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"
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"
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.
Code Implementation of "Unsupervised Recognition of Unknown Objects for Open-World Object 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.
Implementation of CVPR'23 paper "Glocal Energy-based Learning for Few-Shot Open-Set Recognition"
Fooling Machine Learning Models: A Novel Out-of-Distribution Attack through Generative Adversarial Networks
Official PyTorch implementation for "SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection"
[IJCV 2022] Pytorch codes for Open-set Adversarial Defense with Clean-Adversarial Mutual Learning
1st Place Code for FungiCLEF 2023 Competition from UstcAIGroup
Source code for baseline obtenience
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 code for paper "OpenCIL: Benchmarking Out-of-Distribution Detection in Class-Incremental Learning"
VLG: General Video Recognition with Web Textual Knowledge (https://arxiv.org/abs/2212.01638)
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.
Interactive Skeleton Based Few Shot Action Recognition
PyTorch implementation of our CVPR 2024 paper "Unified Entropy Optimization for Open-Set Test-Time Adaptation"
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