Implementation of ICCV'23 paper "Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation"
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Updated
Jan 5, 2023 - Python
Implementation of ICCV'23 paper "Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation"
[IJCV 2022] Pytorch codes for Open-set Adversarial Defense with Clean-Adversarial Mutual Learning
PyTorch implementation of our CVPR 2024 paper "Unified Entropy Optimization for Open-Set Test-Time Adaptation"
Python code for detecting and learning new classes of threats present in crops
Source code of PRJ paper "Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds"
Code Implementation of "Unsupervised Recognition of Unknown Objects for Open-World Object Detection"
1st Place Code for FungiCLEF 2023 Competition from UstcAIGroup
A toolbox for one-class classification and open set recognition based on intra-class splitting
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, semi-supervised anomaly detection.
[ECCV'22 Oral] Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes. Dealing with out-of-distribution detection or open-set recognition in semantic segmentation.
Fooling Machine Learning Models: A Novel Out-of-Distribution Attack through Generative Adversarial Networks
VLG: General Video Recognition with Web Textual Knowledge (https://arxiv.org/abs/2212.01638)
Implementation of CVPR'23 paper "Glocal Energy-based Learning for Few-Shot Open-Set Recognition"
This repository contains the code used to create the results presented in the paper: "From Coarse to Fine-Grained Open-Set Recognition". We investigate the role of label granularity, semantic similarity, and hierarchical representations in open-set recognition (OSR) with an OSR-benchmark based on iNat2021.
Official PyTorch implementation for "SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection"
Official code for paper "OpenCIL: Benchmarking Out-of-Distribution Detection in Class-Incremental Learning"
Source code for baseline obtenience
[CVPR 2022 Oral] Towards Open Set Temporal Action Localization
This is a novel unknown sar target identification method based on feature extraction networks and KLD-RPA joint discrimination. Experiment results form MSTAR dataset shows that our proposed Fea-DA achieves state of the art unknown sar target identification accuracy while maintaining the high recognition accuracy of known target.
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