3 papers
cs.CL2025
Watch and Listen: Understanding Audio-Visual-Speech Moments with Multimodal LLM
Zinuo Li, Xian Zhang, Yongxin Guo +5
Humans naturally understand moments in a video by integrating visual and auditory cues. For example, localizing a scene in the video like "A scientist passionately speaks on wildli…
cs.CV2024
TRACE: Temporal Grounding Video LLM via Causal Event Modeling
Yongxin Guo, Jingyu Liu, Mingda Li +3
Video Temporal Grounding (VTG) is a crucial capability for video understanding models and plays a vital role in downstream tasks such as video browsing and editing. To effectively…
cs.CV2024
Enhancing Long Video Understanding via Hierarchical Event-Based Memory
Dingxin Cheng, Mingda Li, Jingyu Liu +5
Recently, integrating visual foundation models into large language models (LLMs) to form video understanding systems has attracted widespread attention. Most of the existing models…