Christopher Lai

I am a fourth-year undergraduate in Computer Engineering at UC Santa Barbara with a concentration in computer vision. I am currently a member of the Computer Vision Research Lab led by Yuan-Fang Wang, and have also worked in the Four Eyes Lab under Tobias Höllerer. I've also had the chance to work in industry roles at AWS, Blue Origin, and NASA.

Broadly, my research interests are in multimodal computer vision. Recently, I have been focused on fine-grained action recognition in sports, multimodal evaluation of large vision-language models, and video understanding at the intersection of semantics and strategy.

If you're interested in collaboration, feel free to reach out to me via email. I am always excited to chat and learn more about your research!

Email  /  GitHub  /  Google Scholar  /  LinkedIn

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Research

I'm interested in computer vision, machine learning, and robotics.

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DeepSport: A Multimodal Large Language Model for Comprehensive Sports Video Reasoning via Agentic Reinforcement Learning


Junbo Zou, Haotian Xia, Zhen Ye, Shengjie Zhang, Christopher Lai, Vicente Ordonez, Weining Shen, Hanjie Chen
Preprint
arxiv

We introduce DeepSport, the first end-to-end trained MLLM framework designed for multi-task, multi-sport video understanding, featuring active iterative reasoning with a specialized frame-extraction tool and achieving state-of-the-art performance on sports video reasoning tasks.
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SportR: A Benchmark for Multimodal Large Language Model Reasoning in Sports


Haotian Xia, Haonan Ge, Junbo Zou, Hyun Woo Choi, Xuebin Zhang, Danny Suradja, Botao Rui, Ethan Tran, Wendy Jin, Zhen Ye, Xiyang Lin, Christopher Lai, Shengjie Zhang, Junwen Miao, Shichao Chen, Rhys Tracy, Vicente Ordonez, Weining Shen, Hanjie Chen
Preprint
arxiv

We introduce SportR, a multi-sports benchmark designed to train and evaluate MLLMs on fundamental reasoning required for sports intelligence, featuring images and videos with progressive reasoning tasks.
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FACTS: Fine-Grained Action Classification for Tactical Sports


Christopher Lai, Jason Mo, Haotian Xia, Yuan-fang Wang
Preprint
arxiv

We introduce FACTS, a novel transformer-based approach for fine-grained action recognition in fast-paced, close-combat sports that processes raw video data directly, achieving state-of-the-art performance on fencing and boxing actions.
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SPORTU: A Comprehensive Sports Understanding Benchmark for Multimodal Large Language Models


Haotian Xia, Zhengbang Yang, Junbo Zou, Rhys Tracy, Yuqing Wang, Chi Lu, Christopher Lai, Yanjun He, Xun Shao, Zhuoqing Xie, Yuan-fang Wang, Weining Shen, Hanjie Chen
ICLR 2025 (Poster)
arxiv

We introduce SPORTU, a comprehensive benchmark designed to assess Multimodal Large Language Models (MLLMs) across multi-level sports reasoning tasks, featuring both text-based rule comprehension and video-based action recognition components.

Design and source code from Leonid Keselman's Jekyll fork of Jon Barron's website