We introduce ExAct, a video-language benchmark for expert-level analysis of skilled human actions. It contains over 3,500 expert-curated video QA pairs across domains like sports, cooking, and music. Our benchmark reveals a significant performance gap between state-of-the-art VLMs and human experts, highlighting the need for models with a more nuanced understanding of complex human skills.

We present BASKET, a large-scale basketball video dataset for fine-grained skill estimation. BASKET contains more than 4,400 hours of video capturing 32,232 basketball players from all over the world. We benchmark multiple SOTA video recognition models and reveal that these models struggle to achieve good results on our benchmark.

Neural Motion Transfer serves as an effective data augmentation technique for PPG signal estimation from facial videos. We devise the best strategy to augment publicly available datasets with motion augmentation, improving up to 75% over SOTA techniques on five benchmark datasets.

Introducing a multi-reliability and multi-level feature augmentation framework for semi-supervised semantic segmentation, effectively utilizing labeled and unlabeled images and improving segmentation performance on benchmark datasets.

I am enthusiastic in helping other students succeed in computer science. I have shared my knowledge and support students' learning journey in the following course:

University of North Carolina at Chapel Hill, Undergraduate Learning Assistant:

• COMP 301: Foundations of Programming
• COMP 116: Introduction to Scientific Programming

Brandeis University, Teaching Assistant:

• COSI 21A: Data Structures and the Fundamentals of Computing

Cloned from Jon Barron.