2 papers
cs.CV2026
How Should Video LLMs Output Time? An Analysis of Efficient Temporal Grounding Paradigms
Shengji Jin, Yuanhao Zou, Victor Zhu +2
While Multimodal Large Language Models (MLLMs) have advanced Video Temporal Grounding (VTG), existing methods often couple output paradigms with different backbones, datasets, and…
cs.CV2025
A.I.R.: Enabling Adaptive, Iterative, and Reasoning-based Frame Selection For Video Question Answering
Yuanhao Zou, Shengji Jin, Andong Deng +3
Effectively applying Vision-Language Models (VLMs) to Video Question Answering (VideoQA) hinges on selecting a concise yet comprehensive set of frames, as processing entire videos…