How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation

1 code implementation • 12 Dec 2023 • Zhongyi Han, Guanglin Zhou, Rundong He, Jindong Wang, Tailin Wu, Yilong Yin, Salman Khan, Lina Yao, Tongliang Liu, Kun Zhang

We further investigate its adaptability to controlled data perturbations and examine the efficacy of in-context learning as a tool to enhance its adaptation.

Anomaly Detection Autonomous Driving +6

MHPL: Minimum Happy Points Learning for Active Source Free Domain Adaptation

no code implementations • CVPR 2023 • Fan Wang, Zhongyi Han, Zhiyan Zhang, Rundong He, Yilong Yin

Source free domain adaptation (SFDA) aims to transfer a trained source model to the unlabeled target domain without accessing the source data.

Active Learning Source-Free Domain Adaptation

Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection

no code implementations • 16 Sep 2022 • Rundong He, Rongxue Li, Zhongyi Han, Yilong Yin

Based on limited ID labeled data and sufficient unlabeled data, we define a new setting called Weakly-Supervised Out-of-Distribution Detection (WSOOD).

Contrastive Learning Out-of-Distribution Detection +1

Safe-Student for Safe Deep Semi-Supervised Learning With Unseen-Class Unlabeled Data

no code implementations • CVPR 2022 • Rundong He, Zhongyi Han, Xiankai Lu, Yilong Yin

To take advantage of these unseen-class data and ensure performance, we propose a safe SSL method called SAFE-STUDENT from the teacher-student view.