3 papers
cs.CV2025
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…
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…
cs.CV2024
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…