Shaofeng Yin (殷绍峰)

I'm a junior undergraduate student at Peking University, majoring in artificial intelligence. I'm currently visiting Berkeley AI Research and have had a wonderful time working with Haven, Jiaxin and Zora.

My early research focused on human-object interaction detection (HOID) in computer vision. As vision-language models (VLMs) have grown impressively powerful, I've become deeply interested in the study of visual agents.

In life, I have a deep appreciation for art, literature, and philosophy. I'm also deeply commited to volunteer teaching programs. I used to be passionate about algorithm competitions and earned the title of Codeforces Master at the age of 15, but I later stepped away from it due to burnout.

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📚 Selected Publications

Recent benchmarks reveal significant gaps in real-world tool-use proficiency, particularly in functionally diverse multimodal settings requiring multi-step reasoning. To bridge this gap, we propose ToolEngine, a novel data generation pipeline that employs Depth-First Search (DFS) with a dynamic in-context example matching mechanism to simulate human-like tool-use reasoning.

In this paper, we introduce a novel framework for zero-shot HOI detection using Conditional Multi-Modal Prompts, namely CMMP. This approach enhances the generalization of large foundation models, such as CLIP, when fine-tuned for HOI detection.

In this paper, we introduce a novel end-to-end open vocabulary HOI detection framework with conditional multi-level decoding and fine-grained semantic enhancement (CMD-SE), harnessing the potential of Visual-Language Models (VLMs).

🏆 Selected Awards

📸 Selected Photography

The template is stole from Jon Barron.