9 papers · 1 filter
PPLLaVA: Varied Video Sequence Understanding With Prompt Guidance
Shangkun Sun, Ruyang Liu, Haoran Tang +5
In the past year, video-based large language models (Video LLMs) have achieved impressive progress, particularly in their ability to process long videos through extremely extended…
Order from Chaos: Physical World Understanding from Glitchy Gameplay Videos
Meng Cao, Haoran Tang, Haoze Zhao +6
Understanding the physical world, including object dynamics, material properties, and causal interactions, remains a core challenge in artificial intelligence. Although recent mult…
Video Spatial Reasoning with Object-Centric 3D Rollout
Haoran Tang, Meng Cao, Ruyang Liu +4
Recent advances in Multi-modal Large Language Models (MLLMs) have showcased remarkable capabilities in vision-language understanding. However, enabling robust video spatial reasoni…
Flow4Agent: Long-form Video Understanding via Motion Prior from Optical Flow
Ruyang Liu, Shangkun Sun, Haoran Tang +2
Long-form video understanding has always been a challenging problem due to the significant redundancy in both temporal and spatial contents. This challenge is further exacerbated b…
MUSE: Mamba is Efficient Multi-scale Learner for Text-video Retrieval
Haoran Tang, Meng Cao, Jinfa Huang +4
Text-Video Retrieval (TVR) aims to align and associate relevant video content with corresponding natural language queries. Most existing TVR methods are based on large-scale pre-tr…
PhysGame: Uncovering Physical Commonsense Violations in Gameplay Videos
Meng Cao, Haoran Tang, Haoze Zhao +7
Recent advancements in video-based large language models (Video LLMs) have witnessed the emergence of diverse capabilities to reason and interpret dynamic visual content. Among the…