source code for CVPR'22 paper "Unknown-Aware Object Detection: Learning What You Don’t Know from Videos in the Wild"

source code for ICLR'22 paper "VOS: Learning What You Don’t Know by Virtual Outlier Synthesis"

Source code for Self-supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes (AAAI 2024)

Out-of-distribution detection, robustness, and generalization resources. The repository contains a professionally curated list of papers, tutorials, books, videos, articles and open-source librarie…

Official Implementation of the paper: YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detection (CVPR24)

[ICCV'23] Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation

Official implementation of paper "DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition"

[ICCV'23 Oral] Unmasking Anomalies in Road-Scene Segmentation

[ECCV'22 Oral] Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes

Fast Segment Anything

[Arxiv] Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead.

Enjoy the magic of Diffusion models!

This repository is for the first comprehensive survey on Meta AI's Segment Anything Model (SAM).

Official code for RbA: Segmenting Unknown Regions Rejected by All (ICCV 2023)

Code for CVPR2024 'Segment Every Out-of-Distribution Object '

AD4AD: Anomaly Detection for Autonomous Driving

Unofficial implementation of EfficientAD https://arxiv.org/abs/2303.14535

Anomaly Detection via Reverse Distillation from One-Class Embedding

[AAAI 2024] AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model

go语言的分布式计算系统(python中的Celery go实现)

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Official PyTorch implementation of the paper “Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection”, open-set anomaly detection, few-shot anomaly detection.

(CVPR 2023) Revisiting Reverse Distillation for Anomaly Detection

Inspection of Power Line Assets Dataset (InsPLAD)

Code for the Paper "Improving Diffusion Model Efficiency Through Patching"

Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning, CVPR 2021