Xiang (Ryan) Li

I am a third-year Ph.D. student at the University of Illinois Urbana-Champaign (UIUC), advised by Prof. James Rehg.

Previously, I obtained my bachelor's degree at the Hong Kong University of Science and Technology (HKUST), where I was advised by Prof. Yu-Wing Tai and Prof. Chi-Keung Tang.

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Video State-Changing Object Segmentation
Jiangwei Yu*, Xiang Li*, Xinran Zhao, Hongming Zhang, Yu-Xiong Wang
In ICCV, 2023
paper / project page / dataset and code

We present a weakly-supervised Video State-Changing Object Segmentation (VSCOS) benchmark, revealing challenges in current VOS models for state-changing objects and introducing three solutions for improved state-changing object segmentation.

YouTubePD: A Multimodal Benchmark for Parkinson’s Disease Analysis
Andy Zhou*, Samuel Li*, Pranav Sriram*, Xiang Li*, Jiahua Dong*, Ansh Sharma, Yuanyi Zhong, Shirui Luo, Maria Jaromin, Volodymyr Kindratenko, George Heintz, Christopher Zallek, Yu-Xiong Wang
In NeurIPS Datasets and Benchmarks Track, 2023
paper / dataset and code: coming soon

We introduce YouTubePD, the first public multimodal benchmark for Parkinson's Disease (PD) analysis, crowdsourced from existing YouTube videos featuring over 200 subjects. The benchmark provides diverse expert annotations and suggests three tasks, with experimental evaluations indicating the potential and limits of deep learning models for real-world clinical applications.

One-Shot Object Detection without Fine-Tuning
Xiang Li*, Lin Zhang*, Yau Pun Chen, Yu-Wing Tai, Chi-Keung Tang
arXiv, 2020
paper / project page / code

We introduce a two-stage model and training strategies for one-shot object detection by integrating the metric learning with an anchor-free Faster R-CNN-style detection pipeline.

FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation
Xiang Li, Tianhan Wei, Yau Pun Chen, Yu-Wing Tai, Chi-Keung Tang
In CVPR, 2020
paper / dataset and code

A few-shot segmentation dataset containing 1000 varied and balanced object categories with pixelwise annotation of ground-truth segmentation.

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