[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-amusi--awesome-ai-awesomeness":3,"tool-amusi--awesome-ai-awesomeness":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",157379,2,"2026-04-15T23:32:42",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",108322,"2026-04-10T11:39:34",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":32,"last_commit_at":59,"category_tags":60,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":73,"owner_avatar_url":74,"owner_bio":75,"owner_company":76,"owner_location":77,"owner_email":78,"owner_twitter":79,"owner_website":80,"owner_url":81,"languages":79,"stars":82,"forks":83,"last_commit_at":84,"license":79,"difficulty_score":85,"env_os":86,"env_gpu":87,"env_ram":87,"env_deps":88,"category_tags":91,"github_topics":92,"view_count":32,"oss_zip_url":79,"oss_zip_packed_at":79,"status":17,"created_at":100,"updated_at":101,"faqs":102,"releases":103},7986,"amusi\u002Fawesome-ai-awesomeness","awesome-ai-awesomeness","A curated list of awesome awesomeness about artificial intelligence","awesome-ai-awesomeness 是一份精心整理的人工智能领域资源清单，旨在为从业者提供一站式的知识导航。面对 AI 技术迭代快、细分领域多、学习资源分散的痛点，它将海量信息系统化地归类为机器学习、深度学习、计算机视觉、自然语言处理、语音识别等核心板块，并涵盖编程框架、数据集、职业发展到前沿论文解读等全方位内容。\n\n无论是刚入门的学生、寻求灵感的开发者，还是深耕算法的研究人员，都能在这里快速找到高质量的学习路径和实用工具。其独特亮点在于不仅收录了通用的基础资源，还深入覆盖了可解释性 AI、对抗性机器学习、量子机器学习、3D 学习以及移动端部署等前沿与垂直方向，甚至包含了针对特定任务（如图像分类、目标检测）的专项论文汇总。通过这份清单，用户可以高效规避信息筛选的时间成本，直接触达社区公认的优秀项目与研究进展，是探索人工智能世界不可或缺的实用指南。","# Awesome AI Awesomeness\n\nA curated list of awesome awesomeness about artificial intelligence(AI).\n\nIf you want to contribute to this list (please do), send me a pull request.\n\n# Table of Contents\n\n\u003C!-- MarkdownTOC depth=4 -->\n\n- [Artificial Intelligence(AI)](#AI)\n- [Machine Learning(ML)](#ML)\n- [Deep Learning(DL)](#DL)\n- [Computer Vision(CV)](#CV)\n- [Natural Language Processing(NLP)](#NLP)\n- [Speech Recognition](#SR)\n- [Other Research Topics](#ORT)\n- [Programming Languages](#PL)\n- [Framework](#Framework)\n- [Datasets](#Datasets)\n- [AI Career](#AI Career)\n\n\u003Ca name=\"AI\">\u003C\u002Fa>\n\n# Artificial Intelligence(AI)\n\n- [AI](https:\u002F\u002Fgithub.com\u002Fowainlewis\u002Fawesome-artificial-intelligence)\n- [AI-Use-Cases](https:\u002F\u002Fgithub.com\u002Ffaktionai\u002Fawesome-ai-usecases)\n- [AI residency programs information](https:\u002F\u002Fgithub.com\u002Fankitshah009\u002Fall-about-ai-residency)\n- [Explainable AI](https:\u002F\u002Fgithub.com\u002Fwangyongjie-ntu\u002FAwesome-explainable-AI)\n\n\u003Ca name=\"ML\">\u003C\u002Fa>\n\n# Machine Learning(ML)\n\n- [ML](https:\u002F\u002Fgithub.com\u002Fjosephmisiti\u002Fawesome-machine-learning)\n- [ML-Source-Code](https:\u002F\u002Fgithub.com\u002Fsrc-d\u002Fawesome-machine-learning-on-source-code)\n- [ML-CN](https:\u002F\u002Fgithub.com\u002Fjobbole\u002Fawesome-machine-learning-cn)\n- [Adversarial-ML](https:\u002F\u002Fgithub.com\u002Fyenchenlin\u002Fawesome-adversarial-machine-learning)\n- [Quantum-ML](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002Fawesome-quantum-machine-learning)\n- [3D-Machine-Learning](https:\u002F\u002Fgithub.com\u002Ftimzhang642\u002F3D-Machine-Learning)\n- [Machine Learning Interpretability](https:\u002F\u002Fgithub.com\u002Fjphall663\u002Fawesome-machine-learning-interpretability)\n- [Machine Learning System](https:\u002F\u002Fgithub.com\u002FHuaizhengZhang\u002FAwesome-System-for-Machine-Learning)\n- [Mobile Machine Learning](https:\u002F\u002Fgithub.com\u002Ffritzlabs\u002FAwesome-Mobile-Machine-Learning)\n- [Machine Learning Problems](https:\u002F\u002Fgithub.com\u002FRedditSota\u002Fstate-of-the-art-result-for-machine-learning-problems)\n- [Gradient Boosting](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-gradient-boosting-papers)\n- [Decision Tree](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-decision-tree-papers)\n\n\u003Ca name=\"DL\">\u003C\u002Fa>\n\n# Deep Learning(DL)\n\n- [DL](https:\u002F\u002Fgithub.com\u002FChristosChristofidis\u002Fawesome-deep-learning)\n- [DL-Papers](https:\u002F\u002Fgithub.com\u002Fterryum\u002Fawesome-deep-learning-papers)\n- [DL-Resources](https:\u002F\u002Fgithub.com\u002Fguillaume-chevalier\u002FAwesome-Deep-Learning-Resources)\n- [DeepLearning-500-questions](https:\u002F\u002Fgithub.com\u002Fscutan90\u002FDeepLearning-500-questions)\n- [Deep-Learning-in-Production](https:\u002F\u002Fgithub.com\u002Fahkarami\u002FDeep-Learning-in-Production)\n- [DNN Compression and Acceleration](https:\u002F\u002Fgithub.com\u002FMingSun-Tse\u002FEfficientDNNs)\n- [Architecture Search](https:\u002F\u002Fgithub.com\u002Fmarkdtw\u002Fawesome-architecture-search)\n- [Deep Learning for Graphs](https:\u002F\u002Fgithub.com\u002FDeepGraphLearning\u002FLiteratureDL4Graph)\n- [Real-time Network](https:\u002F\u002Fgithub.com\u002Fwpf535236337\u002Freal-time-network)\n- [Deep Learning Interpretability](https:\u002F\u002Fgithub.com\u002FoneTaken\u002Fawesome_deep_learning_interpretability)\n- [Graph-based Deep Learning](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature)\n\n\u003Ca name=\"CV\">\u003C\u002Fa>\n\n# Computer Vision(CV)\n\n- [CV](https:\u002F\u002Fgithub.com\u002Fjbhuang0604\u002Fawesome-computer-vision)\n- [CV2](https:\u002F\u002Fgithub.com\u002Fkjw0612\u002Fawesome-deep-vision)\n- [CV-People](Awesome-People-in-Computer-Vision)\n- [DeepFakes](https:\u002F\u002Fgithub.com\u002Fdatamllab\u002Fawesome-deepfakes-materials)\n- [Event-based Vision Resources](https:\u002F\u002Fgithub.com\u002Fuzh-rpg\u002Fevent-based_vision_resources)\n- [Embodied Vision](https:\u002F\u002Fgithub.com\u002FChanganVR\u002Fawesome-embodied-vision)\n- Research Topics\n    - [Action Recognition](https:\u002F\u002Fgithub.com\u002Fjinwchoi\u002Fawesome-action-recognition)\n    - [Colorization](https:\u002F\u002Fgithub.com\u002FMarkMoHR\u002FAwesome-Image-Colorization)\n    - [Image Classification](https:\u002F\u002Fgithub.com\u002Fweiaicunzai\u002Fawesome-image-classification)\n        - [imgclsmob](https:\u002F\u002Fgithub.com\u002Fosmr\u002Fimgclsmob)\n    - [Image Registration](https:\u002F\u002Fgithub.com\u002FAwesome-Image-Registration-Organization\u002Fawesome-image-registration)\n    - Object Detection\n        - [amusi\u002FObject Detection](https:\u002F\u002Fgithub.com\u002Famusi\u002Fawesome-object-detection)\n        - [hoya012\u002FObject Detection]( https:\u002F\u002Fgithub.com\u002Fhoya012\u002Fdeep_learning_object_detection )\n        - [Tiny Object Detection](https:\u002F\u002Fgithub.com\u002Fkuanhungchen\u002Fawesome-tiny-object-detection)\n        - [Small Object Detection](https:\u002F\u002Fgithub.com\u002Ftjtum-chenlab\u002FSmallObjectDetectionList)\n        - [Video Object Detection](https:\u002F\u002Fgithub.com\u002Fhuanglianghua\u002Fvideo-detection-paper-list)\n        - [Anchor Free Object Detection](https:\u002F\u002Fgithub.com\u002FXinZhangNLPR\u002Fawesome-anchor-free-object-detection)\n    - Face\n        - [Face Detection & Recognition](https:\u002F\u002Fgithub.com\u002FChanChiChoi\u002Fawesome-Face_Recognition)\n        - [awesome-face](https:\u002F\u002Fgithub.com\u002FpolarisZhao\u002Fawesome-face)\n        - [Facial Expression Recognition (FER)](https:\u002F\u002Fgithub.com\u002FEvelynFan\u002FAWESOME-FER)\n        - [Face Landmark Detection](https:\u002F\u002Fgithub.com\u002Fmrgloom\u002FFace-landmarks-detection-benchmark)\n        - [Landmark Detection](https:\u002F\u002Fgithub.com\u002FD-X-Y\u002Flandmark-detection)\n        - [Face Anti-spoofing](https:\u002F\u002Fgithub.com\u002FZitongYu\u002FDeepFAS)\n    - [Gaze Estimation](https:\u002F\u002Fgithub.com\u002FWuZhuoran\u002Fawesome-gaze)\n    - [HDR Image Synthesis](https:\u002F\u002Fgithub.com\u002Fvinthony\u002Fawesome-deep-hdr)\n    - Image Segmentation\n        - [Semantic Segmentation](https:\u002F\u002Fgithub.com\u002Fmrgloom\u002Fawesome-semantic-segmentation)\n        - [Segmentation.X](https:\u002F\u002Fgithub.com\u002FwutianyiRosun\u002FSegmentation.X)\n        - [Panoptic Segmentation](https:\u002F\u002Fgithub.com\u002FAngzz\u002Fawesome-panoptic-segmentation)\n        - [Weakly Supervised Semantic Segmentation](https:\u002F\u002Fgithub.com\u002FYimingCuiCuiCui\u002Fawesome-weakly-supervised-segmentation)\n        - [Referring Image Segmentation](https:\u002F\u002Fgithub.com\u002FMarkMoHR\u002FAwesome-Referring-Image-Segmentation)\n        - [Semi Supervised Learning for Medical Image Segmentation](https:\u002F\u002Fgithub.com\u002FHiLab-git\u002FSSL4MIS)\n    - [Object Tracking](https:\u002F\u002Fgithub.com\u002Ffoolwood\u002Fbenchmark_results)\n        - [Visual Tracking1](https:\u002F\u002Fgithub.com\u002Ffoolwood\u002Fbenchmark_results)\n        - [Visual Tracking2](https:\u002F\u002Fgithub.com\u002Fczla\u002Fdaily-paper-visual-tracking)\n        - [Multi-Object Tracking](https:\u002F\u002Fgithub.com\u002FSpyderXu\u002Fmulti-object-tracking-paper-list)\n        - [Tracking and Detection](https:\u002F\u002Fgithub.com\u002Fabhineet123\u002FDeep-Learning-for-Tracking-and-Detection)\n        - [daily-paper-visual-tracking](https:\u002F\u002Fgithub.com\u002Fczla\u002Fdaily-paper-visual-tracking)\n        - [Multimodal Tracking](https:\u002F\u002Fgithub.com\u002Fzhang-pengyu\u002FMultimodal-Tracking-Survey)\n    - [Pose estimation](https:\u002F\u002Fgithub.com\u002FwjbKimberly\u002Fpose_estimation_CVPR_ECCV_2018)\n        - [Object Pose Estimation](https:\u002F\u002Fgithub.com\u002FYoungXIAO13\u002FObjectPoseEstimationSummary)\n        - Human Pose estimation\n          - [Human Pose estimation 1](https:\u002F\u002Fgithub.com\u002Fcbsudux\u002Fawesome-human-pose-estimation)\n          - [Human Pose estimation 2](https:\u002F\u002Fgithub.com\u002Fwangzheallen\u002Fawesome-human-pose-estimation)\n        - [Hand Pose estimation](https:\u002F\u002Fgithub.com\u002Fxinghaochen\u002Fawesome-hand-pose-estimation)\n        - [6D Object Pose Estimation](https:\u002F\u002Fgithub.com\u002FZhongqunZHANG\u002Fawesome-6d-object)\n    - [Human Motion](https:\u002F\u002Fgithub.com\u002Fderikon\u002Fawesome-human-motion)\n    - [Human-Object Interaction(HOI)](https:\u002F\u002Fgithub.com\u002FDirtyHarryLYL\u002FHOI-Learning-List)\n    - [Long-tailed](https:\u002F\u002Fgithub.com\u002Fzwzhang121\u002FAwesome-of-Long-Tailed-Recognition)\n    - Scene Text\n        - [Scene Text Localization and Recognition](https:\u002F\u002Fgithub.com\u002Fchongyangtao\u002FAwesome-Scene-Text-Recognition)\n        - [Scene Text Localization & Recognition Resources](https:\u002F\u002Fgithub.com\u002Fwhitelok\u002Fimage-text-localization-recognition)\n        - [Scene Text Detection and Recognition](https:\u002F\u002Fgithub.com\u002FJyouhou\u002FSceneTextPapers)\n        - [Text Detection and Recognition](https:\u002F\u002Fgithub.com\u002Fhwalsuklee\u002Fawesome-deep-text-detection-recognition)\n        - [Scene Text Recognition Resources](https:\u002F\u002Fgithub.com\u002FHCIILAB\u002FScene-Text-Recognition)\n    - Super Resolution\n        - [Super Resolution (ChaofWang)](https:\u002F\u002Fgithub.com\u002FChaofWang\u002FAwesome-Super-Resolution)\n        - [Super Resolution (ptkin)](\u003Chttps:\u002F\u002Fgithub.com\u002Fptkin\u002FAwesome-Super-Resolution>)\n        - [Image Super Resolution](https:\u002F\u002Fgithub.com\u002FYapengTian\u002FSingle-Image-Super-Resolution)\n        - [Video Super Resolution](https:\u002F\u002Fgithub.com\u002FLoSealL\u002FVideoSuperResolution)\n    - 3D\n        - [3D Reconstruction](https:\u002F\u002Fgithub.com\u002FopenMVG\u002Fawesome_3DReconstruction_list)\n        - [3D Human Body](https:\u002F\u002Fgithub.com\u002F3DFaceBody\u002Fawesome-3dbody-papers)\n    - [OCR](https:\u002F\u002Fgithub.com\u002Fkba\u002Fawesome-ocr)\n    - Re-ID\n        - [Person Re-ID(1)](https:\u002F\u002Fgithub.com\u002Fbismex\u002FAwesome-person-re-identification)\n        - [Person Re-ID(2)](https:\u002F\u002Fgithub.com\u002FFDU-VTS\u002FAwesome-Person-Re-Identification)\n        - [Vehicle Re-ID(1)](https:\u002F\u002Fgithub.com\u002Flayumi\u002FVehicle_reID-Collection)\n        - [Vehicle Re-ID(2)](https:\u002F\u002Fgithub.com\u002Fknwng\u002Fawesome-vehicle-re-identification)\n    - [Pedestrian Attribute Recognition](https:\u002F\u002Fgithub.com\u002Fwangxiao5791509\u002FPedestrian-Attribute-Recognition-Paper-List)\n    - [Person Search](https:\u002F\u002Fgithub.com\u002Fwangxiao5791509\u002FPerson-Search-Paper-List)\n    - [Image Captioning](https:\u002F\u002Fgithub.com\u002Fzhjohnchan\u002Fawesome-image-captioning)\n    - [Question Answering](https:\u002F\u002Fgithub.com\u002Fdapurv5\u002Fawesome-question-answering)\n    - [Crowd Counting](https:\u002F\u002Fgithub.com\u002Fgjy3035\u002FAwesome-Crowd-Counting)\n    - [Lane Detection](https:\u002F\u002Fgithub.com\u002Famusi\u002Fawesome-lane-detection)\n    - [Low Lignt Enhancement](https:\u002F\u002Fgithub.com\u002FElin24\u002FAwesome-Low-Lignt-Enhancement) \n    - Image Retrieval\n        - [Awesome image retrieval papers (1)](https:\u002F\u002Fgithub.com\u002Fwillard-yuan\u002Fawesome-cbir-papers)\n        - [Awesome image retrieval papers (2)](https:\u002F\u002Fgithub.com\u002Flgbwust\u002Fawesome-image-retrieval-papers)\n    - [Medical Imaging](https:\u002F\u002Fgithub.com\u002Ffepegar\u002Fawesome-medical-imaging)\n        - [Medical Data](https:\u002F\u002Fgithub.com\u002Fbeamandrew\u002Fmedical-data)\n        - [Medical imaging datasets](https:\u002F\u002Fgithub.com\u002Fsfikas\u002Fmedical-imaging-datasets)\n        - [Awesome GAN for Medical Imaging](https:\u002F\u002Fgithub.com\u002Fxinario\u002Fawesome-gan-for-medical-imaging)\n        - [Deep Learning for Medical Applications](https:\u002F\u002Fgithub.com\u002Falbarqouni\u002FDeep-Learning-for-Medical-Applications)\n        - [Medical Image Segmentation](https:\u002F\u002Fgithub.com\u002FJunMa11\u002FSOTA-MedSeg) \n    - [Image Inpainting](https:\u002F\u002Fgithub.com\u002F1900zyh\u002FAwesome-Image-Inpainting)\n    - Image\u002FVideo Dehazing\n        - [awesome-dehazing](https:\u002F\u002Fgithub.com\u002Fyoungguncho\u002Fawesome-dehazing)\n        - [DehazeZoo](https:\u002F\u002Fgithub.com\u002Fcxtalk\u002FDehazeZoo)\n    - Image Denoising\n        - [reproducible-image-denoising-state-of-the-art](https:\u002F\u002Fgithub.com\u002Fwenbihan\u002Freproducible-image-denoising-state-of-the-art)\n        - [Image-Denoising-State-of-the-art](https:\u002F\u002Fgithub.com\u002Fflyywh\u002FImage-Denoising-State-of-the-art)\n        - [Image and Video Denoising](https:\u002F\u002Fgithub.com\u002Fz-bingo\u002Fawesome-image-denoising-state-of-the-art)\n        - [Awesome-Denoise](https:\u002F\u002Fgithub.com\u002FoneTaken\u002FAwesome-Denoise)\n    - [Image Deraining](https:\u002F\u002Fgithub.com\u002FnnUyi\u002FDerainZoo)\n    - [Image\u002FVideo Deblurring]( https:\u002F\u002Fgithub.com\u002Fsubeeshvasu\u002FAwesome-Deblurring )\n    - Image to Image(img2img)\n        - [lzhbrian\u002FImage to Image](https:\u002F\u002Fgithub.com\u002Flzhbrian\u002Fimage-to-image-papers)\n        - [xiaweihao\u002FImage to Image](https:\u002F\u002Fgithub.com\u002Fxiaweihao\u002Fawesome-image-translation)\n    - [Multi-View Stereo(MVS)](https:\u002F\u002Fgithub.com\u002FXYZ-qiyh\u002FAwesome-Learning-MVS)\n    - [Underwater Image Enhancement](https:\u002F\u002Fgithub.com\u002Fcxtalk\u002FAwesome-Underwater-Image-Enhancement)\n    - [Video Analysis](https:\u002F\u002Fgithub.com\u002FHuaizhengZhang\u002FAwsome-Deep-Learning-for-Video-Analysis)\n    - [Video Object Segmentation(VOS)](https:\u002F\u002Fgithub.com\u002Fdu0915\u002FVideo-Object-Segmentation-Paper-List)\n    - [Edge Detection](\u003Chttps:\u002F\u002Fgithub.com\u002FMarkMoHR\u002FAwesome-Edge-Detection-Papers>)\n    - [Local and Global Descriptor](https:\u002F\u002Fgithub.com\u002Fshamangary\u002Fawesome-local-global-descriptor)\n    - [Referring Image Segmentation](https:\u002F\u002Fgithub.com\u002FMarkMoHR\u002FAwesome-Referring-Image-Segmentation)\n    - Salience\n        - [Salient Object Detection(SOD)](https:\u002F\u002Fgithub.com\u002Fjiwei0921\u002FSOD-CNNs-based-code-summary-)\n        - [Saliency Detection & Segmentation](https:\u002F\u002Fgithub.com\u002Flartpang\u002Fawesome-segmentation-saliency-dataset#another-awesome-dataset-list)\n        - [RGB-D Salient Object Detection](https:\u002F\u002Fgithub.com\u002Ftaozh2017\u002FRGBD-SODsurvey)\n    - [Self-Supervised Learning in Medical Imaging](https:\u002F\u002Fgithub.com\u002FSaeedShurrab\u002Fawesome-self-supervised-learning-in-medical-imaging)\n    - [Fashion + AI](https:\u002F\u002Fgithub.com\u002Flzhbrian\u002FCool-Fashion-Papers)\n    - [Event-based Vision Resources](https:\u002F\u002Fgithub.com\u002Fuzh-rpg\u002Fevent-based_vision_resources)\n    - [Video Stabilization](https:\u002F\u002Fgithub.com\u002Fyaochih\u002Fawesome-video-stabilization)\n    - Visual Transformer\n        - [Transformer-in-Vision](https:\u002F\u002Fgithub.com\u002FDirtyHarryLYL\u002FTransformer-in-Vision)\n        - [Awesome Transformer for Vision Resources List](https:\u002F\u002Fgithub.com\u002Flijiaman\u002Fawesome-transformer-for-vision)\n        - [Awesome-Visual-Transformer](https:\u002F\u002Fgithub.com\u002Fdk-liang\u002FAwesome-Visual-Transformer)\n        - [Awesome Visual Representation Learning with Transformers](https:\u002F\u002Fgithub.com\u002Falohays\u002Fawesome-visual-representation-learning-with-transformers)\n\n\u003Ca name=\"NLP\">\u003C\u002Fa>\n\n# Natural Language Processing(NLP)\n\n- [NLP](https:\u002F\u002Fgithub.com\u002Fkeon\u002Fawesome-nlp)\n- [NLP-progress](https:\u002F\u002Fgithub.com\u002Fsebastianruder\u002FNLP-progress)\n- [CoreNLP](https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002FCoreNLP)\n- [NLPIR](https:\u002F\u002Fgithub.com\u002FNLPIR-team\u002FNLPIR)\n- [nlp_course](https:\u002F\u002Fgithub.com\u002Fyandexdataschool\u002Fnlp_course)\n- [nlp-datasets](https:\u002F\u002Fgithub.com\u002Fniderhoff\u002Fnlp-datasets)\n- [nlp-reading-group](https:\u002F\u002Fgithub.com\u002Fclulab\u002Fnlp-reading-group)\n- [NLP Paper](https:\u002F\u002Fgithub.com\u002Fchangwookjun\u002Fnlp-paper)\n- [Awesome-Chinese-NLP](https:\u002F\u002Fgithub.com\u002Fcrownpku\u002FAwesome-Chinese-NLP): 中文自然语言处理相关资料\n- [awesome-dl4nlp](https:\u002F\u002Fgithub.com\u002Fbrianspiering\u002Fawesome-dl4nlp)\n- [awesome-sentence-embedding](https:\u002F\u002Fgithub.com\u002FSeparius\u002Fawesome-sentence-embedding)\n- Research Topics\n  - [Machine Translation](https:\u002F\u002Fgithub.com\u002FZNLP\u002FSOTA-MT) \n\n\u003Ca name=\"SR\">\u003C\u002Fa>\n\n# Speech Recognition\n\n- [speech_recognition](https:\u002F\u002Fgithub.com\u002FUberi\u002Fspeech_recognition)\n- [awesome-speech-recognition-speech-synthesis-papers](https:\u002F\u002Fgithub.com\u002Fzzw922cn\u002Fawesome-speech-recognition-speech-synthesis-papers)\n\n\u003Ca name=\"ORT\">\u003C\u002Fa>\n\n# Other Research Topics\n\n- Bayesian\n     - [Bayesian](https:\u002F\u002Fgithub.com\u002Fdimenwarper\u002Fawesome-bayes)\n     - [Deep Bayesian](https:\u002F\u002Fgithub.com\u002Fotokonoko8\u002Fdeep-Bayesian-nonparametrics-papers)\n- [Capsule Networks](https:\u002F\u002Fgithub.com\u002Fsekwiatkowski\u002Fawesome-capsule-networks)\n- [Contrastive-Learning](https:\u002F\u002Fgithub.com\u002FHobbitLong\u002FPyContrast\u002Fblob\u002Fmaster\u002FAWESOME_CONTRASTIVE_LEARNING.md)\n- [Data Augmentation](https:\u002F\u002Fgithub.com\u002FAgaMiko\u002Fdata-augmentation-review)\n- [Emebedded AI](https:\u002F\u002Fgithub.com\u002Fysh329\u002Fawesome-embedded-ai)\n- GAN(Generative Adversarial Networks)\n  - [GAN-Case-Study](https:\u002F\u002Fgithub.com\u002Fpshams55\u002FGAN-Case-Study)\n  - [really-awesome-gan](https:\u002F\u002Fgithub.com\u002Fnightrome\u002Freally-awesome-gan)\n  - [AdversarialNetsPapers](https:\u002F\u002Fgithub.com\u002Fzhangqianhui\u002FAdversarialNetsPapers)\n  - [the-gan-zoo](https:\u002F\u002Fgithub.com\u002Fhindupuravinash\u002Fthe-gan-zoo)\n  - [Keras-GAN](https:\u002F\u002Fgithub.com\u002Feriklindernoren\u002FKeras-GAN)\n  - [gans-awesome-applications](https:\u002F\u002Fgithub.com\u002Fnashory\u002Fgans-awesome-applications): Curated list of awesome GAN applications and demo\n  - [image-to-image translation](https:\u002F\u002Fgithub.com\u002Fweihaox\u002Fawesome-image-translation)\n  - [GAN Inversion](https:\u002F\u002Fgithub.com\u002Fweihaox\u002Fawesome-image-translation\u002Fblob\u002Fmaster\u002Fawesome-gan-inversion.md)\n- [Graph Neural Networks(GNN)](https:\u002F\u002Fgithub.com\u002Fthunlp\u002FGNNPapers)\n- [Semi-Supervised Learning](https:\u002F\u002Fgithub.com\u002Fyassouali\u002Fawesome-semi-supervised-learning)\n- [SLAM](https:\u002F\u002Fgithub.com\u002Fkanster\u002Fawesome-slam)\n  - [SLAM List](https:\u002F\u002Fgithub.com\u002FOpenSLAM\u002Fawesome-SLAM-list)\n  - [VSLAM](https:\u002F\u002Fgithub.com\u002Ftzutalin\u002Fawesome-visual-slam)\n  - [SLAM(Chinese)](https:\u002F\u002Fgithub.com\u002FYiChenCityU\u002FRecent_SLAM_Research)\n  - [SLAM Datasets](https:\u002F\u002Fgithub.com\u002Fyoungguncho\u002Fawesome-slam-datasets)\n  - [SFM-Visual-SLAM](https:\u002F\u002Fgithub.com\u002Fmarknabil\u002FSFM-Visual-SLAM)\n  - [SLAM Resources](https:\u002F\u002Fgithub.com\u002Fckddls1321\u002FSLAM_Resources)\n  - [wuxiaolang](https:\u002F\u002Fgithub.com\u002Fwuxiaolang)\u002F[Visual_SLAM_Related_Research](https:\u002F\u002Fgithub.com\u002Fwuxiaolang\u002FVisual_SLAM_Related_Research)\n- [Reinforcement Learning](https:\u002F\u002Fgithub.com\u002Faikorea\u002Fawesome-rl)\n     - [Implementation of Reinforcement Learning Algorithms](https:\u002F\u002Fgithub.com\u002Fdennybritz\u002Freinforcement-learning)\n     - [Reinforcement Learning Chinese](https:\u002F\u002Fgithub.com\u002FwwxFromTju\u002Fawesome-reinforcement-learning-zh)：中文整理的强化学习资料\n- [Transfer Learning](https:\u002F\u002Fgithub.com\u002Fjindongwang\u002Ftransferlearning)\n- [Trajectory Prediction](https:\u002F\u002Fgithub.com\u002Fjiachenli94\u002FAwesome-Interaction-aware-Trajectory-Prediction)\n- [Zero-Shot Learning](https:\u002F\u002Fgithub.com\u002Fchichilicious\u002Fawesome-zero-shot-learning)\n- Few-Shot Learning\n     - [Duan-JM\u002FFew-Shot Learning](https:\u002F\u002Fgithub.com\u002FDuan-JM\u002Fawesome-papers-fewshot)\n     - [e-271\u002FFew-Shot Learning](https:\u002F\u002Fgithub.com\u002Fe-271\u002Fawesome-few-shot-learning)\n- Federated Learning\n     - [poga\u002FFederated Learning](https:\u002F\u002Fgithub.com\u002Fpoga\u002Fawesome-federated-learning)\n     - [Federated Computing\u002FLearning](https:\u002F\u002Fgithub.com\u002Ftushar-semwal\u002Fawesome-federated-computing)\n     - [ChanChiChoi\u002FFederated Learning](ChaoChiChoi\u002FFederated Learning)\n- Meta-Learning\n     - [Meta-Learning1](https:\u002F\u002Fgithub.com\u002Fdragen1860\u002Fawesome-meta-learning)\n     - [Meta-Learning2](https:\u002F\u002Fgithub.com\u002Fsudharsan13296\u002FAwesome-Meta-Learning)\n- [Open Set Recognition](https:\u002F\u002Fgithub.com\u002FiCGY96\u002Fawesome_OpenSetRecognition_list)\n- Self-Supervised\n     - [jason718\u002FSelf-Supervised](https:\u002F\u002Fgithub.com\u002Fjason718\u002Fawesome-self-supervised-learning)\n     - [Sungman-Cho\u002FSelf-Supervised]( https:\u002F\u002Fgithub.com\u002FSungman-Cho\u002FAwesome-Self-Supervised-Papers )\n     - [wvangansbeke\u002FSelf-Supervised-Learning-Overview](wvangansbeke\u002FSelf-Supervised-Learning-Overview)\n- [Graph Classification](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-graph-classification)\n- [Incremental Learning](https:\u002F\u002Fgithub.com\u002Fxialeiliu\u002FAwesome-Incremental-Learning)\n- [AutoML](https:\u002F\u002Fgithub.com\u002Fhibayesian\u002Fawesome-automl-papers)\n     - [AutoML Survey](https:\u002F\u002Fgithub.com\u002FDataSystemsGroupUT\u002FAutoML_Survey)\n     - [AutoML-and-Lightweight-Models](https:\u002F\u002Fgithub.com\u002Fguan-yuan\u002Fawesome-AutoML-and-Lightweight-Models)\n     - [NAS](https:\u002F\u002Fgithub.com\u002FD-X-Y\u002FAwesome-NAS)\n     - [Architecture Search](https:\u002F\u002Fgithub.com\u002Fmarkdtw\u002Fawesome-architecture-search)\n     - [LITERATURE ON NEURAL ARCHITECTURE SEARCH](https:\u002F\u002Fwww.automl.org\u002Fautoml\u002Fliterature-on-neural-architecture-search\u002F)\n- [Model Compression](https:\u002F\u002Fgithub.com\u002Fcedrickchee\u002Fawesome-ml-model-compression)\n     - [EfficientDNNs](https:\u002F\u002Fgithub.com\u002FMingSun-Tse\u002FEfficientDNNs)\n     - [Model Compression and Acceleration](https:\u002F\u002Fgithub.com\u002Fmemoiry\u002FAwesome-model-compression-and-acceleration)\n     - [Neural Network Pruning](https:\u002F\u002Fgithub.com\u002Fhe-y\u002FAwesome-Pruning)\n- [Binary Neural Networks](https:\u002F\u002Fgithub.com\u002Fmichaeltinsley\u002Fawesome-binary-neural-networks) \n-  [Multimodal Research](https:\u002F\u002Fgithub.com\u002FEurus-Holmes\u002FAwesome-Multimodal-Research)\n-  [Multimodal Machine Learning](https:\u002F\u002Fgithub.com\u002Fpliang279\u002Fawesome-multimodal-ml)\n- [Neural Rendering](https:\u002F\u002Fgithub.com\u002Fxiaweihao\u002Fawesome-neural-rendering)\n- [NeRF](https:\u002F\u002Fgithub.com\u002Fyenchenlin\u002Fawesome-NeRF)\n- [Domain Adaptation](https:\u002F\u002Fgithub.com\u002Fzhaoxin94\u002Fawsome-domain-adaptation)\n- [Robotics](https:\u002F\u002Fgithub.com\u002Fkiloreux\u002Fawesome-robotics)\n- [Recommender Systems](https:\u002F\u002Fgithub.com\u002Frobi56\u002FDeep-Learning-for-Recommendation-Systems)\n- Autonomous Vehicles\n     - [Autonomous Vehicles](https:\u002F\u002Fgithub.com\u002Ftakeitallsource\u002Fawesome-autonomous-vehicles)\n     - [Autonomous Vehicles-CH]( https:\u002F\u002Fgithub.com\u002FDeepTecher\u002Fawesome-autonomous-vehicle )\n     - [Lidar Point cloud processing for Autonomous Driving](https:\u002F\u002Fgithub.com\u002Fbeedotkiran\u002FLidar_For_AD_references)\n- [Anomaly Detection](https:\u002F\u002Fgithub.com\u002Fyzhao062\u002Fanomaly-detection-resources)\n- [Yochengliu\u002FPoint Cloud Analysis](https:\u002F\u002Fgithub.com\u002FYochengliu\u002Fawesome-point-cloud-analysis)\n- [NUAAXQ\u002FPoint Cloud Analysis](https:\u002F\u002Fgithub.com\u002FNUAAXQ\u002Fawesome-point-cloud-analysis-2020)\n- [3D Point Clouds](https:\u002F\u002Fgithub.com\u002FQingyongHu\u002FSoTA-Point-Cloud)\n- [Affective_Computing](https:\u002F\u002Fgithub.com\u002Fsuzana-ilic\u002FDeep_Learning_Affective_Computing)\n- Knowledge Distillation\n     - [Knowledge Distillation(dkozlov)](https:\u002F\u002Fgithub.com\u002Fdkozlov\u002Fawesome-knowledge-distillation)\n     - [Knowledge Distillation(FLHonker)](https:\u002F\u002Fgithub.com\u002FFLHonker\u002FAwesome-Knowledge-Distillation)\n- [Click-Through Rate Prediction](https:\u002F\u002Fgithub.com\u002Fshenweichen\u002FDeepCTR)\n- [Label Noise](https:\u002F\u002Fgithub.com\u002Fsubeeshvasu\u002FAwesome-Learning-with-Label-Noise)\n- [VAE](https:\u002F\u002Fgithub.com\u002Fmatthewvowels1\u002FAwesome-VAEs)\n- [Imbalanced Learning](https:\u002F\u002Fgithub.com\u002FZhiningLiu1998\u002Fawesome-imbalanced-learning)\n\n\u003Ca name=\"PL\">\u003C\u002Fa>\n\n# Programming Languages\n\n- [C](https:\u002F\u002Fnotabug.org\u002Fkoz.ross\u002Fawesome-c)\n- [C++](https:\u002F\u002Fgithub.com\u002Ffffaraz\u002Fawesome-cpp)\n- [Python](https:\u002F\u002Fgithub.com\u002Fvinta\u002Fawesome-python)\n- [JAVA](https:\u002F\u002Fgithub.com\u002Fakullpp\u002Fawesome-java)\n- [JavaScript](awesome-javascript)\n- [Julia](https:\u002F\u002Fgithub.com\u002Fsvaksha\u002FJulia.jl)\n- [MATLAB](https:\u002F\u002Fgithub.com\u002Fuhub\u002Fawesome-matlab)\n- [R](https:\u002F\u002Fgithub.com\u002Fqinwf\u002Fawesome-R)\n\n\u003Ca name=\"Framework\">\u003C\u002Fa>\n# Framework\n\n- [TensorFlow](https:\u002F\u002Fgithub.com\u002Fjtoy\u002Fawesome-tensorflow)\n  - [TensorFlow From Zero To- One](https:\u002F\u002Fgithub.com\u002Famusi\u002FTensorFlow-From-Zero-To-One)\n  - [TensorFlow Lite](https:\u002F\u002Fgithub.com\u002Fmargaretmz\u002Fawesome-tflite)\n- [PyTorch](https:\u002F\u002Fgithub.com\u002Fbharathgs\u002FAwesome-pytorch-list)\n  - [PyTorch From Zero To- One](https:\u002F\u002Fgithub.com\u002Famusi\u002FPyTorch-From-Zero-To-One)\n- [Keras](https:\u002F\u002Fgithub.com\u002Ffchollet\u002Fkeras-resources)\n- [MXNet](https:\u002F\u002Fgithub.com\u002Fchinakook\u002FAwesome-MXNet)\n- [Caffe](https:\u002F\u002Fgithub.com\u002FMichaelXin\u002FAwesome-Caffe)\n- [Torch](https:\u002F\u002Fgithub.com\u002Fcarpedm20\u002Fawesome-torch)\n- [Chainer](awesome-chainer)\n\n\u003Ca name=\"Datasets\">\u003C\u002Fa>\n\n# Datasets\n\n- [Segmentation & Saliency detection](https:\u002F\u002Fgithub.com\u002Flartpang\u002Fawesome-segmentation-saliency-dataset)\n\n# AI Career\n\n- [Awesome Software Engineering for Machine Learning](https:\u002F\u002Fgithub.com\u002FSE-ML\u002Fawesome-seml)","# 令人惊叹的AI精彩内容\n\n一份精心整理的人工智能（AI）相关精彩内容清单。\n\n如果你希望为这份清单贡献力量（请务必这样做），可以向我发送拉取请求。\n\n# 目录\n\n\u003C!-- MarkdownTOC depth=4 -->\n\n- [人工智能（AI）](#AI)\n- [机器学习（ML）](#ML)\n- [深度学习（DL）](#DL)\n- [计算机视觉（CV）](#CV)\n- [自然语言处理（NLP）](#NLP)\n- [语音识别](#SR)\n- [其他研究主题](#ORT)\n- [编程语言](#PL)\n- [框架](#Framework)\n- [数据集](#Datasets)\n- [AI职业发展](#AI Career)\n\n\u003Ca name=\"AI\">\u003C\u002Fa>\n\n# 人工智能（AI）\n\n- [AI](https:\u002F\u002Fgithub.com\u002Fowainlewis\u002Fawesome-artificial-intelligence)\n- [AI应用场景](https:\u002F\u002Fgithub.com\u002Ffaktionai\u002Fawesome-ai-usecases)\n- [AI驻留项目信息](https:\u002F\u002Fgithub.com\u002Fankitshah009\u002Fall-about-ai-residency)\n- [可解释AI](https:\u002F\u002Fgithub.com\u002Fwangyongjie-ntu\u002FAwesome-explainable-AI)\n\n\u003Ca name=\"ML\">\u003C\u002Fa>\n\n# 机器学习（ML）\n\n- [ML](https:\u002F\u002Fgithub.com\u002Fjosephmisiti\u002Fawesome-machine-learning)\n- [ML源代码](https:\u002F\u002Fgithub.com\u002Fsrc-d\u002Fawesome-machine-learning-on-source-code)\n- [ML中文](https:\u002F\u002Fgithub.com\u002Fjobbole\u002Fawesome-machine-learning-cn)\n- [对抗性ML](https:\u002F\u002Fgithub.com\u002Fyenchenlin\u002Fawesome-adversarial-machine-learning)\n- [量子ML](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002Fawesome-quantum-machine-learning)\n- [3D机器学习](https:\u002F\u002Fgithub.com\u002Ftimzhang642\u002F3D-Machine-Learning)\n- [机器学习可解释性](https:\u002F\u002Fgithub.com\u002Fjphall663\u002Fawesome-machine-learning-interpretability)\n- [机器学习系统](https:\u002F\u002Fgithub.com\u002FHuaizhengZhang\u002FAwesome-System-for-Machine-Learning)\n- [移动机器学习](https:\u002F\u002Fgithub.com\u002Ffritzlabs\u002FAwesome-Mobile-Machine-Learning)\n- [机器学习问题](https:\u002F\u002Fgithub.com\u002FRedditSota\u002Fstate-of-the-art-result-for-machine-learning-problems)\n- [梯度提升](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-gradient-boosting-papers)\n- [决策树](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-decision-tree-papers)\n\n\u003Ca name=\"DL\">\u003C\u002Fa>\n\n# 深度学习（DL）\n\n- [DL](https:\u002F\u002Fgithub.com\u002FChristosChristofidis\u002Fawesome-deep-learning)\n- [DL论文](https:\u002F\u002Fgithub.com\u002Fterryum\u002Fawesome-deep-learning-papers)\n- [DL资源](https:\u002F\u002Fgithub.com\u002Fguillaume-chevalier\u002FAwesome-Deep-Learning-Resources)\n- [深度学习500题](https:\u002F\u002Fgithub.com\u002Fscutan90\u002FDeepLearning-500-questions)\n- [生产环境中的深度学习](https:\u002F\u002Fgithub.com\u002Fahkarami\u002FDeep-Learning-in-Production)\n- [DNN压缩与加速](https:\u002F\u002Fgithub.com\u002FMingSun-Tse\u002FEfficientDNNs)\n- [架构搜索](https:\u002F\u002Fgithub.com\u002Fmarkdtw\u002Fawesome-architecture-search)\n- [图神经网络深度学习](https:\u002F\u002Fgithub.com\u002FDeepGraphLearning\u002FLiteratureDL4Graph)\n- [实时网络](https:\u002F\u002Fgithub.com\u002Fwpf535236337\u002Freal-time-network)\n- [深度学习可解释性](https:\u002F\u002Fgithub.com\u002FoneTaken\u002Fawesome_deep_learning_interpretability)\n- [基于图的深度学习文献](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature)\n\n\u003Ca name=\"CV\">\u003C\u002Fa>\n\n# 计算机视觉（CV）\n\n- [计算机视觉](https:\u002F\u002Fgithub.com\u002Fjbhuang0604\u002Fawesome-computer-vision)\n- [深度视觉](https:\u002F\u002Fgithub.com\u002Fkjw0612\u002Fawesome-deep-vision)\n- [计算机视觉领域人物](Awesome-People-in-Computer-Vision)\n- [DeepFakes](https:\u002F\u002Fgithub.com\u002Fdatamllab\u002Fawesome-deepfakes-materials)\n- [事件驱动视觉资源](https:\u002F\u002Fgithub.com\u002Fuzh-rpg\u002Fevent-based_vision_resources)\n- [具身视觉](https:\u002F\u002Fgithub.com\u002FChanganVR\u002Fawesome-embodied-vision)\n- 研究主题\n    - [动作识别](https:\u002F\u002Fgithub.com\u002Fjinwchoi\u002Fawesome-action-recognition)\n    - [图像上色](https:\u002F\u002Fgithub.com\u002FMarkMoHR\u002FAwesome-Image-Colorization)\n    - [图像分类](https:\u002F\u002Fgithub.com\u002Fweiaicunzai\u002Fawesome-image-classification)\n        - [imgclsmob](https:\u002F\u002Fgithub.com\u002Fosmr\u002Fimgclsmob)\n    - [图像配准](https:\u002F\u002Fgithub.com\u002FAwesome-Image-Registration-Organization\u002Fawesome-image-registration)\n    - 目标检测\n        - [amusi\u002FObject Detection](https:\u002F\u002Fgithub.com\u002Famusi\u002Fawesome-object-detection)\n        - [hoya012\u002FObject Detection]( https:\u002F\u002Fgithub.com\u002Fhoya012\u002Fdeep_learning_object_detection )\n        - [小型目标检测](https:\u002F\u002Fgithub.com\u002Fkuanhungchen\u002Fawesome-tiny-object-detection)\n        - [小目标检测](https:\u002F\u002Fgithub.com\u002Ftjtum-chenlab\u002FSmallObjectDetectionList)\n        - [视频目标检测](https:\u002F\u002Fgithub.com\u002Fhuanglianghua\u002Fvideo-detection-paper-list)\n        - [无锚框目标检测](https:\u002F\u002Fgithub.com\u002FXinZhangNLPR\u002Fawesome-anchor-free-object-detection)\n    - 人脸\n        - [人脸检测与识别](https:\u002F\u002Fgithub.com\u002FChanChiChoi\u002Fawesome-Face_Recognition)\n        - [awesome-face](https:\u002F\u002Fgithub.com\u002FpolarisZhao\u002Fawesome-face)\n        - [面部表情识别(FER)](https:\u002F\u002Fgithub.com\u002FEvelynFan\u002FAWESOME-FER)\n        - [人脸关键点检测](https:\u002F\u002Fgithub.com\u002Fmrgloom\u002FFace-landmarks-detection-benchmark)\n        - [关键点检测](https:\u002F\u002Fgithub.com\u002FD-X-Y\u002Flandmark-detection)\n        - [人脸防伪](https:\u002F\u002Fgithub.com\u002FZitongYu\u002FDeepFAS)\n    - [视线估计](https:\u002F\u002Fgithub.com\u002FWuZhuoran\u002Fawesome-gaze)\n    - [HDR图像合成](https:\u002F\u002Fgithub.com\u002Fvinthony\u002Fawesome-deep-hdr)\n    - 图像分割\n        - [语义分割](https:\u002F\u002Fgithub.com\u002Fmrgloom\u002Fawesome-semantic-segmentation)\n        - [Segmentation.X](https:\u002F\u002Fgithub.com\u002FwutianyiRosun\u002FSegmentation.X)\n        - [全景分割](https:\u002F\u002Fgithub.com\u002FAngzz\u002Fawesome-panoptic-segmentation)\n        - [弱监督语义分割](https:\u002F\u002Fgithub.com\u002FYimingCuiCuiCui\u002Fawesome-weakly-supervised-segmentation)\n        - [指代图像分割](https:\u002F\u002Fgithub.com\u002FMarkMoHR\u002FAwesome-Referring-Image-Segmentation)\n        - [医学图像分割的半监督学习](https:\u002F\u002Fgithub.com\u002FHiLab-git\u002FSSL4MIS)\n    - [目标跟踪](https:\u002F\u002Fgithub.com\u002Ffoolwood\u002Fbenchmark_results)\n        - [视觉跟踪1](https:\u002F\u002Fgithub.com\u002Ffoolwood\u002Fbenchmark_results)\n        - [视觉跟踪2](https:\u002F\u002Fgithub.com\u002Fczla\u002Fdaily-paper-visual-tracking)\n        - [多目标跟踪](https:\u002F\u002Fgithub.com\u002FSpyderXu\u002Fmulti-object-tracking-paper-list)\n        - [跟踪与检测](https:\u002F\u002Fgithub.com\u002Fabhineet123\u002FDeep-Learning-for-Tracking-and-Detection)\n        - [daily-paper-visual-tracking](https:\u002F\u002Fgithub.com\u002Fczla\u002Fdaily-paper-visual-tracking)\n        - [多模态跟踪](https:\u002F\u002Fgithub.com\u002Fzhang-pengyu\u002FMultimodal-Tracking-Survey)\n    - [姿态估计](https:\u002F\u002Fgithub.com\u002FwjbKimberly\u002Fpose_estimation_CVPR_ECCV_2018)\n        - [物体姿态估计](https:\u002F\u002Fgithub.com\u002FYoungXIAO13\u002FObjectPoseEstimationSummary)\n        - 人体姿态估计\n          - [人体姿态估计1](https:\u002F\u002Fgithub.com\u002Fcbsudux\u002Fawesome-human-pose-estimation)\n          - [人体姿态估计2](https:\u002F\u002Fgithub.com\u002Fwangzheallen\u002Fawesome-human-pose-estimation)\n        - [手部姿态估计](https:\u002F\u002Fgithub.com\u002Fxinghaochen\u002Fawesome-hand-pose-estimation)\n        - [6D物体姿态估计](https:\u002F\u002Fgithub.com\u002FZhongqunZHANG\u002Fawesome-6d-object)\n    - [人体运动](https:\u002F\u002Fgithub.com\u002Fderikon\u002Fawesome-human-motion)\n    - [人机交互(HOI)](https:\u002F\u002Fgithub.com\u002FDirtyHarryLYL\u002FHOI-Learning-List)\n    - [长尾数据](https:\u002F\u002Fgithub.com\u002Fzwzhang121\u002FAwesome-of-Long-Tailed-Recognition)\n    - 场景文本\n        - [场景文本定位与识别](https:\u002F\u002Fgithub.com\u002Fchongyangtao\u002FAwesome-Scene-Text-Recognition)\n        - [场景文本定位与识别资源](https:\u002F\u002Fgithub.com\u002Fwhitelok\u002Fimage-text-localization-recognition)\n        - [场景文本检测和识别](https:\u002F\u002Fgithub.com\u002FJyouhou\u002FSceneTextPapers)\n        - [文本检测和识别](https:\u002F\u002Fgithub.com\u002Fhwalsuklee\u002Fawesome-deep-text-detection-recognition)\n        - [场景文本识别资源](https:\u002F\u002Fgithub.com\u002FHCIILAB\u002FScene-Text-Recognition)\n    - 超分辨率\n        - [超分辨率（ChaofWang）](https:\u002F\u002Fgithub.com\u002FChaofWang\u002FAwesome-Super-Resolution)\n        - [超分辨率（ptkin）](\u003Chttps:\u002F\u002Fgithub.com\u002Fptkin\u002FAwesome-Super-Resolution>)\n        - [图像超分辨率](https:\u002F\u002Fgithub.com\u002FYapengTian\u002FSingle-Image-Super-Resolution)\n        - [视频超分辨率](https:\u002F\u002Fgithub.com\u002FLoSealL\u002FVideoSuperResolution)\n    - 3D\n        - [3D重建](https:\u002F\u002Fgithub.com\u002FopenMVG\u002Fawesome_3DReconstruction_list)\n        - [3D人体](https:\u002F\u002Fgithub.com\u002F3DFaceBody\u002Fawesome-3dbody-papers)\n    - [OCR](https:\u002F\u002Fgithub.com\u002Fkba\u002Fawesome-ocr)\n    - Re-ID\n        - [行人Re-ID(1)](https:\u002F\u002Fgithub.com\u002Fbismex\u002FAwesome-person-re-identification)\n        - [行人Re-ID(2)](https:\u002F\u002Fgithub.com\u002FFDU-VTS\u002FAwesome-Person-Re-Identification)\n        - [车辆Re-ID(1)](https:\u002F\u002Fgithub.com\u002Flayumi\u002FVehicle_reID-Collection)\n        - [车辆Re-ID(2)](https:\u002F\u002Fgithub.com\u002Fknwng\u002Fawesome-vehicle-re-identification)\n    - [行人属性识别](https:\u002F\u002Fgithub.com\u002Fwangxiao5791509\u002FPedestrian-Attribute-Recognition-Paper-List)\n    - [人员搜索](https:\u002F\u002Fgithub.com\u002Fwangxiao5791509\u002FPerson-Search-Paper-List)\n    - [图像字幕生成](https:\u002F\u002Fgithub.com\u002Fzhjohnchan\u002Fawesome-image-captioning)\n    - [问答](https:\u002F\u002Fgithub.com\u002Fdapurv5\u002Fawesome-question-answering)\n    - [人群计数](https:\u002F\u002Fgithub.com\u002Fgjy3035\u002FAwesome-Crowd-Counting)\n    - [车道检测](https:\u002F\u002Fgithub.com\u002Famusi\u002Fawesome-lane-detection)\n    - [低光增强](https:\u002F\u002Fgithub.com\u002FElin24\u002FAwesome-Low-Lignt-Enhancement) \n    - 图像检索\n        - [优秀的图像检索论文(1)](https:\u002F\u002Fgithub.com\u002Fwillard-yuan\u002Fawesome-cbir-papers)\n        - [优秀的图像检索论文(2)](https:\u002F\u002Fgithub.com\u002Flgbwust\u002Fawesome-image-retrieval-papers)\n    - [医学影像](https:\u002F\u002Fgithub.com\u002Ffepegar\u002Fawesome-medical-imaging)\n        - [医学数据](https:\u002F\u002Fgithub.com\u002Fbeamandrew\u002Fmedical-data)\n        - [医学影像数据集](https:\u002F\u002Fgithub.com\u002Fsfikas\u002Fmedical-imaging-datasets)\n        - [用于医学影像的优秀GAN](https:\u002F\u002Fgithub.com\u002Fxinario\u002Fawesome-gan-for-medical-imaging)\n        - [面向医疗应用的深度学习](https:\u002F\u002Fgithub.com\u002Falbarqouni\u002FDeep-Learning-for-Medical-Applications)\n        - [医学图像分割](https:\u002F\u002Fgithub.com\u002FJunMa11\u002FSOTA-MedSeg) \n    - [图像修复](https:\u002F\u002Fgithub.com\u002F1900zyh\u002FAwesome-Image-Inpainting)\n    - 图像\u002F视频去雾\n        - [awesome-dehazing](https:\u002F\u002Fgithub.com\u002Fyoungguncho\u002Fawesome-dehazing)\n        - [DehazeZoo](https:\u002F\u002Fgithub.com\u002Fcxtalk\u002FDehazeZoo)\n    - 图像去噪\n        - [可复现的图像去噪最先进方法](https:\u002F\u002Fgithub.com\u002Fwenbihan\u002Freproducible-image-denoising-state-of-the-art)\n        - [图像去噪最先进方法](https:\u002F\u002Fgithub.com\u002Fflyywh\u002FImage-Denoising-State-of-the-art)\n        - [图像和视频去噪](https:\u002F\u002Fgithub.com\u002Fz-bingo\u002Fawesome-image-denoising-state-of-the-art)\n        - [Awesome-Denoise](https:\u002F\u002Fgithub.com\u002FoneTaken\u002FAwesome-Denoise)\n    - [图像去雨](https:\u002F\u002Fgithub.com\u002FnnUyi\u002FDerainZoo)\n    - [图像\u002F视频去模糊]( https:\u002F\u002Fgithub.com\u002Fsubeeshvasu\u002FAwesome-Deblurring )\n    - 图像到图像(img2img)\n        - [lzhbrian\u002FImage to Image](https:\u002F\u002Fgithub.com\u002Flzhbrian\u002Fimage-to-image-papers)\n        - [xiaweihao\u002FImage to Image](https:\u002F\u002Fgithub.com\u002Fxiaweihao\u002Fawesome-image-translation)\n    - [多视图立体(MVS)](https:\u002F\u002Fgithub.com\u002FXYZ-qiyh\u002FAwesome-Learning-MVS)\n    - [水下图像增强](https:\u002F\u002Fgithub.com\u002Fcxtalk\u002FAwesome-Underwater-Image-Enhancement)\n    - [视频分析](https:\u002F\u002Fgithub.com\u002FHuaizhengZhang\u002FAwsome-Deep-Learning-for-Video-Analysis)\n    - [视频对象分割(VOS)](https:\u002F\u002Fgithub.com\u002Fdu0915\u002FVideo-Object-Segmentation-Paper-List)\n    - [边缘检测](\u003Chttps:\u002F\u002Fgithub.com\u002FMarkMoHR\u002FAwesome-Edge-Detection-Papers>)\n    - [局部与全局描述子](https:\u002F\u002Fgithub.com\u002Fshamangary\u002Fawesome-local-global-descriptor)\n    - [指代图像分割](https:\u002F\u002Fgithub.com\u002FMarkMoHR\u002FAwesome-Referring-Image-Segmentation)\n    - 显著性\n        - [显著性目标检测(SOD)](https:\u002F\u002Fgithub.com\u002Fjiwei0921\u002FSOD-CNNs-based-code-summary-)\n        - [显著性检测与分割](https:\u002F\u002Fgithub.com\u002Flartpang\u002Fawesome-segmentation-saliency-dataset#another-awesome-dataset-list)\n        - [RGB-D显著性目标检测](https:\u002F\u002Fgithub.com\u002Ftaozh2017\u002FRGBD-SODsurvey)\n    - [医学影像中的自监督学习](https:\u002F\u002Fgithub.com\u002FSaeedShurrab\u002Fawesome-self-supervised-learning-in-medical-imaging)\n    - [时尚+AI](https:\u002F\u002Fgithub.com\u002Flzhbrian\u002FCool-Fashion-Papers)\n    - [事件驱动视觉资源](https:\u002F\u002Fgithub.com\u002Fuzh-rpg\u002Fevent-based_vision_resources)\n    - [视频稳定](https:\u002F\u002Fgithub.com\u002Fyaochih\u002Fawesome-video-stabilization)\n    - 视觉Transformer\n        - [Transformer-in-Vision](https:\u002F\u002Fgithub.com\u002FDirtyHarryLYL\u002FTransformer-in-Vision)\n        - [优秀的视觉Transformer资源列表](https:\u002F\u002Fgithub.com\u002Flijiaman\u002Fawesome-transformer-for-vision)\n        - [Awesome-Visual-Transformer](https:\u002F\u002Fgithub.com\u002Fdk-liang\u002FAwesome-Visual-Transformer)\n        - [使用Transformer进行视觉表征学习的优秀资源](https:\u002F\u002Fgithub.com\u002Falohays\u002Fawesome-visual-representation-learning-with-transformers)\n\n\u003Ca name=\"NLP\">\u003C\u002Fa>\n\n\n\n# 自然语言处理(NLP)\n\n- [NLP](https:\u002F\u002Fgithub.com\u002Fkeon\u002Fawesome-nlp)\n- [NLP-progress](https:\u002F\u002Fgithub.com\u002Fsebastianruder\u002FNLP-progress)\n- [CoreNLP](https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002FCoreNLP)\n- [NLPIR](https:\u002F\u002Fgithub.com\u002FNLPIR-team\u002FNLPIR)\n- [nlp_course](https:\u002F\u002Fgithub.com\u002Fyandexdataschool\u002Fnlp_course)\n- [nlp-datasets](https:\u002F\u002Fgithub.com\u002Fniderhoff\u002Fnlp-datasets)\n- [nlp-reading-group](https:\u002F\u002Fgithub.com\u002Fclulab\u002Fnlp-reading-group)\n- [NLP Paper](https:\u002F\u002Fgithub.com\u002Fchangwookjun\u002Fnlp-paper)\n- [Awesome-Chinese-NLP](https:\u002F\u002Fgithub.com\u002Fcrownpku\u002FAwesome-Chinese-NLP)：中文自然语言处理相关资料\n- [awesome-dl4nlp](https:\u002F\u002Fgithub.com\u002Fbrianspiering\u002Fawesome-dl4nlp)\n- [awesome-sentence-embedding](https:\u002F\u002Fgithub.com\u002FSeparius\u002Fawesome-sentence-embedding)\n- 研究主题\n  - [机器翻译](https:\u002F\u002Fgithub.com\u002FZNLP\u002FSOTA-MT) \n\n\u003Ca name=\"SR\">\u003C\u002Fa>\n\n# 语音识别\n\n- [speech_recognition](https:\u002F\u002Fgithub.com\u002FUberi\u002Fspeech_recognition)\n- [awesome-speech-recognition-speech-synthesis-papers](https:\u002F\u002Fgithub.com\u002Fzzw922cn\u002Fawesome-speech-recognition-speech-synthesis-papers)\n\n\u003Ca name=\"ORT\">\u003C\u002Fa>\n\n# 其他研究主题\n\n- 贝叶斯\n     - [贝叶斯](https:\u002F\u002Fgithub.com\u002Fdimenwarper\u002Fawesome-bayes)\n     - [深度贝叶斯](https:\u002F\u002Fgithub.com\u002Fotokonoko8\u002Fdeep-Bayesian-nonparametrics-papers)\n- [胶囊网络](https:\u002F\u002Fgithub.com\u002Fsekwiatkowski\u002Fawesome-capsule-networks)\n- [对比学习](https:\u002F\u002Fgithub.com\u002FHobbitLong\u002FPyContrast\u002Fblob\u002Fmaster\u002FAWESOME_CONTRASTIVE_LEARNING.md)\n- [数据增强](https:\u002F\u002Fgithub.com\u002FAgaMiko\u002Fdata-augmentation-review)\n- [嵌入式AI](https:\u002F\u002Fgithub.com\u002Fysh329\u002Fawesome-embedded-ai)\n- GAN（生成对抗网络）\n  - [GAN案例研究](https:\u002F\u002Fgithub.com\u002Fpshams55\u002FGAN-Case-Study)\n  - [超赞GAN](https:\u002F\u002Fgithub.com\u002Fnightrome\u002Freally-awesome-gan)\n  - [对抗网络论文](https:\u002F\u002Fgithub.com\u002Fzhangqianhui\u002FAdversarialNetsPapers)\n  - [GAN动物园](https:\u002F\u002Fgithub.com\u002Fhindupuravinash\u002Fthe-gan-zoo)\n  - [Keras-GAN](https:\u002F\u002Fgithub.com\u002Feriklindernoren\u002FKeras-GAN)\n  - [GAN精彩应用](https:\u002F\u002Fgithub.com\u002Fnashory\u002Fgans-awesome-applications)：精选的GAN应用和演示列表\n  - [图像到图像转换](https:\u002F\u002Fgithub.com\u002Fweihaox\u002Fawesome-image-translation)\n  - [GAN反演](https:\u002F\u002Fgithub.com\u002Fweihaox\u002Fawesome-image-translation\u002Fblob\u002Fmaster\u002Fawesome-gan-inversion.md)\n- [图神经网络(GNN)](https:\u002F\u002Fgithub.com\u002Fthunlp\u002FGNNPapers)\n- [半监督学习](https:\u002F\u002Fgithub.com\u002Fyassouali\u002Fawesome-semi-supervised-learning)\n- [SLAM](https:\u002F\u002Fgithub.com\u002Fkanster\u002Fawesome-slam)\n  - [SLAM列表](https:\u002F\u002Fgithub.com\u002FOpenSLAM\u002Fawesome-SLAM-list)\n  - [视觉SLAM](https:\u002F\u002Fgithub.com\u002Ftzutalin\u002Fawesome-visual-slam)\n  - [SLAM（中文）](https:\u002F\u002Fgithub.com\u002FYiChenCityU\u002FRecent_SLAM_Research)\n  - [SLAM数据集](https:\u002F\u002Fgithub.com\u002Fyoungguncho\u002Fawesome-slam-datasets)\n  - [SFM-视觉SLAM](https:\u002F\u002Fgithub.com\u002Fmarknabil\u002FSFM-Visual-SLAM)\n  - [SLAM资源](https:\u002F\u002Fgithub.com\u002Fckddls1321\u002FSLAM_Resources)\n  - [wuxiaolang](https:\u002F\u002Fgithub.com\u002Fwuxiaolang)\u002F[视觉SLAM相关研究](https:\u002F\u002Fgithub.com\u002Fwuxiaolang\u002FVisual_SLAM_Related_Research)\n- [强化学习](https:\u002F\u002Fgithub.com\u002Faikorea\u002Fawesome-rl)\n     - [强化学习算法实现](https:\u002F\u002Fgithub.com\u002Fdennybritz\u002Freinforcement-learning)\n     - [强化学习中文](https:\u002F\u002Fgithub.com\u002FwwxFromTju\u002Fawesome-reinforcement-learning-zh)：中文整理的强化学习资料\n- [迁移学习](https:\u002F\u002Fgithub.com\u002Fjindongwang\u002Ftransferlearning)\n- [轨迹预测](https:\u002F\u002Fgithub.com\u002Fjiachenli94\u002FAwesome-Interaction-aware-Trajectory-Prediction)\n- [零样本学习](https:\u002F\u002Fgithub.com\u002Fchichilicious\u002Fawesome-zero-shot-learning)\n- 少样本学习\n     - [Duan-JM\u002F少样本学习](https:\u002F\u002Fgithub.com\u002FDuan-JM\u002Fawesome-papers-fewshot)\n     - [e-271\u002F少样本学习](https:\u002F\u002Fgithub.com\u002Fe-271\u002Fawesome-few-shot-learning)\n- 联邦学习\n     - [poga\u002F联邦学习](https:\u002F\u002Fgithub.com\u002Fpoga\u002Fawesome-federated-learning)\n     - [联邦计算\u002F学习](https:\u002F\u002Fgithub.com\u002Ftushar-semwal\u002Fawesome-federated-computing)\n     - [ChanChiChoi\u002F联邦学习](ChaoChiChoi\u002FFederated Learning)\n- 元学习\n     - [元学习1](https:\u002F\u002Fgithub.com\u002Fdragen1860\u002Fawesome-meta-learning)\n     - [元学习2](https:\u002F\u002Fgithub.com\u002Fsudharsan13296\u002FAwesome-Meta-Learning)\n- [开放集识别](https:\u002F\u002Fgithub.com\u002FiCGY96\u002Fawesome_OpenSetRecognition_list)\n- 自监督\n     - [jason718\u002F自监督](https:\u002F\u002Fgithub.com\u002Fjason718\u002Fawesome-self-supervised-learning)\n     - [Sungman-Cho\u002F自监督]( https:\u002F\u002Fgithub.com\u002FSungman-Cho\u002FAwesome-Self-Supervised-Papers )\n     - [wvangansbeke\u002F自监督学习概述](wvangansbeke\u002FSelf-Supervised-Learning-Overview)\n- [图分类](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-graph-classification)\n- [增量学习](https:\u002F\u002Fgithub.com\u002Fxialeiliu\u002FAwesome-Incremental-Learning)\n- [AutoML](https:\u002F\u002Fgithub.com\u002Fhibayesian\u002Fawesome-automl-papers)\n     - [AutoML综述](https:\u002F\u002Fgithub.com\u002FDataSystemsGroupUT\u002FAutoML_Survey)\n     - [AutoML与轻量级模型](https:\u002F\u002Fgithub.com\u002Fguan-yuan\u002Fawesome-AutoML-and-Lightweight-Models)\n     - [NAS](https:\u002F\u002Fgithub.com\u002FD-X-Y\u002FAwesome-NAS)\n     - [架构搜索](https:\u002F\u002Fgithub.com\u002Fmarkdtw\u002Fawesome-architecture-search)\n     - [神经架构搜索文献](https:\u002F\u002Fwww.automl.org\u002Fautoml\u002Fliterature-on-neural-architecture-search\u002F)\n- [模型压缩](https:\u002F\u002Fgithub.com\u002Fcedrickchee\u002Fawesome-ml-model-compression)\n     - [EfficientDNNs](https:\u002F\u002Fgithub.com\u002FMingSun-Tse\u002FEfficientDNNs)\n     - [模型压缩与加速](https:\u002F\u002Fgithub.com\u002Fmemoiry\u002FAwesome-model-compression-and-acceleration)\n     - [神经网络剪枝](https:\u002F\u002Fgithub.com\u002Fhe-y\u002FAwesome-Pruning)\n- [二值神经网络](https:\u002F\u002Fgithub.com\u002Fmichaeltinsley\u002Fawesome-binary-neural-networks) \n- [多模态研究](https:\u002F\u002Fgithub.com\u002FEurus-Holmes\u002FAwesome-Multimodal-Research)\n- [多模态机器学习](https:\u002F\u002Fgithub.com\u002Fpliang279\u002Fawesome-multimodal-ml)\n- [神经渲染](https:\u002F\u002Fgithub.com\u002Fxiaweihao\u002Fawesome-neural-rendering)\n- [NeRF](https:\u002F\u002Fgithub.com\u002Fyenchenlin\u002Fawesome-NeRF)\n- [领域适应](https:\u002F\u002Fgithub.com\u002Fzhaoxin94\u002Fawsome-domain-adaptation)\n- [机器人技术](https:\u002F\u002Fgithub.com\u002Fkiloreux\u002Fawesome-robotics)\n- [推荐系统](https:\u002F\u002Fgithub.com\u002Frobi56\u002FDeep-Learning-for-Recommendation-Systems)\n- 自动驾驶车辆\n     - [自动驾驶车辆](https:\u002F\u002Fgithub.com\u002Ftakeitallsource\u002Fawesome-autonomous-vehicles)\n     - [自动驾驶车辆-CH]( https:\u002F\u002Fgithub.com\u002FDeepTecher\u002Fawesome-autonomous-vehicle )\n     - [用于自动驾驶的激光雷达点云处理](https:\u002F\u002Fgithub.com\u002Fbeedotkiran\u002FLidar_For_AD_references)\n- [异常检测](https:\u002F\u002Fgithub.com\u002Fyzhao062\u002Fanomaly-detection-resources)\n- [Yochengliu\u002F点云分析](https:\u002F\u002Fgithub.com\u002FYochengliu\u002Fawesome-point-cloud-analysis)\n- [NUAAXQ\u002F点云分析](https:\u002F\u002Fgithub.com\u002FNUAAXQ\u002Fawesome-point-cloud-analysis-2020)\n- [3D点云](https:\u002F\u002Fgithub.com\u002FQingyongHu\u002FSoTA-Point-Cloud)\n- [情感计算](https:\u002F\u002Fgithub.com\u002Fsuzana-ilic\u002FDeep_Learning_Affective_Computing)\n- 知识蒸馏\n     - [知识蒸馏(dkozlov)](https:\u002F\u002Fgithub.com\u002Fdkozlov\u002Fawesome-knowledge-distillation)\n     - [知识蒸馏(FLHonker)](https:\u002F\u002Fgithub.com\u002FFLHonker\u002FAwesome-Knowledge-Distillation)\n- [点击率预测](https:\u002F\u002Fgithub.com\u002Fshenweichen\u002FDeepCTR)\n- [标签噪声](https:\u002F\u002Fgithub.com\u002Fsubeeshvasu\u002FAwesome-Learning-with-Label-Noise)\n- [VAE](https:\u002F\u002Fgithub.com\u002Fmatthewvowels1\u002FAwesome-VAEs)\n- [不平衡学习](https:\u002F\u002Fgithub.com\u002FZhiningLiu1998\u002Fawesome-imbalanced-learning)\n\n\u003Ca name=\"PL\">\u003C\u002Fa>\n\n# 编程语言\n\n- [C](https:\u002F\u002Fnotabug.org\u002Fkoz.ross\u002Fawesome-c)\n- [C++](https:\u002F\u002Fgithub.com\u002Ffffaraz\u002Fawesome-cpp)\n- [Python](https:\u002F\u002Fgithub.com\u002Fvinta\u002Fawesome-python)\n- [JAVA](https:\u002F\u002Fgithub.com\u002Fakullpp\u002Fawesome-java)\n- [JavaScript](awesome-javascript)\n- [Julia](https:\u002F\u002Fgithub.com\u002Fsvaksha\u002FJulia.jl)\n- [MATLAB](https:\u002F\u002Fgithub.com\u002Fuhub\u002Fawesome-matlab)\n- [R](https:\u002F\u002Fgithub.com\u002Fqinwf\u002Fawesome-R)\n\n\u003Ca name=\"Framework\">\u003C\u002Fa>\n\n# 框架\n\n- [TensorFlow](https:\u002F\u002Fgithub.com\u002Fjtoy\u002Fawesome-tensorflow)\n  - [TensorFlow 从零到一](https:\u002F\u002Fgithub.com\u002Famusi\u002FTensorFlow-From-Zero-To-One)\n  - [TensorFlow Lite](https:\u002F\u002Fgithub.com\u002Fmargaretmz\u002Fawesome-tflite)\n- [PyTorch](https:\u002F\u002Fgithub.com\u002Fbharathgs\u002FAwesome-pytorch-list)\n  - [PyTorch 从零到一](https:\u002F\u002Fgithub.com\u002Famusi\u002FPyTorch-From-Zero-To-One)\n- [Keras](https:\u002F\u002Fgithub.com\u002Ffchollet\u002Fkeras-resources)\n- [MXNet](https:\u002F\u002Fgithub.com\u002Fchinakook\u002FAwesome-MXNet)\n- [Caffe](https:\u002F\u002Fgithub.com\u002FMichaelXin\u002FAwesome-Caffe)\n- [Torch](https:\u002F\u002Fgithub.com\u002Fcarpedm20\u002Fawesome-torch)\n- [Chainer](awesome-chainer)\n\n\u003Ca name=\"Datasets\">\u003C\u002Fa>\n\n# 数据集\n\n- [分割与显著性检测](https:\u002F\u002Fgithub.com\u002Flartpang\u002Fawesome-segmentation-saliency-dataset)\n\n# AI 职业发展\n\n- [机器学习软件工程精选资源](https:\u002F\u002Fgithub.com\u002FSE-ML\u002Fawesome-seml)","# awesome-ai-awesomeness 快速上手指南\n\n`awesome-ai-awesomeness` 并非一个可安装的软件库或框架，而是一个**精选资源列表（Curated List）**。它汇集了人工智能、机器学习、深度学习、计算机视觉等领域的优质开源项目、论文、数据集和学习资料。\n\n因此，本指南旨在指导开发者如何高效地浏览、检索并利用该列表中的资源，而非执行传统的安装命令。\n\n## 环境准备\n\n由于该项目本质上是 GitHub 上的 Markdown 文档集合，使用门槛极低：\n\n*   **系统要求**：任意操作系统（Windows, macOS, Linux）。\n*   **前置依赖**：\n    *   现代 Web 浏览器（推荐 Chrome, Edge, Firefox）用于在线浏览。\n    *   或 Git 工具（可选），用于克隆仓库到本地离线阅读。\n*   **网络建议**：\n    *   该项目托管于 GitHub。国内用户访问可能受限或速度较慢。\n    *   **推荐方案**：使用国内代码托管平台镜像（如 Gitee 镜像，若有）或配置 GitHub 加速代理。若无加速条件，建议直接访问其渲染后的静态页面（如有）或使用 Git 克隆后本地查看。\n\n## 获取与浏览步骤\n\n你可以通过以下两种方式访问该资源列表：\n\n### 方式一：在线浏览（推荐）\n\n直接在浏览器中访问 GitHub 仓库页面，利用目录跳转阅读。\n\n1.  访问仓库主页：\n    ```text\n    https:\u002F\u002Fgithub.com\u002Fowainlewis\u002Fawesome-artificial-intelligence (主入口及关联列表)\n    ```\n    *注：`awesome-ai-awesomeness` 通常指代这一类 Awesome 列表的集合，具体细分领域请参考 README 中的目录链接。*\n\n2.  点击页面上的 **Table of Contents**（目录）跳转到感兴趣的主题，例如：\n    *   `Machine Learning(ML)`\n    *   `Computer Vision(CV)`\n    *   `Natural Language Processing(NLP)`\n\n### 方式二：本地克隆（适合离线查阅）\n\n如果你希望将这份庞大的资源列表保存到本地，方便搜索和整理，可以使用 Git 克隆。\n\n1.  打开终端（Terminal 或 CMD）。\n2.  执行克隆命令（若直连速度慢，请替换为国内镜像地址）：\n    ```bash\n    git clone https:\u002F\u002Fgithub.com\u002Fowainlewis\u002Fawesome-artificial-intelligence.git\n    ```\n    *(注：如果针对特定的子列表，如机器学习中文版，可克隆：`git clone https:\u002F\u002Fgithub.com\u002Fjobbole\u002Fawesome-machine-learning-cn.git`)*\n\n3.  进入目录并使用 Markdown 阅读器（如 VS Code, Typora）打开 `.md` 文件：\n    ```bash\n    cd awesome-artificial-intelligence\n    code .  # 使用 VS Code 打开\n    ```\n\n## 基本使用\n\n该列表的核心价值在于**发现**和**导航**。以下是最高效的使用流程：\n\n### 1. 按领域定位资源\n根据你当前的开发需求，在目录中找到对应板块。例如，如果你需要**目标检测（Object Detection）**相关的最新论文和代码：\n*   跳转至 `Computer Vision(CV)` -> `Object Detection` 章节。\n*   你会看到类似以下的精选列表：\n    *   [amusi\u002FObject Detection](https:\u002F\u002Fgithub.com\u002Famusi\u002Fawesome-object-detection)\n    *   [Tiny Object Detection](https:\u002F\u002Fgithub.com\u002Fkuanhungchen\u002Fawesome-tiny-object-detection)\n\n### 2. 深入子列表\n点击上述链接进入具体的子仓库。这些子仓库通常包含更详细的：\n*   **Paper List**：经典与最新论文清单。\n*   **Code Implementation**：对应的 GitHub 代码实现链接。\n*   **Datasets**：相关数据集下载源。\n\n### 3. 实战示例：寻找中文机器学习教程\n假设你想寻找适合中文读者的机器学习入门资料：\n1.  在总目录中找到 `Machine Learning(ML)`。\n2.  点击 **[ML-CN](https:\u002F\u002Fgithub.com\u002Fjobbole\u002Fawesome-machine-learning-cn)** 链接。\n3.  在该子页面中，你可以直接找到分类好的“入门教程”、“视频课程”和“实战项目”，并直接跳转到对应的中文资源页。\n\n### 4. 贡献资源（可选）\n如果你发现了优秀的 AI 工具并希望收录：\n1.  Fork 对应的仓库。\n2.  按照现有格式在 `.md` 文件中添加链接。\n3.  提交 Pull Request (PR)。\n    ```bash\n    # 示例工作流\n    git checkout -b add-new-resource\n    # 编辑 README.md 添加内容\n    git commit -m \"Add awesome new AI tool\"\n    git push origin add-new-resource\n    ```\n\n通过这种方式，`awesome-ai-awesomeness` 将成为你探索 AI 技术栈的最高效导航图。","某初创公司的算法工程师团队正着手研发一款基于计算机视觉的工业缺陷检测系统，急需在两周内完成技术选型并复现主流模型。\n\n### 没有 awesome-ai-awesomeness 时\n- **信息检索低效**：工程师需在 GitHub、arXiv 和各类博客间反复切换搜索，花费数天才能拼凑出零散的物体检测（Object Detection）和图像分类资源。\n- **前沿方向遗漏**：由于缺乏系统性指引，团队容易忽略“事件相机视觉（Event-based Vision）”或“可解释性深度学习”等关键细分领域的最新进展，导致技术方案滞后。\n- **学习路径混乱**：新人面对海量且质量参差不齐的教程不知所措，难以快速找到如\"DeepLearning-500-questions\"这样结构化的面试与学习资料，拖慢上手速度。\n- **生产落地困难**：在从实验模型转向生产环境时，找不到关于\"Deep-Learning-in-Production\"或模型压缩加速的专门清单，导致部署阶段频繁踩坑。\n\n### 使用 awesome-ai-awesomeness 后\n- **资源一键直达**：通过目录直接定位到 Computer Vision 板块下的 Object Detection 和 Image Classification 子项，瞬间获取该领域最权威的开源项目列表，将调研时间从数天缩短至几小时。\n- **视野全面覆盖**：借助\"Other Research Topics\"及细分专题链接，团队迅速发现了适合产线高速场景的事件视觉资源，并引入了可解释性 AI 方案以提升客户信任度。\n- **成长体系清晰**：团队成员利用 curated 的学习路径和问答库，快速统一了知识基线，显著降低了沟通成本和培训周期。\n- **落地有据可依**：直接参考\"Machine Learning System\"和\"DNN Compression\"相关清单，选择了经过验证的部署框架与加速策略，确保系统按时高质量上线。\n\nawesome-ai-awesomeness 将原本碎片化、高成本的 AI 技术探索过程，转化为一条结构化、高效率的研发加速通道。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Famusi_awesome-ai-awesomeness_454902ba.png","amusi","Amusi","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Famusi_84f21910.jpg","欢迎关注微信公众号: CVer，了解最新最前沿的AI\u002FCV技术","CVer","Shanghai, China","1609951733@qq.com",null,"https:\u002F\u002Fmp.weixin.qq.com\u002Fs\u002FEGiZz-Mt-8pHG7Sq_OA_UA","https:\u002F\u002Fgithub.com\u002Famusi",975,126,"2026-04-12T16:56:16",1,"","未说明",{"notes":89,"python":87,"dependencies":90},"该项目是一个人工智能领域的资源列表（Awesome List），仅包含指向其他 GitHub 仓库、论文和资料的链接集合，本身不包含可执行的代码、模型或安装脚本，因此无需特定的运行环境、GPU、内存或依赖库。用户只需浏览器即可访问，或克隆仓库查看 Markdown 文件。",[],[14,15,35],[93,94,95,96,97,98,99],"artificial-intelligence","artificial-neural-networks","neural-networks","computer-vision","deep-learning","machine-l","natural-language-processing","2026-03-27T02:49:30.150509","2026-04-16T10:56:52.948366",[],[]]