Showing cs.CVShow all
3 papers · 1 filter
cs.CV2026
Closing the Confusion Loop: CLIP-Guided Alignment for Source-Free Domain Adaptation
Shanshan Wang, Ziying Feng, Xiaozheng Shen +4
Source-Free Domain Adaptation (SFDA) tackles the problem of adapting a pre-trained source model to an unlabeled target domain without accessing any source data, which is quite suit…
cs.CV2025
Single-Domain Generalized Object Detection by Balancing Domain Diversity and Invariance
Zhenwei He, Hongsu Ni
Single-domain generalization for object detection (S-DGOD) seeks to transfer learned representations from a single source domain to unseen target domains. While recent approaches h…
cs.CV2024
Gradually Vanishing Gap in Prototypical Network for Unsupervised Domain Adaptation
Shanshan Wang, Hao Zhou, Xun Yang +4
Unsupervised domain adaptation (UDA) is a critical problem for transfer learning, which aims to transfer the semantic information from labeled source domain to unlabeled target dom…