3 papers
cs.CV2026
TempR1: Improving Temporal Understanding of MLLMs via Temporal-Aware Multi-Task Reinforcement Learning
Tao Wu, Li Yang, Gen Zhan +6
Enhancing the temporal understanding of Multimodal Large Language Models (MLLMs) is essential for advancing long-form video analysis, enabling tasks such as temporal localization,…
cs.CL2026
Predicting Contextual Informativeness for Vocabulary Learning using Deep Learning
Tao Wu, Adam Kapelner
We describe a modern deep learning system that automatically identifies informative contextual examples (\qu{contexts}) for first language vocabulary instruction for high school st…
cs.CV2025
Online Video Understanding: OVBench and VideoChat-Online
Zhenpeng Huang, Xinhao Li, Jiaqi Li +7
Multimodal Large Language Models (MLLMs) have significantly progressed in offline video understanding. However, applying these models to real-world scenarios, such as autonomous dr…