计算机视觉：异常检测(paper with code汇总更新中）

豆芽8192025-12-15 11:41

本文汇总异常检测相关论文，持续更新，所有内容均为开源，欢迎交流学习！

  1. 【异常检测】（WACV2026）VLMDiff: Leveraging Vision-Language Models for Multi-Class Anomaly Detection with Diffusion论文地址：https://arxiv.org//pdf/25108173开源代码：[https://github.com/giddyyupp/VLMDiff](https://github.com/giddyyupp/VLMDiff "https://github.com/giddyyupp/VLMDiff")
  2. 【异常检测】UniADC: A Unified Framework for Anomaly Detection and Classification论文地址：https://arxiv.org//pdf/2511.06644开源代码（即将开源）：[https://github.com/cnulab/UniADC](https://github.com/cnulab/UniADC "https://github.com/cnulab/UniADC")
  3. 【异常检测】（AAAI2026）Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory论文地址：https://arxiv.org//pdf/2511.05966开源代码：[https://github.com/Sunny5250/CIF](https://github.com/Sunny5250/CIF "https://github.com/Sunny5250/CIF")
  4. 【Diffusion】FreeSliders: Training-Free, Modality-Agnostic Concept Sliders for Fine-Grained Diffusion Control in Images, Audio, and Video论文地址：https://arxiv.org//pdf/2511.00103工程主页：https://azencot-group.github.io/FreeSliders/开源代码：[https://github.com/azencot-group/Free_Sliders](https://github.com/azencot-group/Free_Sliders "https://github.com/azencot-group/Free_Sliders")
  5. 【视频异常检测】（NeurIPS2025）A Unified Reasoning Framework for Holistic Zero-Shot Video Anomaly Analysis论文地址：https://arxiv.org//pdf/2511.00962工程主页：https://rathgrith.github.io/Unified_Frame_VAA/开源代码：[https://github.com/Rathgrith/NeurIPS_2025-URF-ZS-HVAA](https://github.com/Rathgrith/NeurIPS_2025-URF-ZS-HVAA "https://github.com/Rathgrith/NeurIPS_2025-URF-ZS-HVAA")
  6. 【Diffusion】H2-Cache: A Novel Hierarchical Dual-Stage Cache for High-Performance Acceleration of Generative Diffusion Models论文地址：https://arxiv.org//pdf/2510.27171开源代码：[https://github.com/Bluear7878/H2-cache-A-Hierarchical-Dual-Stage-Cache](https://github.com/Bluear7878/H2-cache-A-Hierarchical-Dual-Stage-Cache "https://github.com/Bluear7878/H2-cache-A-Hierarchical-Dual-Stage-Cache")
  7. 【视频异常检测】（NeurIPS2025）VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Tree论文地址：https://arxiv.org//pdf/2510.22693开源代码（即将开源）：[https://github.com/wenlongli10/VADTree](https://github.com/wenlongli10/VADTree "https://github.com/wenlongli10/VADTree")
  8. 【异常检测】（DICTA2025）2D_3D Feature Fusion via Cross-Modal Latent Synthesis and Attention Guided Restoration for Industrial Anomaly Detection论文地址：https://arxiv.org//pdf/2510.21793开源代码：[https://github.com/adabrh/MAFR](https://github.com/adabrh/MAFR "https://github.com/adabrh/MAFR")
  9. 【图像修复】Residual Diffusion Bridge Model for Image Restoration论文地址：https://arxiv.org//pdf/2510.23116开源代码：[https://github.com/MiliLab/RDBM](https://github.com/MiliLab/RDBM "https://github.com/MiliLab/RDBM")
  10. 【视频异常检测】MoniTor: Exploiting Large Language Models with Instruction for Online Video Anomaly Detection论文地址：https://arxiv.org//pdf/2521449开源代码：[https://github.com/YsTvT/MoniTor](https://github.com/YsTvT/MoniTor "https://github.com/YsTvT/MoniTor")
  11. 【Diffusion】A Survey on Cache Methods in Diffusion Models: Toward Efficient Multi-Modal Generation论文地址：https://arxiv.org//pdf/2510.19755开源代码：[https://github.com/Shenyi-Z/Cache4Diffusion](https://github.com/Shenyi-Z/Cache4Diffusion "https://github.com/Shenyi-Z/Cache4Diffusion")
  12. 【伪装目标检测】（ICCV2025）Beyond Single Images: Retrieval Self-Augmented Unsupervised Camouflaged Object Detection论文地址：https://arxiv.org//pdf/2510.18437开源代码：[https://github.com/xiaohainku/RISE](https://github.com/xiaohainku/RISE "https://github.com/xiaohainku/RISE")
  13. 【异常检测】IAD-GPT: Advancing Visual Knowledge in Multimodal Large Language Model for Industrial Anomaly Detection论文地址：https://arxiv.org//pdf/2510.16036开源代码（即将开源）：[https://github.com/LiZeWen1225/IAD-GPT](https://github.com/LiZeWen1225/IAD-GPT "https://github.com/LiZeWen1225/IAD-GPT")
  14. 【异常检测】Rebellious Student: A Complementary Learning Framework for Background Feature Enhancement in Hyperspectral Anomaly Detection论文地址：https://arxiv.org//pdf/2510.18781开源代码：[https://github.com/xjpp2016/FERS](https://github.com/xjpp2016/FERS "https://github.com/xjpp2016/FERS")
  15. 【异常检测】（NeurIPS2025）Registration is a Powerful Rotation-Invariance Learner for 3D Anomaly Detection论文地址：https://arxiv.org//pdf/2510.16865开源代码：[https://github.com/CHen-ZH-W/Reg2Inv](https://github.com/CHen-ZH-W/Reg2Inv "https://github.com/CHen-ZH-W/Reg2Inv")
  16. 【异常检测】MIRAD - A comprehensive real-world robust anomaly detection dataset for Mass Individualization论文地址：https://arxiv.org//pdf/2510.16370开源代码：[https://github.com/wu33learn/MIRAD](https://github.com/wu33learn/MIRAD "https://github.com/wu33learn/MIRAD")
  17. 【异常检测】One Dinomaly2 Detect Them All: A Unified Framework for Full-Spectrum Unsupervised Anomaly Detection论文地址：https://arxiv.org//pdf/2510.17611开源代码：[https://github.com/guojiajeremy/Dinomaly](https://github.com/guojiajeremy/Dinomaly "https://github.com/guojiajeremy/Dinomaly")
  18. 【异常检测】（VMV2025）Quantized FCA: Efficient Zero-Shot Texture Anomaly Detection论文地址：https://arxiv.org//pdf/2510.15602工程主页：https://reality.tf.fau.de/publications/2025/ardelean2025quantized/ardelean2025quantized.html开源代码：[https://github.com/TArdelean/QuantizedFCA](https://github.com/TArdelean/QuantizedFCA "https://github.com/TArdelean/QuantizedFCA")
  19. 【缺陷检测】Self-Supervised Multi-Scale Transformer with Attention-Guided Fusion for Efficient Crack Detection论文地址：https://arxiv.org//pdf/2510.10378开源代码：[https://github.com/Blessing988/Crack-Segmenter](https://github.com/Blessing988/Crack-Segmenter "https://github.com/Blessing988/Crack-Segmenter")
  20. 【异常检测】On the Problem of Consistent Anomalies in Zero-Shot Industrial Anomaly Detection论文地址：https://arxiv.org//pdf/2510.10456开源代码：[https://github.com/DumBringer/CoDeGraph](https://github.com/DumBringer/CoDeGraph "https://github.com/DumBringer/CoDeGraph")
  21. 【强化学习】Reinforcing Diffusion Models by Direct Group Preference Optimization论文地址：https://arxiv.org//pdf/2510.08425开源代码（即将开源）：[https://github.com/Luo-Yihong/DGPO](https://github.com/Luo-Yihong/DGPO "https://github.com/Luo-Yihong/DGPO")
  22. 【强化学习】Spotlight on Token Perception for Multimodal Reinforcement Learning论文地址：https://arxiv.org//pdf/2510.09285开源代码：[https://github.com/huaixuheqing/VPPO-RL](https://github.com/huaixuheqing/VPPO-RL "https://github.com/huaixuheqing/VPPO-RL")
  23. 【缺陷检测】Automated Neural Architecture Design for Industrial Defect Detection论文地址：https://arxiv.org//pdf/2510.06669开源代码（即将开源）：[https://github.com/Yuxi104/AutoNAD](https://github.com/Yuxi104/AutoNAD "https://github.com/Yuxi104/AutoNAD")
  24. 【异常检测】Unified Unsupervised Anomaly Detection via Matching Cost Filtering论文地址：https://arxiv.org//pdf/2510.03363开源代码：[https://github.com/ZHE-SAPI/CostFilter-AD](https://github.com/ZHE-SAPI/CostFilter-AD "https://github.com/ZHE-SAPI/CostFilter-AD")
  25. 【Diffusion】SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization论文地址：https://arxiv.org//pdf/2510.04961开源代码：[https://github.com/facebookresearch/SSDD](https://github.com/facebookresearch/SSDD "https://github.com/facebookresearch/SSDD")
  26. 【Diffusion】TAG:Tangential Amplifying Guidance for Hallucination-Resistant Diffusion Sampling论文地址：https://arxiv.org//pdf/2510.04533工程主页：https://hyeon-cho.github.io/TAG/开源代码：[https://github.com/hyeon-cho/Tangential-Amplifying-Guidance](https://github.com/hyeon-cho/Tangential-Amplifying-Guidance "https://github.com/hyeon-cho/Tangential-Amplifying-Guidance")
  27. 【异常检测】Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors论文地址：https://arxiv.org//pdf/2510.01934开源代码：[https://github.com/ymxlzgy/FoundAD](https://github.com/ymxlzgy/FoundAD "https://github.com/ymxlzgy/FoundAD")
  28. 【异常检测】（NeurIPS2025）Normal-Abnormal Guided Generalist Anomaly Detection论文地址：https://arxiv.org//pdf/2510.00495开源代码：[https://github.com/JasonKyng/NAGL](https://github.com/JasonKyng/NAGL "https://github.com/JasonKyng/NAGL")
  29. 【异常检测】UniMMAD: Unified Multi-Modal and Multi-Class Anomaly Detection via MoE-Driven Feature Decompression论文地址：https://arxiv.org//pdf/2509.25934开源代码（即将开源）：[https://github.com/yuanzhao-CVLAB/UniMMAD](https://github.com/yuanzhao-CVLAB/UniMMAD "https://github.com/yuanzhao-CVLAB/UniMMAD")
  30. 【视频异常检测】（NeurIPS2025）PANDA: Towards Generalist Video Anomaly Detection via Agentic AI Engineer论文地址：https://arxiv.org//pdf/2509.26386开源代码：[https://github.com/showlab/PANDA](https://github.com/showlab/PANDA "https://github.com/showlab/PANDA")
  31. 【Diffusion】SAIP: A Plug-and-Play Scale-adaptive Module in Diffusion-based Inverse Problems论文地址：https://arxiv.org//pdf/2509.24580开源代码：[https://github.com/seulaugues/SAIPcode](https://github.com/seulaugues/SAIPcode "https://github.com/seulaugues/SAIPcode")
  32. 【遥感大模型】SAR-KnowLIP: Towards Multimodal Foundation Models for Remote Sensing论文地址：https://arxiv.org//pdf/2509.23927开源代码（即将开源）：[https://github.com/yangyifremad/SARKnowLIP](https://github.com/yangyifremad/SARKnowLIP "https://github.com/yangyifremad/SARKnowLIP")
  33. 【遥感大模型】Towards Faithful Reasoning in Remote Sensing: A Perceptually-Grounded GeoSpatial Chain-of-Thought for Vision-Language Models论文地址：https://arxiv.org//pdf/2509.22221开源代码：[https://github.com/minglangL/RSThinker](https://github.com/minglangL/RSThinker "https://github.com/minglangL/RSThinker")
  34. 【强化学习】SPARK: Synergistic Policy And Reward Co-Evolving Framework论文地址：https://arxiv.org//pdf/2509.22624开源代码：[https://github.com/InternLM/Spark](https://github.com/InternLM/Spark "https://github.com/InternLM/Spark")
  35. 【Diffusion】（ICCV2025）From Prompt to Progression: Taming Video Diffusion Models for Seamless Attribute Transition论文地址：https://arxiv.org//pdf/2509.19690开源代码：[https://github.com/lynn-ling-lo/Prompt2Progression](https://github.com/lynn-ling-lo/Prompt2Progression "https://github.com/lynn-ling-lo/Prompt2Progression")
  36. 【缺陷检测】Advancing Metallic Surface Defect Detection via Anomaly-Guided Pretraining on a Large Industrial Dataset论文地址：https://arxiv.org//pdf/2509.18919工程主页：https://clovermini.github.io/AGSSP-Dev/开源代码：[https://github.com/clovermini/AGSSP](https://github.com/clovermini/AGSSP "https://github.com/clovermini/AGSSP")
  37. 【医学图像异常检测】（MICCAI2025）Anomaly Detection by Clustering DINO Embeddings using a Dirichlet Process Mixture论文地址：https://arxiv.org//pdf/2509.19997开源代码：[https://github.com/NicoSchulthess/anomalydino-dpmm](https://github.com/NicoSchulthess/anomalydino-dpmm "https://github.com/NicoSchulthess/anomalydino-dpmm")
  38. 【医学图像异常检测】（MICCAI2025）Anomaly Detection by Clustering DINO Embeddings using a Dirichlet Process Mixture论文地址：https://arxiv.org//pdf/2509.19997开源代码：[https://github.com/NicoSchulthess/anomalydino-dpmm](https://github.com/NicoSchulthess/anomalydino-dpmm "https://github.com/NicoSchulthess/anomalydino-dpmm")
  39. 【Diffusion】Kuramoto Orientation Diffusion Models论文地址：https://arxiv.org//pdf/2509.15328开源代码：[https://github.com/KingJamesSong/OrientationDiffusion](https://github.com/KingJamesSong/OrientationDiffusion "https://github.com/KingJamesSong/OrientationDiffusion")
  40. 【图像生成】Understand Before You Generate: Self-Guided Training for Autoregressive Image Generation论文地址：https://arxiv.org//pdf/2509.15185开源代码（即将开源）：[https://github.com/yuexy/ST-AR](https://github.com/yuexy/ST-AR "https://github.com/yuexy/ST-AR")
  41. 【Diffusion】Compute Only 16 Tokens in One Timestep: Accelerating Diffusion Transformers with Cluster-Driven Feature Caching论文地址：https://arxiv.org//pdf/2509.10312开源代码：[https://github.com/Shenyi-Z/Cache4Diffusion](https://github.com/Shenyi-Z/Cache4Diffusion "https://github.com/Shenyi-Z/Cache4Diffusion")
  42. 【异常检测】AD-DINOv3: Enhancing DINOv3 for Zero-Shot Anomaly Detection with Anomaly-Aware Calibration论文地址：https://arxiv.org//pdf/2509.14084开源代码（即将开源）：[https://github.com/Kaisor-Yuan/AD-DINOv3](https://github.com/Kaisor-Yuan/AD-DINOv3 "https://github.com/Kaisor-Yuan/AD-DINOv3")
  43. 【基础网络架构：Mamba】VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation论文地址：https://arxiv.org//pdf/2509.04669开源代码：[https://github.com/Wertyuui345/VCMamba](https://github.com/Wertyuui345/VCMamba "https://github.com/Wertyuui345/VCMamba")
  44. 【异常检测】（ICIAP 2025）Efficient Odd-One-Out Anomaly Detection论文地址：https://arxiv.org//pdf/2509.04326工程主页：https://silviochito.github.io/EfficientOddOneOut/开源代码：[https://github.com/SilvioChito/EfficientOddOneOut](https://github.com/SilvioChito/EfficientOddOneOut "https://github.com/SilvioChito/EfficientOddOneOut")
  45. 【图像生成】FocusDPO: Dynamic Preference Optimization for Multi-Subject Personalized Image Generation via Adaptive Focus论文地址：https://arxiv.org//pdf/2509.01181工程主页：https://bytedance-fanqie-ai.github.io/FocusDPO/开源代码（即将开源）：[https://github.com/bytedance-fanqie-ai/FocusDPO](https://github.com/bytedance-fanqie-ai/FocusDPO "https://github.com/bytedance-fanqie-ai/FocusDPO")
  46. 【图像增强】Unsupervised Ultra-High-Resolution UAV Low-Light Image Enhancement: A Benchmark, Metric and Framework论文地址：https://arxiv.org//pdf/2509.01373开源代码（即将开源）：[https://github.com/lwCVer/U3D](https://github.com/lwCVer/U3D "https://github.com/lwCVer/U3D")
  47. 【异常检测】SALAD -- Semantics-Aware Logical Anomaly Detection论文地址：https://arxiv.org//pdf/2509.02101开源代码：[https://github.com/MaticFuc/SALAD](https://github.com/MaticFuc/SALAD "https://github.com/MaticFuc/SALAD")
  48. 【基础网络架构：Transformer】LF-ViT: Reducing Spatial Redundancy in Vision Transformer for Efficient Image Recognition论文地址：https://arxiv.org//pdf/2402.00033开源代码：[https://github.com/edgeai1/LF-ViT](https://github.com/edgeai1/LF-ViT "https://github.com/edgeai1/LF-ViT")
  49. 【Diffusion】AToM: Amortized Text-to-Mesh using 2D Diffusion论文地址：https://arxiv.org//pdf/2402.00867工程主页：[https://snap-research.github.io/AToM/](https://snap-research.github.io/AToM/ "https://snap-research.github.io/AToM/")
  50. 【Diffusion】AnimateLCM: Accelerating the Animation of Personalized Diffusion Models and Adapters with Decoupled Consistency Learning论文地址：https://arxiv.org//pdf/2402.00769工程主页：https://animatelcm.github.io/开源代码：[https://github.com/G-U-N/AnimateLCM](https://github.com/G-U-N/AnimateLCM "https://github.com/G-U-N/AnimateLCM")
  51. 【图像增强】LYT-Net: Lightweight YUV Transformer-based Network for Low-Light Image Enhancement论文地址：https://arxiv.org//pdf/2401.15204开源代码：[https://github.com/albrateanu/LYT-Net](https://github.com/albrateanu/LYT-Net "https://github.com/albrateanu/LYT-Net")
  52. 【多模态】Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation论文地址：https://arxiv.org//pdf/2401.15688工程主页：https://zhenyuw16.github.io/CompAgent/开源代码（即将开源）：[https://github.com/zhenyuw16/CompAgent_code](https://github.com/zhenyuw16/CompAgent_code "https://github.com/zhenyuw16/CompAgent_code")
  53. 【Diffusion】Diffutoon: High-Resolution Editable Toon Shading via Diffusion Models论文地址：https://arxiv.org//pdf/2401.16224工程主页：https://ecnu-cilab.github.io/DiffutoonProjectPage/开源代码：[https://github.com/Artiprocher/DiffSynth-Studio](https://github.com/Artiprocher/DiffSynth-Studio "https://github.com/Artiprocher/DiffSynth-Studio")
  54. 【基础网络架构：Transformer】Rethinking Patch Dependence for Masked Autoencoders论文地址：https://arxiv.org//pdf/2401.14391工程主页：https://crossmae.github.io/开源代码：[https://github.com/TonyLianLong/CrossMAE](https://github.com/TonyLianLong/CrossMAE "https://github.com/TonyLianLong/CrossMAE")
  55. 【Diffusion】Diffuse to Choose: Enriching Image Conditioned Inpainting in Latent Diffusion Models for Virtual Try-All论文地址：https://arxiv.org//pdf/2401.13795工程主页：[https://diffuse2choose.github.io/](https://diffuse2choose.github.io/ "https://diffuse2choose.github.io/")
  56. 【基础网络架构：Transformer】Convolutional Initialization for Data-Efficient Vision Transformers论文地址：https://arxiv.org//pdf/2401.12511开源代码：[https://github.com/osiriszjq/impulse_init](https://github.com/osiriszjq/impulse_init "https://github.com/osiriszjq/impulse_init")
  57. 【基础网络架构：CNN】Shift-ConvNets: Small Convolutional Kernel with Large Kernel Effects论文地址：https://arxiv.org//pdf/2401.12736开源代码：[https://github.com/lidc54/shift-wiseConv](https://github.com/lidc54/shift-wiseConv "https://github.com/lidc54/shift-wiseConv")
  58. 【异常分割】ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation论文地址：https://arxiv.org//pdf/2401.12665开源代码（即将开源）：[https://github.com/Lszcoding/ClipSAM](https://github.com/Lszcoding/ClipSAM "https://github.com/Lszcoding/ClipSAM")
  59. 【Diffusion】Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs论文地址：https://arxiv.org//pdf/2401.11708开源代码：[https://github.com/YangLing0818/RPG-DiffusionMaster](https://github.com/YangLing0818/RPG-DiffusionMaster "https://github.com/YangLing0818/RPG-DiffusionMaster")
  60. 【基础网络架构：Transformer】Understanding Video Transformers via Universal Concept Discovery论文地址：https://arxiv.org//pdf/2401.10831工程主页：[https://yorkucvil.github.io/VTCD/](https://yorkucvil.github.io/VTCD/ "https://yorkucvil.github.io/VTCD/")
  61. 【基础网络架构】VMamba: Visual State Space Model论文地址：https://arxiv.org//pdf/2401.10166开源代码：[https://github.com/MzeroMiko/VMamba](https://github.com/MzeroMiko/VMamba "https://github.com/MzeroMiko/VMamba")
  62. 【Diffusion】DiffusionGPT: LLM-Driven Text-to-Image Generation System论文地址：https://arxiv.org//pdf/2401.10061工程主页：https://diffusiongpt.github.io/开源代码：[https://github.com/DiffusionGPT/DiffusionGPT](https://github.com/DiffusionGPT/DiffusionGPT "https://github.com/DiffusionGPT/DiffusionGPT")
  63. 【Diffusion】（ICLR2024）Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image Synthesis论文地址：https://arxiv.org//pdf/2401.09048开源代码：[https://github.com/tomtom1103/compose-and-conquer](https://github.com/tomtom1103/compose-and-conquer "https://github.com/tomtom1103/compose-and-conquer")
  64. 【Diffusion】VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models论文地址：https://arxiv.org//pdf/2401.09047工程主页：https://ailab-cvc.github.io/videocrafter2/开源代码：[https://github.com/AILab-CVC/VideoCrafter](https://github.com/AILab-CVC/VideoCrafter "https://github.com/AILab-CVC/VideoCrafter")
  65. 【Diffusion】Fixed Point Diffusion Models论文地址：https://arxiv.org//pdf/2401.08741工程主页：https://lukemelas.github.io/fixed-point-diffusion-models/代码即将开源
  66. 【基础网络架构：CNN】Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications论文地址：https://arxiv.org//pdf/2401.06197开源代码：[https://github.com/OpenGVLab/DCNv4](https://github.com/OpenGVLab/DCNv4 "https://github.com/OpenGVLab/DCNv4")

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