9 citations · 18 across the 9 of their papers we have counts for
9 papers · 1 filter
EventLens: Event-Structure Reinforcement Learning for Video Understanding
Guowen Zhang, Boshen Xu, Zihao Yue +6
Recent multimodal large language models (MLLMs) achieve strong results on video question answering, yet often fail to recover the temporal structure that makes a video coherent. Th…
StreamPro: From Reactive Perception to Proactive Decision-Making in Streaming Video
Ao Li, Zihan Xiao, Zihao Yue +7
Proactive streaming video understanding requires models to continuously process video streams and decide when to respond, rather than merely what to respond. This naturally introdu…
TimeViper: A Hybrid Mamba-Transformer Vision-Language Model for Efficient Long Video Understanding
Boshen Xu, Zihan Xiao, Jiaze Li +4
We introduce TimeViper, a hybrid vision-language model designed to tackle challenges of long video understanding. Processing long videos demands both an efficient model architectur…
REVISOR: Beyond Textual Reflection, Towards Multimodal Introspective Reasoning in Long-Form Video Understanding
Jiaze Li, Hao Yin, Wenhui Tan +7
Self-reflection mechanisms that rely on purely text-based rethinking processes perform well in most multimodal tasks. However, when directly applied to long-form video understandin…
EgoDTM: Towards 3D-Aware Egocentric Video-Language Pretraining
Boshen Xu, Yuting Mei, Xinbi Liu +2
Egocentric video-language pretraining has significantly advanced video representation learning. Humans perceive and interact with a fully 3D world, developing spatial awareness tha…
Time-R1: Post-Training Large Vision Language Model for Temporal Video Grounding
Ye Wang, Ziheng Wang, Boshen Xu +14
Temporal Video Grounding (TVG), the task of locating specific video segments based on language queries, is a core challenge in long-form video understanding. While recent Large Vis…
Do Egocentric Video-Language Models Truly Understand Hand-Object Interactions?
Boshen Xu, Ziheng Wang, Yang Du +3
Egocentric video-language pretraining is a crucial step in advancing the understanding of hand-object interactions in first-person scenarios. Despite successes on existing testbeds…
POV: Prompt-Oriented View-Agnostic Learning for Egocentric Hand-Object Interaction in the Multi-View World
Boshen Xu, Sipeng Zheng, Qin Jin
We humans are good at translating third-person observations of hand-object interactions (HOI) into an egocentric view. However, current methods struggle to replicate this ability o…
SPAFormer: Sequential 3D Part Assembly with Transformers
Boshen Xu, Sipeng Zheng, Qin Jin
We introduce SPAFormer, an innovative model designed to overcome the combinatorial explosion challenge in the 3D Part Assembly (3D-PA) task. This task requires accurate prediction…