3 citations · 3 across the 5 of their papers we have counts for
7 papers · 1 filter
Viper-F1: Fast and Fine-Grained Multimodal Understanding with Cross-Modal State-Space Modulation
Quoc-Huy Trinh
Recent advances in multimodal large language models (MLLMs) have enabled impressive progress in vision-language understanding, yet their high computational cost limits deployment i…
v-HUB: A Benchmark for Video Humor Understanding from Vision and Sound
Zhengpeng Shi, Yanpeng Zhao, Jianqun Zhou +6
AI models capable of comprehending humor hold real-world promise -- for example, enhancing engagement in human-machine interactions. To gauge and diagnose the capacity of multimoda…
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…
TC-LLaVA: Rethinking the Transfer from Image to Video Understanding with Temporal Considerations
Mingze Gao, Jingyu Liu, Mingda Li +5
Multimodal Large Language Models (MLLMs) have significantly improved performance across various image-language applications. Recently, there has been a growing interest in adapting…
The SkatingVerse Workshop & Challenge: Methods and Results
Jian Zhao, Lei Jin, Jianshu Li +16
The SkatingVerse Workshop & Challenge aims to encourage research in developing novel and accurate methods for human action understanding. The SkatingVerse dataset used for the Skat…
VTG-LLM: Integrating Timestamp Knowledge into Video LLMs for Enhanced Video Temporal Grounding
Yongxin Guo, Jingyu Liu, Mingda Li +6
Video Temporal Grounding (VTG) strives to accurately pinpoint event timestamps in a specific video using linguistic queries, significantly impacting downstream tasks like video bro…