[ECCV2024] Mitigating Background Shift in Class-Incremental Semantic Segmentation
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Updated
Jul 19, 2024 - Python
[ECCV2024] Mitigating Background Shift in Class-Incremental Semantic Segmentation
You can’t handle the (dirty) truth: Data-centric insights improve pseudo-labeling
A full pipeline AutoML tool for tabular data
Pseudo-labeling for tabular data
Unpublished paper focusing on the effects of pseudo-labeling without specialized models and supporting algorithms. Includes presentation slides, paper, experiments and task-specific preprocessing/pseudo-labeling library.
The dataset for the paper 'Learning self-supervised traversability with navigation experiences of mobile robots: A risk-aware self-training approach'
[IJCAI 2023] Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation
auto_labeler - An all-in-one library to automatically label vision data
This repository contains the implementation of the Self Meta Pseudo Labels (SMPL) method for semi-supervised learning
Code for my paper "Semi-Supervised Unconstrained Head Pose Estimation in the Wild"
code released for our ICML 2020 paper "Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation"
This repo contains implementation of uncertainty estimation, rectification, and minimization for guiding the pseudo-label learning in semi-supervised defect segmentation setting.
[IJCAI 2022] Official Pytorch code for paper “S2 Transformer for Image Captioning”
[NeurIPS 2022] Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering
This repo contains implementation of semi-supervised defect segmentation based on pairwise similarity map consistency and ensemble-based cross pseudo labels
[IJCAI 2023] Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation
The main objective of this repository is to become familiar with the task of Domain Adaptation applied to the Real-time Semantic Segmentation networks.
"In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning" by Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat, Mubarak Shah (ICLR 2021)
Federated Semantic Segmentation with Fourier Domain Adaptation and Pseudo-labelling
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