3 papers
cs.CV2026
WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMs
Jack Hong, Shilin Yan, Jiayin Cai +3
We introduce WorldSense, the first benchmark to assess the multi-modal video understanding, that simultaneously encompasses visual, audio, and text inputs. In contrast to existing…
cs.CV2025
Progressive Scaling Visual Object Tracking
Jack Hong, Shilin Yan, Zehao Xiao +4
In this work, we propose a progressive scaling training strategy for visual object tracking, systematically analyzing the influence of training data volume, model size, and input r…
cs.LG2025
DynaPrompt: Dynamic Test-Time Prompt Tuning
Zehao Xiao, Shilin Yan, Jack Hong +6
Test-time prompt tuning enhances zero-shot generalization of vision-language models but tends to ignore the relatedness among test samples during inference. Online test-time prompt…