Yingfeng Yu
Papers
1
Total Citations
2
H-Index
1
About
Yingfeng Yu is a researcher in embodied AI and multimodal perception, with a focus on audio-visual navigation—a field that trains robots to locate sound-emitting targets using egocentric visual and auditory inputs. Their most cited work, “Pay Self-Attention to Audio-Visual Navigation” (2022), introduces a novel self-attention mechanism for fusing audio and visual streams, enabling more robust and efficient navigation in complex environments. This contribution addresses a critical challenge in embodied AI: how to dynamically align and integrate sensory modalities for real-time decision-making. With 2 citations, the paper has laid groundwork for subsequent studies in cross-modal learning and robotics. Yu’s research sits at the intersection of computer vision, audio processing, and reinforcement learning, advancing the development of autonomous agents that can perceive and act in human-centric spaces. Their work is particularly relevant for applications in assistive robotics, search-and-rescue, and human-robot interaction, where understanding both visual scenes and auditory cues is essential. As the field of audio-visual navigation grows, Yu’s contributions continue to influence how researchers design fusion strategies for embodied agents.
Research Focus
Key Achievements
Top Papers
- 1Pay Self-Attention to Audio-Visual Navigation2 citations · 2022