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20212026
most citedInter-Domain Mixup for Semi-Supervised Domain Adaptation

32 citations · 71 across the 12 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.CV2024

Style-Preserving Lip Sync via Audio-Aware Style Reference

Weizhi Zhong, Jichang Li, Yinqi Cai +4

Audio-driven lip sync has recently drawn significant attention due to its widespread application in the multimedia domain. Individuals exhibit distinct lip shapes when speaking the…

cs.CV2024

Learning Background Prompts to Discover Implicit Knowledge for Open Vocabulary Object Detection

Jiaming Li, Jiacheng Zhang, Jichang Li +4

Open vocabulary object detection (OVD) aims at seeking an optimal object detector capable of recognizing objects from both base and novel categories. Recent advances leverage knowl…

cs.CV2024★ 1 cited

AlignSAM: Aligning Segment Anything Model to Open Context via Reinforcement Learning

Duojun Huang, Xinyu Xiong, Jie Ma +4

Powered by massive curated training data, Segment Anything Model (SAM) has demonstrated its impressive generalization capabilities in open-world scenarios with the guidance of prom…

cs.CV2024★ 32 cited

Inter-Domain Mixup for Semi-Supervised Domain Adaptation

Jichang Li, Guanbin Li, Yizhou Yu

Semi-supervised domain adaptation (SSDA) aims to bridge source and target domain distributions, with a small number of target labels available, achieving better classification perf…

cs.CV2024★ 21 cited

Adaptive Betweenness Clustering for Semi-Supervised Domain Adaptation

Jichang Li, Guanbin Li, Yizhou Yu

Compared to unsupervised domain adaptation, semi-supervised domain adaptation (SSDA) aims to significantly improve the classification performance and generalization capability of t…