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UniTTA: Unified Benchmark and Versatile Framework Towards Realistic Test-Time Adaptation
Chaoqun Du, Yulin Wang, Jiayi Guo +3
Test-Time Adaptation (TTA) aims to adapt pre-trained models to the target domain during testing. In reality, this adaptability can be influenced by multiple factors. Researchers ha…
Everything to the Synthetic: Diffusion-driven Test-time Adaptation via Synthetic-Domain Alignment
Jiayi Guo, Junhao Zhao, Chaoqun Du +6
Test-time adaptation (TTA) aims to improve the performance of source-domain pre-trained models on previously unseen, shifted target domains. Traditional TTA methods primarily adapt…
Probabilistic Contrastive Learning for Long-Tailed Visual Recognition
Chaoqun Du, Yulin Wang, Shiji Song +1
Long-tailed distributions frequently emerge in real-world data, where a large number of minority categories contain a limited number of samples. Such imbalance issue considerably i…
SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning
Chaoqun Du, Yizeng Han, Gao Huang
Recent advancements in semi-supervised learning have focused on a more realistic yet challenging task: addressing imbalances in labeled data while the class distribution of unlabel…