[CVPR 2023] Pytorch Implementation for CVPR2023 paper: Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection

[CVPR 2025] official implementation of “Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection”

[NeurIPS 2024 Spotlight] Pytorch Implementation for NeurIPS 2024 paper: ResAD: A Simple Framework for Class Generalizable Anomaly Detection

An anomaly detection dataset (named GTanoIC) consisting of seven types of chips, including 1750 real non-defective samples, 470 real defective samples, and pixel-level annotations..

[Nature Machine Intelligence 2025] Emulating Human-like Adaptive Vision for Efficient and Flexible Machine Visual Perception

This is a large Chip-surface-defect-dataset, with 2270 real non-defective samples, 1241 real defective samples, and 7250 synthetic defective samples.

Content-Adaptive Downsampling in Convolutional Neural Networks (CVPR 2023 Workshop on Efficient Deep Learning for Computer Vision)

Normal-Abnormal Guided Generalist Anomaly Detection (NeurIPS 2025)

A PCB defect dataset.

Multi-Prototype Collaborative Perception Enhancement Network for Few-shot Semantic Segmentation

Code for our ICCV 2019 paper PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment

[AAAI 2024 Oral] AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models

The Code of Mask-Guided Correlation Learning for Few-Shot Segmentation in Remote Sensing Imagery (IEEE Transactions on Geoscience and Remote Sensing 2024)

Codes and dataset (iSAID-5i) for Scale-aware Detailed Matching for Few-Shot Aerial Image Semantic Segmentation

[TMM'25] AFANet: Adaptive Frequency-Aware Network for Weakly-Supervised Few-Shot Semantic Segmentation

The official implementation of RoBiS for the CVPR2025 VAND3.0 challenge Track 1.

[TASE 2024] AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

[CVPR 2025] Official Implementation of "Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection". The first multi-class UAD model that can compete with single-class SOTAs

Image anomaly detection benchmark in industrial manufacturing

[ICCV ADFM'25] ADer is an open source visual anomaly detection toolbox based on PyTorch, which supports multiple popular AD datasets and approaches.

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)。

Frequency-aware Image Restoration for Industrial Visual anomaly detection

[ICCV 2025] SALAD -- Semantics-Aware Logical Anomaly Detection