jam-cc/MMAD

Accompanying code for the ICCV2025 paper "MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning"

[JMS 2026] A Comprehensive Survey for Real-World Industrial Surface Defect Detection: Challenges, Approaches, and Prospects (Journal of Manufacturing Systems)

[ICML '26] Code, Data and Red Teaming for ZeroBench

Paper list and datasets for industrial image anomaly/defect detection (updating). 工业异常/瑕疵检测论文及数据集检索库(持续更新)。

Vanilla torch and timm industrial knn-based anomaly detection for images.

[CVPR 2025 HIghlight] XLRS-Bench: ould Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?

UniMMAD: Unified Multi-Modal and Multi-Class Anomaly Detection via MoE-Driven Feature Decompression

VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer (CVPR 2026)

Open-source evaluation toolkit of large multi-modality models (LMMs), support 220+ LMMs, 80+ benchmarks