1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CV2024★ 1 cited
GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning
Sanqing Qu, Tianpei Zou, Florian Röhrbein +4
Deep neural networks often exhibit sub-optimal performance under covariate and category shifts. Source-Free Domain Adaptation (SFDA) presents a promising solution to this dilemma,…
cs.CV2023★ 1 cited
Upcycling Models under Domain and Category Shift
Sanqing Qu, Tianpei Zou, Florian Roehrbein +4
Deep neural networks (DNNs) often perform poorly in the presence of domain shift and category shift. How to upcycle DNNs and adapt them to the target task remains an important open…
cs.CV2020
Approximated Bilinear Modules for Temporal Modeling
Xinqi Zhu, Chang Xu, Langwen Hui +2
We consider two less-emphasized temporal properties of video: 1. Temporal cues are fine-grained; 2. Temporal modeling needs reasoning. To tackle both problems at once, we exploit a…