Xinyu Huang (黄新宇)

I am a fourth-year Ph.D. student at the School of Computer Science Fudan University, advised by Prof. Rui Feng and Prof. Yuejie Zhang. Meanwhile, I'm also a research intern at OPPO Research Institute, supervised by Researcher Youcai Zhang. I was fortunate to work with Prof. Yandong Guo and Prof. Lei Zhang.

My research interests include computer vision and multi-modality. I created the Recognize Anything Model (RAM) Family, which is a series of open-source and powerful image recognition models.

I expect to graduate at June 2025. I am opening to both academic positions and industrial research positions. Kindly download my Resume, and do not hesitate to email me if you're interested :)

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RAM++ is the next generation of RAM, which can recognize any category with high accuracy, including both predefined common categories and diverse open-set categories.

RAM is an image tagging model, which can recognize any common category with high accuracy.

Tag2Text is a vision-language model guided by tagging, which can support tagging and comprehensive captioning simultaneously.

We propose IDEA to provide more explicit textual supervision (including multiple valuable tags and texts composed by multiple tags) for visual models.

Multi-label learning in the presence of missing labels(MLML) is a challenging problem. We propose two simple yet effective methods via robust loss design based on an observation.

We provide Recognize Anything Model Family (RAM) demonstrating superior image recognition ability!

RAM Faimly marry Grounded-SAM, which can automatically recognize, detect, and segment for an image! RAM Family showcases powerful image recognition capabilities!