40 citations · 74 across the 10 of their papers we have counts for
5 papers · 1 filter
A Skill-augmented Agentic Framework and Benchmark for Multi-Video Understanding
Yue Zhang, Liqiang Jing, Jia Li +4
Multimodal Large Language Models have achieved strong performance in single-video understanding, yet their ability to reason across multiple videos remains limited. Existing approa…
Towards Spatio-Temporal World Scene Graph Generation from Monocular Videos
Rohith Peddi, Saurabh, Shravan Shanmugam +4
Spatio-temporal scene graphs provide a principled representation for modeling evolving object interactions, yet existing methods remain fundamentally frame-centric: they reason onl…
Can Video Large Multimodal Models Think Like Doubters-or Double-Down: A Study on Defeasible Video Entailment
Yue Zhang, Jilei Sun, Yunhui Guo +1
Video Large Multimodal Models (VLMMs) have made impressive strides in understanding video content, but they often struggle with abstract and adaptive reasoning-the ability to revis…
Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven Optimization
Yue Zhang, Liqiang Jing, Vibhav Gogate
We introduce a new task called Defeasible Visual Entailment (DVE), where the goal is to allow the modification of the entailment relationship between an image premise and a text hy…
Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation
Rohith Peddi, Saurabh, Ayush Abhay Shrivastava +2
Spatio-Temporal Scene Graphs (STSGs) provide a concise and expressive representation of dynamic scenes by modeling objects and their evolving relationships over time. However, real…