内有目前 video reid 方向的顶会所有论文汇总，包括基本分类、 常用数据库下载、常用 code

➢ 推荐综述

[1] Deep Learning for Person Re-identification: A Survey and Outlook, TPAMI 2020(近期的 reid 工作汇总)
[2] Person Re-identification: Past, Present and Future, arXiv 2016(早期的 reid 工作汇总)

  • 基本看上上面两个 survey 对目前 reid 的发展脉络就可以有一个清晰的把握了
  • 看论文的时候 related work 里面的论文都可以参考看一看
  • 一些必备的技术点:bagtricks, agw, sbs, pcb, mgn, arcface
➢ 优秀团队 / 学术大佬

■ 郑伟诗 Wei-Shi Zheng 中山大学智能科学与系统实验室(iSEE) https://www.isee-ai.cn/~zhwshi/open in new window
[1] J. Yang, W. -S. Zheng, Q. Yang, Y. -C. Chen and Q. Tian, "Spatial-Temporal Graph Convolutional Network for Video-Based Person Re-Identification," 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 3286-3296, doi: 10.1109/CVPR42600.2020.00335.
[2] J. Yang et al., "Learning to Know Where to See: A Visibility-Aware Approach for Occluded Person Re-identification," 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 11865-11874, doi: 10.1109/ICCV48922.2021.01167.

■ 常虹 Chang Hong 中国科学院计算机技术研究院(CAS) https://people.ucas.ac.cn/~changhongopen in new window
[1] R. Hou, B. Ma, H. Chang, X. Gu, S. Shan and X. Chen, "VRSTC: Occlusion-Free Video Person Re-Identification," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 7176-7185, doi: 10.1109/CVPR.2019.00735.
[2] R. Hou, H. Chang, B. Ma, R. Huang and S. Shan, "BiCnet-TKS: Learning Efficient Spatial-Temporal Representation for Video Person Re-Identification," 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 2014-2023, doi: 10.1109/CVPR46437.2021.00205.

➢ 可见光 reid 代表论文

[1] Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification, ECCV2020
[2] Identity-Guided Human Semantic Parsing for Person Re-Identification, ECCV2020
[3] Faster Person Re-Identification, ECCV20 (轻量级 reid， https://github.com/wangguanan/light-reidopen in new window )
[4] Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-Identification, ECCV2020
[5] Re-Ranking Person Re-Identification With k-Reciprocal Encoding， CVPR2017(将 reranking 应用到 reid，涨点显著)
[6] Person Re-Identification in the Wild, CVPR2017(从检测到识别)

➢ 异质 reid 代表论文

[1] Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification，CVPR2020
[2] Cross-Modality Person Re-Identification With Shared-Specific Feature Transfer, CVPR2020
[3] RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment, ICCV2019
[4] Infrared-Visible Cross-Modal Person Re-Identification with an X Modality, AAAI2020

➢ 常用开源项目

✧ 超分辨率重建（Super-resolution）

论文汇总：论文汇总链接，例如GitHub上的 Awesome 系列

➢ 推荐综述

[1] Video Super Resolution Based on Deep Learning: A comprehensive survey
[2] Deep Learning for Image Super-resolution: A Survey
[3] Blind Image Super-Resolution: A Survey and Beyond

  • 三篇综述分别代表超分领域中的三个小领域：视频超分辨率、单图超分辨率、盲图像超分辨率。
➢ 优秀团队 / 学术大佬

■ Xintao Wang:现任腾讯ARC实验室（深圳）研究员。毕业于香港中文大学多媒体实验室。主要研究图像和视频的恢复。
个人主页：https://xinntao.github.io/
[1] Wang X, Chan K C K, Yu K, et al. Edvr: Video restoration with enhanced deformable convolutional networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2019: 0-0.
[2] Wang X, Yu K, Dong C, et al. Recovering realistic texture in image super-resolution by deep spatial feature transform[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 606-615.
[3] Wang X, Yu K, Wu S, et al. Esrgan: Enhanced super-resolution generative adversarial networks[C]//Proceedings of the European conference on computer vision (ECCV) workshops. 2018: 0-0.
■ Kelvin C.K. Chan：新加坡南洋理工大学，计算机科学与工程学院，香港中文大学学士学位。
个人主页：https://ckkelvinchan.github.io/
[1] Chan K C K, Wang X, Yu K, et al. BasicVSR: The search for essential components in video super-resolution and beyond[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 4947-4956.
[2] Chan K C K, Zhou S, Xu X, et al. BasicVSR++: Improving video super-resolution with enhanced propagation and alignment[J]. arXiv preprint arXiv:2104.13371, 2021.
[3] Chan K C K, Wang X, Yu K, et al. Understanding deformable alignment in video super-resolution[J]. arXiv preprint arXiv:2009.07265, 2020, 4(3): 4.

➢ 经典论文：（推荐加“👍”）

  • Video Super-Resolution
    [1] Ward C M, Harguess J, Crabb B, et al. Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)[C]//Applications of Digital Image Processing XL. International Society for Optics and Photonics, 2017, 10396: 1039605.
    简介：该篇论文是深度学习在超分辨率的开山之作。每个入门SR的学者都需要从这一篇论文开始。
    [2] Liu D, Wang Z, Fan Y, et al. Robust video super-resolution with learned temporal dynamics[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 2507-2515.
    [3] Tian Y, Zhang Y, Fu Y, et al. Tdan: Temporally-deformable alignment network for video super-resolution[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 3360-3369.
    [4] 👍【Wang X, Chan K C K, Yu K, et al. Edvr: Video restoration with enhanced deformable convolutional networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2019: 0-0.
    [5] Haris M, Shakhnarovich G, Ukita N. Recurrent back-projection network for video super-resolution[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 3897-3906.
    [6] Chan K C K, Wang X, Yu K, et al. BasicVSR: The search for essential components in video super-resolution and beyond[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 4947-4956.
    [7] Chan K C K, Wang X, Yu K, et al. Understanding deformable alignment in video super-resolution[J]. arXiv preprint arXiv:2009.07265, 2020, 4(3): 4.

  • Blind Image Super-Resolution [1] Zhang K, Zuo W, Zhang L. Learning a single convolutional super-resolution network for multiple degradations[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 3262-3271.
    [2] Gu J, Lu H, Zuo W, et al. Blind super-resolution with iterative kernel correction[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 1604-1613.
    [3] Wei Y, Gu S, Li Y, et al. Unsupervised real-world image super resolution via domain-distance aware training[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 13385-13394.
    [4] Shocher A, Cohen N, Irani M. “zero-shot” super-resolution using deep internal learning[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 3118-3126.
    [5] Hui Z, Li J, Wang X, et al. Learning the Non-differentiable Optimization for Blind Super-Resolution[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 2093-2102.

➢ 经典开源项目

○ 【名称】【链接】【简介】

[1] BasicVSR: https://github.com/xinntao/BasicSR
简介：该项目里包含了很多经典论文的复现，因此可直接将这个开源代码用会就可。

✧ 视频异常检测 (Video Anomaly Detection) -> 谭明圮

论文汇总：

[1] https://github.com/fjchange/awesome-video-anomaly-detection 该 repo 内有目前 视频异常检测（VAD） 方向的优秀论文汇总，包括基本分类、 常用数据库下载、 开源code、 综述
[2] https://github.com/shot1107/anomaly_detection_papers 该repo 内有异常检测每年顶会的论文，包括但不限于视频异常检测，可参考借鉴。

➢ 认识异常检测
1. 简单介绍（从异常行为检测--> 视频异常行为检测）

[1] 异常行为检测简介： https://mp.weixin.qq.com/s/UmT0DjFqRPsjv2m28ySvdw
[2] 基于深度学习的异常行为检测介绍：https://mp.weixin.qq.com/s/Aghbz4m1eWFCNGgEy8q6Cg
[3] 基于深度学习的异常行为检测研究现状： https://mp.weixin.qq.com/s/MwpELRlC1cuDgqn4staAzA
[4] 基于深度学习的视频异常行为事件检测简介: https://mp.weixin.qq.com/s/i3Xw2-ivARnF7rBSFtxugw
[5] 基于视频的异常行为检测算法介绍: https://mp.weixin.qq.com/s/Dxsc3oCuO0wYkeFubMfSNw

2.论文综述：

[1] 邬开俊等. 视频异常检测技术研究进展[J]. 计算机科学与探索, 2022 （中文综述，但没有那么全面，可以有一个初步了解）
[2] Bharathkumar Ramachandra et al. A survey of single-scene video anomaly detection (TPAMI 2020)

➢ 优秀团队 / 学术大佬
■ 高盛华 上海科技大学（视觉与数据智能中心）

[1] A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework (ICCV 2017) -->proposed Shanghaitech dataset.
[2] Future Frame Prediction for Anomaly Detection – A New Baseline (CVPR 2018)
[3] Future Frame Prediction for Anomaly Detection (TPAMI 2022)

■ Radu Ionescu SecurifAI/University of Bucharest

[1] Detecting abnormal events in video using Narrowed Normality Clusters (WACV 2019)
[2] Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video (CVPR 2019)
[3] Anomaly Detection in Video via Self-Supervised and Multi-Task Learning (CVPR 2021)
[4] A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video (TPAMI 2021)
[5] UBnormal New Benchmark for Supervised Open-Set Video Anomaly Detection (CVPR 2022) [6] Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection (CVPR 2022)

➢ 经典论文：（推荐加“👍”）
■ Unsupervised VAD

  • Conference Papers
    [1] Learning Temporal Regularity in Video Sequences (CVPR 2016)
    [2] A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework -->Proposed Shanghaitech dataset.
    [2] 👍Future Frame Prediction for Anomaly Detection -- A New Baseline (CVPR 2018)
    [3] 👍Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (ICCV 2019) --> The first to employ memory module on video anomaly detection
    [4] 👍Object-Centric Auto-Encoders and Dummy Anomalies for Abnormal Event Detection (CVPR 2019) --> The first to combine object detection and vad to achieve object-level anomaly dtection.
    [5] AnoPCN: Video Anomaly Detection via Deep Predictive Coding Network (ACM MM 2019) --> The first hybrid model
    [6] 👍Learning Memory-guided Normality for Anomaly Detection (CVPR 2020) --> Based on MemAE
    [7] Cluster Attention Contrast for Video Anomaly Detection (ACM MM 2020) --> The first to apply Contrastive Learninig
    [8] 👍Anomaly Detection in Video via Self-Supervised and Multi-Task Learning (CVPR 2021) --> object-level
    [9] 👍A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction (ICCV 2021) --> Hybrid model
    [10] Anomaly Detection in Video Sequence with Appearance-Motion Correspondence (ICCV 2019) --> Two stream network
    [11] Video Anomaly Detection and Localization via Gaussian Mixture Fully Convolutional Variational Autoencoder --> Two stream network
    [12] Self-supervised Sparse Representation for Video Anomaly Detection (ECCV 2022) --> A first attempt to slove unsupervised and weakly supervised VAD [13] Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles (ECCV 2022)

  • Joural Papers
    [1] Video Anomaly Detection with Sparse Coding Inspired Deep Neural Networks (TPAMI 2021)
    [2] A Background-Agnostic Framework With Adversarial Training for Abnormal Event Detection in Video (TPAMI 2022)
    [3] Influence-Aware Attention Networks for Anomaly Detection in Surveillance Videos (TCSVT 2022)
    [4] Bidirectional Spatio-Temporal Feature Learning With Multiscale Evaluation for Video Anomaly Detection (TCSVT 2022)
    [5] Anomaly Detection With Bidirectional Consistency in Videos (TNNLS 2022)
    [6] Variational Abnormal Behavior Detection With Motion Consistency (TIP 2022)
    [7] DoTA: Unsupervised Detection of Traffic Anomaly in Driving Videos (TPAMI 2023)
    [8] A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos (TCSVT 2023)
    [9] Learnable Locality-Sensitive Hashing for Video Anomaly Detection (TCSVT 2023)
    [10] A Kalman Variational Autoencoder Model Assisted by Odometric Clustering for Video Frame Prediction and Anomaly Detection (TIP 2023)
    [11] Abnormal Event Detection and Localization via Adversarial Event Prediction (TNNLS 2023)

■ Weakly supervised VAD

[1] 👍 Real-world Anomaly Detection in Surveillance Videos (CVPR 2018)
[2] Weakly Supervised Video Anomaly Detection via Center-Guided Discrimative Learning (ICME 2020)

[3] Decouple and Resolve: Transformer-Based Models for Online Anomaly Detection From Weakly Labeled Videos (TIFS 2023)

➢ 经典项目

○ MNAD --> https://github.com/cvlab-yonsei/MNAD 可作为baseline.

➢ 发现的新的有意思的研究方向--> Explainable Anomaly Detection (EAD) 可解释性异常检测
1. DEFINITION

The aim of this TASK is to detect and automatically generate high-level explanations of anomalous events in video. Understanding the cause of an anomalous event is crucialas the required response is dependant on its nature andseverity. --> Anomaly Detection & Anoamly Explanation

2. RELATED WORK

[1] Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge (ICCV 2017)
[2] X-MAN: Explaining multiple sources of anomalies in video (CVPR workshop 2021)
[3] Discrete neural representations for explainable anomaly detection (WACV 2022)

✧ 航拍图像目标检测（Drone-view Object Detection）-> 莫梦竟成

论文汇总：论文汇总链接，例如GitHub上的 Awesome 系列

➢ 推荐综述

[1] 【发表于】【年份】【论文名】【团队名】【论文链接】【代码链接】【简介】
[2] 【知乎/微信/博客等网页链接】【内容简介】

➢ 优秀团队 / 学术大佬

■ 【团队名】【团队链接】【所属机构】【细化方向】
[1] 【代表论文】
■ 【大佬名】【个人主页】【所属机构】【细化方向】
[1] 【代表论文】

➢ 经典论文：（推荐加“👍”）

[1] 【发表于】【年份】【论文名】【团队名】【论文链接】【代码链接】【简介】
[2] 👍【发表于】【年份】【论文名】【团队名】【论文链接】【代码链接】【简介】

➢ 经典项目

○ 【名称】【链接】【简介】

✧ 小样本目标检测（Few-shot Object Detection）-> 陈泰岳

论文汇总：论文汇总链接，例如GitHub上的 Awesome 系列

➢ 推荐综述

[1] 【发表于】【年份】【论文名】【团队名】【论文链接】【代码链接】【简介】
[2] 【知乎/微信/博客等网页链接】【内容简介】

➢ 优秀团队 / 学术大佬

■ 【团队名】【团队链接】【所属机构】【细化方向】
[1] 【代表论文】
■ 【大佬名】【个人主页】【所属机构】【细化方向】
[1] 【代表论文】

➢ 经典论文：（推荐加“👍”）

[1] 【发表于】【年份】【论文名】【团队名】【论文链接】【代码链接】【简介】
[2] 👍【发表于】【年份】【论文名】【团队名】【论文链接】【代码链接】【简介】

➢ 经典项目

○ 【名称】【链接】【简介】

参考文档：

  1. 【在iiplab做科研】 https://docs.qq.com/pdf/DR3NNa2xqU0Rld1B2open in new window，特别感谢～
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