5 citations · 5 across the 5 of their papers we have counts for
5 papers
Bridging Time and Space: Decoupled Spatio-Temporal Alignment for Video Grounding
Xuezhen Tu, Jingyu Wu, Fangyu Kang +4
Spatio-Temporal Video Grounding requires jointly localizing target objects across both temporal and spatial dimensions based on natural language queries, posing fundamental challen…
Agentic Video Intelligence: A Flexible Framework for Advanced Video Exploration and Understanding
Hong Gao, Yiming Bao, Xuezhen Tu +6
Video understanding requires not only visual recognition but also complex reasoning. While Vision-Language Models (VLMs) demonstrate impressive capabilities, they typically process…
APVR: Hour-Level Long Video Understanding with Adaptive Pivot Visual Information Retrieval
Hong Gao, Yiming Bao, Xuezhen Tu +3
Current multimodal large language models (MLLMs) struggle with hour-level video understanding, facing significant challenges not only in modeling the substantial information volume…
Collaborative Learning of On-Device Small Model and Cloud-Based Large Model: Advances and Future Directions
Chaoyue Niu, Yucheng Ding, Junhui Lu +7
The conventional cloud-based large model learning framework is increasingly constrained by latency, cost, personalization, and privacy concerns. In this survey, we explore an emerg…
Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective
Xuezhen Tu, Kun Zhu, Nguyen Cong Luong +3
Federated learning (FL) becomes popular and has shown great potentials in training large-scale machine learning (ML) models without exposing the owners' raw data. In FL, the data o…