Haibo Min
Papers
1
Total Citations
5
H-Index
1
About
Haibo Min is a researcher whose work sits at the compelling intersection of computer vision, audio processing, and robotics, with a particular focus on how machines can learn to understand and interact with their environments using multiple senses. A key contribution is their pioneering exploration of self-supervised learning for cross-modal perception, most notably demonstrated in their 2020 paper on aligning objects and sound. This work tackles the challenging problem of sound source separation—a critical capability for applications like human-robot interaction and scene understanding—by ingeniously leveraging both visual and audio information from videos without requiring explicit human labels. While their highly-cited work continues to gain recognition with over 5 citations, Min’s research is notable for its forward-thinking approach to multimodal learning, aiming to give robots a more holistic, human-like understanding of the world. By addressing fundamental challenges in how machines fuse sight and sound, Haibo Min is contributing to the development of more perceptive, autonomous, and interactive robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Self-Supervised Learning for Alignment of Objects and Sound5 citations · 2020