7 papers · 1 filter
Grounding Video Reasoning in Physical Signals
Alibay Osmanli, Zixu Cheng, Shaogang Gong
Physical video understanding requires more than naming an event correctly. A model can answer a question about pouring, sliding, or collision from textual regularities while still…
GraphThinker: Reinforcing Temporally Grounded Video Reasoning with Event Graph Thinking
Zixu Cheng, Da Li, Jian Hu +4
Video reasoning requires a fine-grained understanding of the temporal dependencies and event-level relations between objects and events in videos. Current Multimodal Large Language…
Uncertainty-quantified Rollout Policy Adaptation for Unlabelled Cross-domain Temporal Grounding
Jian Hu, Zixu Cheng, Shaogang Gong +4
Video Temporal Grounding (TG) aims to temporally locate video segments matching a natural language description (a query) in a long video. While Vision-Language Models (VLMs) are ef…
V-STaR: Benchmarking Video-LLMs on Video Spatio-Temporal Reasoning
Zixu Cheng, Jian Hu, Ziquan Liu +3
Human processes video reasoning in a sequential spatio-temporal reasoning logic, we first identify the relevant frames ("when") and then analyse the spatial relationships ("where")…
CoS: Chain-of-Shot Prompting for Long Video Understanding
Jian Hu, Zixu Cheng, Chenyang Si +2
Multi-modal Large Language Models (MLLMs) struggle with long videos due to the need for excessive visual tokens. These tokens exceed massively the context length of MLLMs, resultin…
INT: Instance-Specific Negative Mining for Task-Generic Promptable Segmentation
Jian Hu, Zixu Cheng, Shaogang Gong
Task-generic promptable image segmentation aims to achieve segmentation of diverse samples under a single task description by utilizing only one task-generic prompt. Current method…
SHINE: Saliency-aware HIerarchical NEgative Ranking for Compositional Temporal Grounding
Zixu Cheng, Yujiang Pu, Shaogang Gong +2
Temporal grounding, also known as video moment retrieval, aims at locating video segments corresponding to a given query sentence. The compositional nature of natural language enab…