Yueting Zhuang
Papers
2
Total Citations
58
H-Index
2
About
Yueting Zhuang is a leading researcher in computer vision and multimedia, with a focus on domain adaptation, human-robot interaction, and emotion recognition. Her work addresses critical challenges in enabling intelligent systems to adapt and understand human behavior. Zhuang’s most cited paper, "Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation" (2022, 51 citations), introduces a novel approach to robot vision, allowing neural networks to adapt to new environments without access to source data—a key advancement for autonomous systems constrained by storage limitations. This work tackles the practical problem of domain shift in real-world applications. Additionally, her research on "Dilated Context Integrated Network with Cross-Modal Consensus for Temporal Emotion Localization in Videos" (2022, 7 citations) pioneers the task of temporally locating emotions in videos, moving beyond simple video-level classification to enable more nuanced human-robot interactions. This contribution is vital for developing robots that can respond to emotional cues in real-time. Zhuang’s work consistently bridges the gap between theoretical advances and practical deployment, making her a notable figure in the fields of domain adaptation and affective computing.
Research Focus
Key Achievements
Top Papers
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- 2