collaborators

9 papers

cs.CV2026

Non-Colliding Biometric Identities for Digital Entities: Geometry, Capacity, and Million-Scale Virtual Identity Provisioning

Yuyang Ji, Yixuan Shen, Anil Jain +2

Digital entities such as AI agents and humanoid robots increasingly operate alongside real humans, yet their identity infrastructure is based on credentials rather than embodied bi…

cs.CY2026

Can Multimodal LLMs See Science Instruction? Benchmarking Pedagogical Reasoning in K-12 Classroom Videos

Yixuan Shen, Peng He, Honglu Liu +6

K-12 science classrooms are rich sites of inquiry where students coordinate phenomena, evidence, and explanatory models through discourse; yet, the multimodal complexity of these i…

cs.CV2026

IDSelect: A RL-Based Cost-Aware Selection Agent for Video-based Multi-Modal Person Recognition

Yuyang Ji, Yixuan Shen, Kien Nguyen +2

Video-based person recognition achieves robust identification by integrating face, body, and gait. However, current systems waste computational resources by processing all modaliti…

cs.CV2026

BioGait-VLM: A Tri-Modal Vision-Language-Biomechanics Framework for Interpretable Clinical Gait Assessment

Erdong Chen, Yuyang Ji, Jacob K. Greenberg +5

Video-based Clinical Gait Analysis often suffers from poor generalization as models overfit environmental biases instead of capturing pathological motion. To address this, we propo…

cs.CV2026

Building a Mind Palace: Structuring Environment-Grounded Semantic Graphs for Effective Long Video Analysis with LLMs

Zeyi Huang, Yuyang Ji, Xiaofang Wang +11

Long-form video understanding with Large Vision Language Models is challenged by the need to analyze temporally dispersed yet spatially concentrated key moments within limited cont…

cs.CV2025

IMPROVE: Iterative Model Pipeline Refinement and Optimization Leveraging LLM Experts

Eric Xue, Ke Chen, Zeyi Huang +2

Large language model (LLM) agents have emerged as a promising solution to automate the workflow of machine learning, but most existing methods share a common limitation: they attem…