2 citations · 5 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter
Meng Cao, Haoran Tang, Jinfa Huang +7
Text-Video Retrieval (TVR) aims to align relevant video content with natural language queries. To date, most state-of-the-art TVR methods learn image-to-video transfer learning bas…
ST-LLM: Large Language Models Are Effective Temporal Learners
Ruyang Liu, Chen Li, Haoran Tang +3
Large Language Models (LLMs) have showcased impressive capabilities in text comprehension and generation, prompting research efforts towards video LLMs to facilitate human-AI inter…