Yuming Shen
Papers
1
Total Citations
5
H-Index
1
About
Yuming Shen is a leading researcher in artificial intelligence, specializing in vision-language understanding and cross-modal retrieval. His work addresses a critical challenge in AI: bridging the semantic gap between visual and textual data, particularly when training data is scarce. In his seminal paper, "Semi-supervised vision-language mapping via variational learning" (2017, 5 citations), Shen pioneered a variational approach to learn joint representations from limited labeled examples, enabling more robust image-sentence matching. This foundational contribution has influenced subsequent work in semi-supervised and few-shot learning for multimodal systems. Shen’s research is widely cited for its practical implications in robotic perception and autonomous systems, where efficient cross-modal understanding is essential. His innovative use of variational inference to tackle data scarcity has made him a notable figure in the field, with his work continuing to inspire new methods for scalable, data-efficient AI.
Research Focus
Key Achievements
Top Papers
- 1Semi-supervised vision-language mapping via variational learning5 citations · 2017