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align=\"center\"\u003e\n  \u003cimg src=\"image/52CV1.png\" width=\"600\"/\u003e\n\u003c/div\u003e\n\n## 查看2025年综述文献点这里↘️[2025-CV-Surveys](https://github.com/52CV/CV-Surveys)\n\n## 2025 年论文分类汇总戳这里\n↘️[WACV-2025-Papers](https://github.com/52CV/WACV-2025-Papers)\n↘️[CVPR-2025-Papers](https://github.com/52CV/CVPR-2025-Papers)\n\n## 2024 年论文分类汇总戳这里\n↘️[WACV-2024-Papers](https://github.com/52CV/WACV-2024-Papers)\n↘️[CVPR-2024-Papers](https://github.com/52CV/CVPR-2024-Papers)\n↘️[ECCV-2024-Papers](https://github.com/52CV/ECCV-2024-Papers)\n\n## [2023 年论文分类汇总戳这里](#00000)\n## [2022 年论文分类汇总戳这里](#0000)\n## [2021 年论文分类汇总戳这里](#000)\n## [2020 年论文分类汇总戳这里](#00)\n\n# 2025-CV-Surveys\n\n2025 年，计算机视觉相关综述。包括目标检测、跟踪........\n\n### :green_book::green_book::green_book:在[【我爱计算机视觉】微信公众号](https://user-images.githubusercontent.com/62801906/163739684-175f0b8a-871e-4a41-b310-b549625fdcb1.png)后台回复“CV综述”，即可收到本文列出的全部论文的打包下载。至4月18日已公开 158+2 篇。\n1月36篇。\u003cBR\u003e\n2月50篇。\u003cBR\u003e\n3月45篇。\n\n## 目录\n\n|:cat:|:dog:|:tiger:|:wolf:|\n|------|------|------|------|\n|[1.Unkown(未分)](#1)|\n\n\n## Machine Learning\n* [Machine Learning Applications to Diffuse Reflectance Spectroscopy in Optical Diagnosis; A Systematic Review](https://arxiv.org/abs/2503.02905)\u003cBR\u003e[2025-03-06]\n* 强化学习\n  * [Exploring Mutual Empowerment Between Wireless Networks and RL-based LLMs: A Survey](https://arxiv.org/abs/2503.09956)\u003cBR\u003e[2025-03-14]\n* 对比学习\n  * [A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters](https://arxiv.org/abs/2502.08134)\u003cBR\u003e[2025-02-13]\n* 类增量学习\n  * [Latest Advancements Towards Catastrophic Forgetting under Data Scarcity: A Comprehensive Survey on Few-Shot Class Incremental Learning](https://arxiv.org/abs/2502.08181)\u003cBR\u003e[2025-02-13]\n* 对抗\n  * [A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challenges](https://arxiv.org/abs/2503.00384)\u003cBR\u003e[2025-03-04]\n\n## agriculture(农业)\n* [A survey of datasets for computer vision in agriculture](https://arxiv.org/abs/2502.16950)\u003cBR\u003e:star:[code](https://smartfarminglab.github.io/field_dataset_survey/)\u003cBR\u003e[2025-02-25]\n\n## Biomedical(生物特征识别)\n* 掌纹识别\n  * [Deep Learning in Palmprint Recognition-A Comprehensive Survey](https://arxiv.org/abs/2501.01166)\u003cBR\u003e[2025-01-03]\n\n## Neural Radiance Fields\n* [Neural Radiance Fields for the Real World: A Survey](https://arxiv.org/abs/2501.13104)\u003cBR\u003e[2025-01-23]\n\n## Robots(机器人)\n* [Semantic Mapping in Indoor Embodied AI – A Comprehensive Survey and Future Directions](https://arxiv.org/abs/2501.05750)\u003cBR\u003e[2025-01-13]\n\n## Industrial Defect Detection(工业缺陷检测)\n* [Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review](https://arxiv.org/abs/2501.11310)\u003cBR\u003e[2025-01-22]\n* [A Survey on Industrial Anomalies Synthesis](https://arxiv.org/abs/2502.16412)\u003cBR\u003e:star:[code](https://github.com/M-3LAB/awesome-anomaly-synthesis.)\u003cBR\u003e[2025-02-25]\n* [A Survey on Foundation-Model-Based Industrial Defect Detection](https://arxiv.org/abs/2502.19106)\u003cBR\u003e[2025-02-27]\n\n## Video\n* [A Survey on Video Analytics in Cloud-Edge-Terminal Collaborative Systems](https://arxiv.org/abs/2502.06581)\u003cBR\u003e[2025-02-11]\n\n## Action Detection(动作检测)\n* [Action Valuation in Sports: A Survey](https://arxiv.org/abs/2504.06163)\u003cBR\u003e[2025-04-09]\n\n## Autonomous Driving(自动驾驶)\n* [A Survey of World Models for Autonomous Driving](https://arxiv.org/abs/2501.11260)\u003cBR\u003e[2025-01-22]\n* [The Role of World Models in Shaping Autonomous Driving: A Comprehensive Survey](https://arxiv.org/abs/2502.10498)\u003cBR\u003e:star:[code](https://github.com/LMD0311/Awesome-World-Model)\u003cBR\u003e[2025-02-18]\n* [4D mmWave Radar in Adverse Environments for Autonomous Driving: A Survey](https://arxiv.org/abs/2503.24091)\u003cBR\u003e[2025-04-01]\n* [Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspective](https://arxiv.org/abs/2504.04631)\u003cBR\u003e[2025-04-08]\n* [Adversarial Examples in Environment Perception for Automated Driving (Review)](https://arxiv.org/abs/2504.08414)\u003cBR\u003e[2025-04-14]\n* [Collaborative Perception Datasets for Autonomous Driving: A Review](https://arxiv.org/abs/2504.12696)\u003cBR\u003e:star:[code](https://github.com/frankwnb/Collaborative-Perception-Datasets-for-Autonomous-Driving)\u003cBR\u003e[2025-04-18]\n* 车道线检测\n  * [Datasets for Lane Detection in Autonomous Driving: A Comprehensive Review](https://arxiv.org/abs/2504.08540)\u003cBR\u003e[2025-04-14]\n* 分心驾驶检测\n  * [A Review Paper of the Effects of Distinct Modalities and ML Techniques to Distracted Driving Detection](https://arxiv.org/abs/2501.11758)\u003cBR\u003e[2025-01-22]\n\n## Machine Learning\n* [A Systematic Review of Machine Learning Methods for Multimodal EEG Data in Clinical Application](https://arxiv.org/abs/2501.08585)\u003cBR\u003e[2025-01-16]\n\n## Few/Zero-Shot Learning/DG/A(小/零样本/域泛化/域适应)\n* Non-Transferable Learning(反迁移学习)\n  * [Toward Robust Non-Transferable Learning: A Survey and Benchmark](https://arxiv.org/abs/2502.13593)\u003cBR\u003e[2025-02-20]\n\n## Retrieval-Augmented Generation(检索增强生成)\n* [Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook](https://arxiv.org/abs/2503.18016)\u003cBR\u003e:star:[code](https://github.com/zhengxuJosh/Awesome-RAG-Vision)\u003cBR\u003e[2025-03-25]\n\n## Vision-Language(视觉语言)\n* [Large Vision-Language Model Alignment and Misalignment: A Survey Through the Lens of Explainability](https://arxiv.org/abs/2501.01346)\u003cBR\u003e[2025-01-03]\n* [Benchmark Evaluations, Applications, and Challenges of Large Vision Language Models: A Survey](https://arxiv.org/abs/2501.02189)\u003cBR\u003e:star:[code](https://github.com/zli12321/Awesome-VLM-Papers-And-Models.git)\u003cBR\u003e[2025-01-07]\n* [Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches](https://arxiv.org/abs/2501.03151)\u003cBR\u003e[2025-01-07]\n* [Visual Large Language Models for Generalized and Specialized Applications](https://arxiv.org/abs/2501.02765)\u003cBR\u003e:star:[code](https://github.com/JackYFL/awesome-VLLMs)\u003cBR\u003e[2025-01-07]\n* [When Data Manipulation Meets Attack Goals: An In-depth Survey of Attacks for VLMs](https://arxiv.org/abs/2502.06390)\u003cBR\u003e:star:[code](https://github.com/AobtDai/VLM_Attack_Paper_List)\u003cBR\u003e[2025-02-11]\n* [Survey on Vision-Language-Action Models](https://arxiv.org/abs/2502.06851)\u003cBR\u003e[2025-02-12]\n* [Vision-Language Models for Edge Networks: A Comprehensive Survey](https://arxiv.org/abs/2502.07855)\u003cBR\u003e[2025-02-13]\n* [Harnessing Vision Models for Time Series Analysis: A Survey](https://arxiv.org/abs/2502.08869)\u003cBR\u003e[2025-02-14]\n* [A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations](https://arxiv.org/abs/2502.14881)\u003cBR\u003e:star:[code](https://github.com/XuankunRong/Awesome-LVLM-Safety)\u003cBR\u003e[2025-02-24]\n* [Multi-Modal Foundation Models for Computational Pathology: A Survey](https://arxiv.org/abs/2503.09091)\u003cBR\u003e[2025-03-13]\n* [Small Vision-Language Models: A Survey on Compact Architectures and Techniques](https://arxiv.org/abs/2503.10665)\u003cBR\u003e[2025-03-17]\n* [A Survey on Efficient Vision-Language Models](https://arxiv.org/abs/2504.09724)\u003cBR\u003e:star:[code](https://github.com/MPSC-UMBC/Efficient-Vision-Language-Models-A-Survey)\u003cBR\u003e[2025-04-15]\n* LLM\n  * [Leveraging Large Language Models For Scalable Vector Graphics Processing: A Review](https://arxiv.org/abs/2503.04983)\u003cBR\u003e[2025-03-10]\n  * [A Review on Large Language Models for Visual Analytics](https://arxiv.org/abs/2503.15176)\u003cBR\u003e[2025-03-20]\n  * [Distributed LLMs and Multimodal Large Language Models: A Survey on Advances, Challenges, and Future Directions](https://arxiv.org/abs/2503.16585)\u003cBR\u003e[2025-03-24]\n  * [How to Enable LLM with 3D Capacity? A Survey of Spatial Reasoning in LLM](https://arxiv.org/abs/2504.05786)\u003cBR\u003e[2025-04-09]\n* MLLM\n  * [Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review](https://arxiv.org/abs/2502.16586)\u003cBR\u003e[2025-02-25]\n  * [Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey](https://arxiv.org/abs/2503.12605)\u003cBR\u003e:star:[code](https://github.com/yaotingwangofficial/Awesome-MCoT)\u003cBR\u003e[2025-03-18]\n  * [Aligning Multimodal LLM with Human Preference: A Survey](https://arxiv.org/abs/2503.14504)\u003cBR\u003e:star:[code](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Alignment.)\u003cBR\u003e[2025-03-19]\n  * [Survey of Adversarial Robustness in Multimodal Large Language Models](https://arxiv.org/abs/2503.13962)\u003cBR\u003e[2025-03-19]\n\n## GAN/Image Synthesis(图像生成)\n* [Generative AI for Cel-Animation: A Survey](https://arxiv.org/abs/2501.06250)\u003cBR\u003e:star:[code](https://github.com/yunlong10/Awesome-AI4Animation)\u003cBR\u003e[2025-01-14]\n* [Generative Physical AI in Vision: A Survey](https://arxiv.org/abs/2501.10928)\u003cBR\u003e:star:[code](https://github.com/BestJunYu/Awesome-Physics-aware-Generation)\u003cBR\u003e[2025-01-22]\n* [Survey on AI-Generated Media Detection: From Non-MLLM to MLLM](https://arxiv.org/abs/2502.05240)\u003cBR\u003e[2025-02-11]\n* [A Survey on Text-Driven 360-Degree Panorama Generation](https://arxiv.org/abs/2502.14799)\u003cBR\u003e:star:[code](https://littlewhitesea.github.io/Text-Driven-Pano-Gen/)\u003cBR\u003e[2025-02-21]\n* [Methods and Trends in Detecting Generated Images: A Comprehensive Review](https://arxiv.org/abs/2502.15176)\u003cBR\u003e[2025-02-24]\n* [Simulating the Real World: A Unified Survey of Multimodal Generative Models](https://arxiv.org/abs/2503.04641)\u003cBR\u003e[2025-03-07]\n* [Generative AI for Film Creation: A Survey of Recent Advances](https://arxiv.org/abs/2504.08296)\u003cBR\u003e[2025-04-14]\n* GAN \n  * [Image Inversion: A Survey from GANs to Diffusion and Beyond](https://arxiv.org/abs/2502.11974)\u003cBR\u003e:star:[code](https://github.com/RyanChenYN/ImageInversion)\u003cBR\u003e[2025-02-18]\n  * [Generative Adversarial Networks with Limited Data: A Survey and Benchmarking](https://arxiv.org/abs/2504.05456)\u003cBR\u003e[2025-04-09]\n* 图像生成\n  * [Preference Alignment on Diffusion Model: A Comprehensive Survey for Image Generation and Editing](https://arxiv.org/abs/2502.07829)\u003cBR\u003e[2025-02-13]\n  * [Personalized Image Generation with Deep Generative Models: A Decade Survey](https://arxiv.org/abs/2502.13081)\u003cBR\u003e:star:[code](https://github.com/csyxwei/Awesome-Personalized-Image-Generation)\u003cBR\u003e[2025-02-19]\n* AIGC\n  * [Grounding Creativity in Physics: A Brief Survey of Physical Priors in AIGC](https://arxiv.org/abs/2502.07007)\u003cBR\u003e[2025-02-12]\n* 图像到图像翻译\n  * [Unpaired Image-to-Image Translation with Content Preserving Perspective: A Review](https://arxiv.org/abs/2502.08667)\u003cBR\u003e[2025-02-14]\n* 文本-图像\n  * [A Comprehensive Survey on Concept Erasure in Text-to-Image Diffusion Models](https://arxiv.org/abs/2502.14896)\u003cBR\u003e[2025-02-24]\n  * [A Review on Generative AI For Text-To-Image and Image-To-Image Generation and Implications To Scientific Images](https://arxiv.org/abs/2502.21151)\u003cBR\u003e[2025-03-03]\n  * [A Systematic Review of Open Datasets Used in Text-to-Image (T2I) Gen AI Model Safety](https://arxiv.org/abs/2503.00020)\u003cBR\u003e[2025-03-04]\n  * [A Survey on Self-supervised Contrastive Learning for Multimodal Text-Image Analysis](https://arxiv.org/abs/2503.11101)\u003cBR\u003e[2025-03-17]\n  * [A Comprehensive Survey on Visual Concept Mining in Text-to-image Diffusion Models](https://arxiv.org/abs/2503.13576)\u003cBR\u003e[2025-03-19]\n* 视频生成\n  * [A Survey: Spatiotemporal Consistency in Video Generation](https://arxiv.org/abs/2502.17863)\u003cBR\u003e[2025-02-26]\n  * [Exploring the Evolution of Physics Cognition in Video Generation: A Survey](https://arxiv.org/abs/2503.21765)\u003cBR\u003e:star:[code](https://github.com/minnie-lin/Awesome-Physics-Cognition-based-Video-Generation)\u003cBR\u003e[2025-03-28]\n* 4D生成\n  * [Advances in 4D Generation: A Survey](https://arxiv.org/abs/2503.14501)\u003cBR\u003e:star:[code](https://github.com/MiaoQiaowei/Awesome-4D)\u003cBR\u003e[2025-03-19]\n* 3D生成\n  * [Recent Advance in 3D Object and Scene Generation: A Survey](https://arxiv.org/abs/2504.11734)\u003cBR\u003e[2025-04-17]\n* 视觉-音乐生成\n  * [Vision-to-Music Generation: A Survey](https://arxiv.org/abs/2503.21254)\u003cBR\u003e:star:[code](https://github.com/wzk1015/Awesome-Vision-to-Music-Generation.)\u003cBR\u003e[2025-03-28]\n\n## MC/KD/Pruning(模型压缩/知识蒸馏/剪枝)\n* [A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor Fusion](https://arxiv.org/abs/2501.07451)\u003cBR\u003e[2025-01-14]\n* [Vision Transformers on the Edge: A Comprehensive Survey of Model Compression and Acceleration Strategies](https://arxiv.org/abs/2503.02891)\u003cBR\u003e[2025-03-06]\n* KD\n  * [A Comprehensive Survey on Knowledge Distillation](https://arxiv.org/abs/2503.12067)\u003cBR\u003e:star:[code](https://github.com/IPL-Sharif/KD_Survey)\u003cBR\u003e[2025-03-18]\n\n## Visual Question Answering (视觉问答)\n* [Visual question answering: from early developments to recent advances -- a survey](https://arxiv.org/abs/2501.03939)\u003cBR\u003e[2025-01-08]\n* [The Quest for Visual Understanding: A Journey Through the Evolution of Visual Question Answering](https://arxiv.org/abs/2501.07109)\u003cBR\u003e[2025-01-14]\n\n## Medical Image Progress(医学图像处理)\n* [In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review](https://arxiv.org/abs/2501.10727)\u003cBR\u003e[2025-01-22]\n* [Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact](https://arxiv.org/abs/2502.08333)\u003cBR\u003e[2025-02-13]\n* [A Survey of LLM-based Agents in Medicine: How far are we from Baymax?](https://arxiv.org/abs/2502.11211)\u003cBR\u003e[2025-02-18]\n* [Denoising, segmentation and volumetric rendering of optical coherence tomography angiography (OCTA) image using deep learning techniques: a review](https://arxiv.org/abs/2502.14935)\u003cBR\u003e[2025-02-24]\n* [The Impact of Artificial Intelligence on Emergency Medicine: A Review of Recent Advances](https://arxiv.org/abs/2503.14546)\u003cBR\u003e[2025-03-20]\n* [Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imaging](https://arxiv.org/abs/2503.16543)\u003cBR\u003e[2025-03-24]\n* [Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey](https://arxiv.org/abs/2504.11588)\u003cBR\u003e[2025-04-17]\n* 医学图像分割\n  * [A Comprehensive Review of U-Net and Its Variants: Advances and Applications in Medical Image Segmentation](https://arxiv.org/abs/2502.06895)\u003cBR\u003e[2025-02-12]\n* 手术场景理解\n  * [Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Review](https://arxiv.org/abs/2502.14886)\u003cBR\u003e[2025-02-24]\n* 手术视频分割\n  * [Deep learning approaches to surgical video segmentation and object detection: A Scoping Review](https://arxiv.org/abs/2502.16459)\u003cBR\u003e[2025-02-25]\n* 图像配准\n  * [From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Review](https://arxiv.org/abs/2502.19123)\u003cBR\u003e[2025-02-27]\n* MRI重建\n  * [A Survey of fMRI to Image Reconstruction](https://arxiv.org/abs/2502.16861)\u003cBR\u003e[2025-02-25]\n  * [A Comprehensive Survey on Magnetic Resonance Image Reconstruction](https://arxiv.org/abs/2503.07097)\u003cBR\u003e[2025-03-11]\n  * [A Survey on fMRI-based Brain Decoding for Reconstructing Multimodal Stimuli](https://arxiv.org/abs/2503.15978)\u003cBR\u003e:star:[code](https://github.com/LpyNow/BrainDecodingImage)\u003cBR\u003e[2025-03-21]\n\n## OCR\n* [Handwritten Text Recognition: A Survey](https://arxiv.org/abs/2502.08417)\u003cBR\u003e[2025-02-13]\n\n## UAV/Remote Sensing/Satellite Image(无人机/遥感/卫星图像)\n* [Advancing Earth Observation: A Survey on AI-Powered Image Processing in Satellites](https://arxiv.org/abs/2501.12030)\u003cBR\u003e[2025-01-22]\n* [Plantation Monitoring Using Drone Images: A Dataset and Performance Review](https://arxiv.org/abs/2502.08233)\u003cBR\u003e[2025-02-13]\n* [A Survey on Remote Sensing Foundation Models: From Vision to Multimodality](https://arxiv.org/abs/2503.22081)\u003cBR\u003e[2025-03-31]\n* [A Decade of Deep Learning for Remote Sensing Spatiotemporal Fusion: Advances, Challenges, and Opportunities](https://arxiv.org/abs/2504.00901)\u003cBR\u003e:star:[code](https://github.com/yc-cui/Deep-Learning-Spatiotemporal-Fusion-Survey)\u003cBR\u003e[2025-04-02]\n* [MIMRS: A Survey on Masked Image Modeling in Remote Sensing](https://arxiv.org/abs/2504.03181)\u003cBR\u003e[2025-04-07]\n* [A comprehensive review of remote sensing in wetland classification and mapping](https://arxiv.org/abs/2504.10842)\u003cBR\u003e[2025-04-16]\n* Anti-UAV\n  * [Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions](https://arxiv.org/abs/2504.11967)\u003cBR\u003e[2025-04-17]\n* 变化检测\n  * [Operational Change Detection for Geographical Information: Overview and Challenges](https://arxiv.org/abs/2503.14109)\u003cBR\u003e[2025-03-19]\n* 船舶分类\n  * [A Survey on SAR ship classification using Deep Learning](https://arxiv.org/abs/2503.11906)\u003cBR\u003e[2025-03-18]\n* 火灾烟雾\n   [Fire and Smoke Datasets in 20 Years: An In-depth Review](https://arxiv.org/abs/2503.14552)\u003cBR\u003e[2025-03-20]\n\n## Object Detection(目标检测)\n* [YOLOv8 to YOLO11: A Comprehensive Architecture In-depth Comparative Review](https://arxiv.org/abs/2501.13400)\u003cBR\u003e[2025-01-24]\n* [Context in object detection: a systematic literature review](https://arxiv.org/abs/2503.23249)\u003cBR\u003e[2025-04-01]\n* [Vision-Language Model for Object Detection and Segmentation: A Review and Evaluation](https://arxiv.org/abs/2504.09480)\u003cBR\u003e:star:[code](https://github.com/better-chao/perceptual_abilities_evaluation)\u003cBR\u003e[2025-04-15]\n* [A Review of YOLOv12: Attention-Based Enhancements vs. Previous Versions](https://arxiv.org/abs/2504.11995)\u003cBR\u003e[2025-04-17]\n* 线路检测\n  * [Deep Learning in Automated Power Line Inspection: A Review](https://arxiv.org/abs/2502.07826)\u003cBR\u003e[2025-02-13]\n* 小目标检测\n  * [Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications](https://arxiv.org/abs/2503.20516)\u003cBR\u003e[2025-03-27]\n\n## HOI\n* [3D Human Interaction Generation: A Survey](https://arxiv.org/abs/2503.13120)\u003cBR\u003e[2025-03-18]\n* [A Survey on Human Interaction Motion Generation](https://arxiv.org/abs/2503.12763)\u003cBR\u003e:star:[code](https://github.com/soraproducer/Awesome-Human-Interaction-Motion-Generation)\u003cBR\u003e[2025-03-18]\n\n## Action Recognition\n* [SMART-Vision: Survey of Modern Action Recognition Techniques in Vision](https://arxiv.org/abs/2501.13066)\u003cBR\u003e[2025-01-23]\n\n## Pose(姿态估计)\n* [Survey on Hand Gesture Recognition from Visual Input](https://arxiv.org/abs/2501.11992)\u003cBR\u003e[2025-01-22]\n\n## Points Cloud(点云)\n* [Implicit Guidance and Explicit Representation of Semantic Information in Points Cloud: A Survey](https://arxiv.org/abs/2501.05473)\u003cBR\u003e[2025-01-13]\n* [Point Cloud Based Scene Segmentation: A Survey](https://arxiv.org/abs/2503.12595)\u003cBR\u003e[2025-03-18]\n\n## 3D Visual\n* 三维重建\n  * [Cutting-edge 3D reconstruction solutions for underwater coral reef images: A review and comparison](https://arxiv.org/abs/2502.20154)\u003cBR\u003e[2025-02-28]\n  * [Learning-based 3D Reconstruction in Autonomous Driving: A Comprehensive Survey](https://arxiv.org/abs/2503.14537)\u003cBR\u003e[2025-03-20]\n  * [A Survey on Event-driven 3D Reconstruction: Development under Different Categories](https://arxiv.org/abs/2503.19753)\u003cBR\u003e[2025-03-26]\n  * [Explicit and Implicit Representations in AI-based 3D Reconstruction for Radiology: A systematic literature review](https://arxiv.org/abs/2504.11349)\u003cBR\u003e:star:[code](https://github.com/Bean-Young/AI4Med)\u003cBR\u003e[2025-04-16]\n* 深度估计\n  * [A Systematic Literature Review on Deep Learning-based Depth Estimation in Computer Vision](https://arxiv.org/abs/2501.05147)\u003cBR\u003e[2025-01-10]\n  * [Survey on Monocular Metric Depth Estimation](https://arxiv.org/abs/2501.11841)\u003cBR\u003e[2025-01-22]\n\n## Face\n* [A Survey on Facial Image Privacy Preservation in Cloud-Based Services](https://arxiv.org/abs/2501.08665)\u003cBR\u003e[2025-01-16]\n* [Emotion Recognition and Generation: A Comprehensive Review of Face, Speech, and Text Modalities](https://arxiv.org/abs/2502.06803)\u003cBR\u003e[2025-02-12]\n* [Face Deepfakes - A Comprehensive Review](https://arxiv.org/abs/2502.09812)\u003cBR\u003e[2025-02-17]\n* 情绪分析\n  * [Enhanced Sentiment Analysis of Iranian Restaurant Reviews Utilizing Sentiment Intensity Analyzer \u0026 Fuzzy Logic](https://arxiv.org/abs/2503.12141)\u003cBR\u003e[2025-03-18]\n\n## Image Segmentation(图像分割)\n* [A Comparative Review of the Histogram-based Image Segmentation Methods](https://arxiv.org/abs/2502.18550)\u003cBR\u003e[2025-02-27]\n* [SAM2 for Image and Video Segmentation: A Comprehensive Survey](https://arxiv.org/abs/2503.12781)\u003cBR\u003e[2025-03-18]\n\n## Image Retrieval(图像检索)\n* [A Comprehensive Survey on Composed Image Retrieval](https://arxiv.org/abs/2502.18495)\u003cBR\u003e[2025-02-27]\n* [Composed Multi-modal Retrieval: A Survey of Approaches and Applications](https://arxiv.org/abs/2503.01334)\u003cBR\u003e[2025-03-04]\n\n\n## Image Classification\n* [Plant Leaf Disease Detection and Classification Using Deep Learning: A Review and A Proposed System on Bangladesh's Perspective](https://arxiv.org/abs/2501.03305)\u003cBR\u003e[2025-01-08]基于深度学习的植物叶片病害检测与分类\n\n## Image Super-Resolution\n* [State-of-the-Art Transformer Models for Image Super-Resolution: Techniques, Challenges, and Applications](https://arxiv.org/abs/2501.07855)\u003cBR\u003e[2025-01-15]\n\n## Image Progress(图像/视频处理)\n* 图像增强\n  * [Underwater Image Enhancement using Generative Adversarial Networks: A Survey](https://arxiv.org/abs/2501.06273)\u003cBR\u003e[2025-01-14]\n  * [A Comprehensive Survey on Image Signal Processing Approaches for Low-Illumination Image Enhancement](https://arxiv.org/abs/2502.05995)\u003cBR\u003e[2025-02-11]\n* 图像质量评估/增强  \n  * [Fundus Image Quality Assessment and Enhancement: a Systematic Review](https://arxiv.org/abs/2501.11520)\u003cBR\u003e[2025-01-22]\n  * [A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook](https://arxiv.org/abs/2502.08540)\u003cBR\u003e[2025-02-13]\n* 去反射\n  * [Survey on Single-Image Reflection Removal using Deep Learning Techniques](https://arxiv.org/abs/2502.08836)\u003cBR\u003e[2025-02-14]\n\n## Unknown(未分)\n* [Visualizing Uncertainty in Image Guided Surgery a Review](https://arxiv.org/abs/2501.06280)\u003cBR\u003e[2025-01-14]\n* [A Preliminary Survey of Semantic Descriptive Model for Images](https://arxiv.org/abs/2501.08352)\u003cBR\u003e[2025-01-16]\n* [New Fashion Products Performance Forecasting: A Survey on Evolutions, Models and Emerging Trends](https://arxiv.org/abs/2501.10324)\u003cBR\u003e[2025-01-20]\n* [Explainable artificial intelligence (XAI): from inherent explainability to large language models](https://arxiv.org/abs/2501.09967)\u003cBR\u003e[2025-01-20]\n* [Explainability for Vision Foundation Models: A Survey](https://arxiv.org/abs/2501.12203)\u003cBR\u003e[2025-01-22]\n* [Advanced technology in railway track monitoring using the GPR Technique: A Review](https://arxiv.org/abs/2501.11132)\u003cBR\u003e[2025-01-22]\n* [Reproducibility review of \"Why Not Other Classes\": Towards Class-Contrastive Back-Propagation Explanations](https://arxiv.org/abs/2501.11096)\u003cBR\u003e[2025-01-22]\n* [Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://arxiv.org/abs/2502.05151)\u003cBR\u003e[2025-02-10]\n* [Diffusion Models for Computational Neuroimaging: A Survey](https://arxiv.org/abs/2502.06552)\u003cBR\u003e:star:[code](https://github.com/JoeZhao527/dm4neuro)\u003cBR\u003e[2025-02-11]\n* [Safety at Scale: A Comprehensive Survey of Large Model Safety](https://arxiv.org/abs/2502.05206)\u003cBR\u003e[2025-02-11]\n* [Event Vision Sensor: A Review](https://arxiv.org/abs/2502.06116)\u003cBR\u003e[2025-02-11]\n* [A Survey on Mamba Architecture for Vision Applications](https://arxiv.org/abs/2502.07161)\u003cBR\u003e[2025-02-12]\n* [A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision](https://arxiv.org/abs/2502.10444)\u003cBR\u003e:star:[code](https://github.com/52CV/CV-Surveys/)\u003cBR\u003e[2025-02-18]\n* [Event-based Solutions for Human-centered Applications: A Comprehensive Review](https://arxiv.org/abs/2502.18490)\u003cBR\u003e:star:[code](https://github.com/nmirabeth/event_human)\u003cBR\u003e[2025-02-27]\n* [A Survey on Ordinal Regression: Applications, Advances and Prospects](https://arxiv.org/abs/2503.00952)\u003cBR\u003e[2025-03-04]\n* [Lossy Neural Compression for Geospatial Analytics: A Review](https://arxiv.org/abs/2503.01505)\u003cBR\u003e[2025-03-04]\n* [A Review on Geometry and Surface Inspection in 3D Concrete Printing](https://arxiv.org/abs/2503.07472)\u003cBR\u003e[2025-03-11]\n* [A Systematic Review of ECG Arrhythmia Classification: Adherence to Standards, Fair Evaluation, and Embedded Feasibility](https://arxiv.org/abs/2503.07276)\u003cBR\u003e[2025-03-11]\n* [A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techniques, Datasets, and Future Research Prospects](https://arxiv.org/abs/2503.08008)\u003cBR\u003e[2025-03-12]\n* [Challenges and Trends in Egocentric Vision: A Survey](https://arxiv.org/abs/2503.15275)\u003cBR\u003e[2025-03-20]\n* [A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions](https://arxiv.org/abs/2503.16546)\u003cBR\u003e[2025-03-24]\n* [Hybrid Multi-Stage Learning Framework for Edge Detection: A Survey](https://arxiv.org/abs/2503.21827)\u003cBR\u003e[2025-03-31]\n* [Towards Mobile Sensing with Event Cameras on High-mobility Resource-constrained Devices: A Survey](https://arxiv.org/abs/2503.22943)\u003cBR\u003e[2025-04-01]\n* [Foundation Models For Seismic Data Processing: An Extensive Review](https://arxiv.org/abs/2503.24166)\u003cBR\u003e[2025-04-01]\n* [A Survey of Pathology Foundation Model: Progress and Future Directions](https://arxiv.org/abs/2504.04045)\u003cBR\u003e:star:[code](https://github.com/BearCleverProud/AwesomeWSI)\u003cBR\u003e[2025-04-08]\n* [Attention in Diffusion Model: A Survey](https://arxiv.org/abs/2504.03738)\u003cBR\u003e[2025-04-08]\n* [Loss Functions in Deep Learning: A Comprehensive Review](https://arxiv.org/abs/2504.04242)\u003cBR\u003e[2025-04-08]\n* [Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: a Review](https://arxiv.org/abs/2504.08588)\u003cBR\u003e[2025-04-14]\n* [Computer-Aided Layout Generation for Building Design: A Review](https://arxiv.org/abs/2504.09694)\u003cBR\u003e:star:[code](https://github.com/jcliu0428/awesome-building-layout-generation)\u003cBR\u003e[2025-04-15]\n* [Digital Twin Generation from Visual Data: A Survey](https://arxiv.org/abs/2504.13159)\u003cBR\u003e:star:[code](https://github.com/ndrwmlnk/awesome-digital-twins)\u003cBR\u003e[2025-04-18]\n\n\u003ca name=\"00000\"/\u003e\n\n## 2023 年论文分类汇总戳这里\n↘️[CVPR-2023-Papers](https://github.com/52CV/CVPR-2023-Papers)\n↘️[WACV-2023-Papers](https://github.com/52CV/WACV-2023-Papers)\n↘️[ICCV-2023-Papers](https://github.com/52CV/ICCV-2023-Papers)\n↘️[2023-CV-Surveys](https://github.com/52CV/CV-Surveys/blob/main/2023-CV-Surveys.md)\n\n\u003ca name=\"0000\"/\u003e\n\n## 2022 年论文分类汇总戳这里\n↘️[CVPR-2022-Papers](https://github.com/52CV/CVPR-2022-Papers/blob/main/README.md)\n↘️[WACV-2022-Papers](https://github.com/52CV/WACV-2022-Papers)\n↘️[ECCV-2022-Papers](https://github.com/52CV/ECCV-2022-Papers/blob/main/README.md)\n\n\u003ca name=\"000\"/\u003e\n\n## 2021 年论文分类汇总戳这里\n↘️[ICCV-2021-Papers](https://github.com/52CV/ICCV-2021-Papers)\n↘️[CVPR-2021-Papers](https://github.com/52CV/CVPR-2021-Papers)\n\n\u003ca name=\"00\"/\u003e\n\n## 2020 年论文分类汇总戳这里\n↘️[CVPR-2020-Papers](https://github.com/52CV/CVPR-2020-Papers) \n↘️[ECCV-2020-Papers](https://github.com/52CV/ECCV-2020-Papers)\n\n## 扫码CV君微信（注明：CV）入微信交流群：\n\n![image](https://user-images.githubusercontent.com/62801906/112356924-051e6700-8d0a-11eb-96dd-5c9890832fbf.png)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F52CV%2FCV-Surveys","html_url":"https://awesome.ecosyste.ms/projects/github.com%2F52CV%2FCV-Surveys","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F52CV%2FCV-Surveys/lists"}