1 citations · 1 across the 8 of their papers we have counts for
6 papers · 1 filter
Visual Token Coding for Video Multimodal Large Language Models
Chenxin Fang, Tao Chen, JunChao You +3
In this paper, we propose a new token compression paradigm for video Multimodal Large Language Models (MLLMs), termed Visual Token Coding (VTC). Inspired by classical video coding…
Towards a Dynamic and Fixed-budget Memory Bank for Efficient Streaming Video Understanding
Baiyang Song, Yuli Lin, Qiong Wu +5
Currently, streaming video understanding is still a daunting task for existing \emph{multimodal large language models} (MLLMs). Its difficulties not only lie in handling the ever-i…
Towards Fast and Effective Long Video Understanding of Multimodal Large Language Models via Adaptive Quasi-Gaussian Sampling
Kun Zhang, Chenxin Fang, Tao Chen +4
Long video understanding remains a daunting challenge for Multimodal Large Language Models (MLLMs) due to the excessive computation and memory footprint. Thus, keyframe selection i…
ForestPrune: High-ratio Visual Token Compression for Video Multimodal Large Language Models via Spatial-Temporal Forest Modeling
Shaobo Ju, Baiyang Song, Tao Chen +6
Due to the great saving of computation and memory overhead, token compression has become a research hot-spot for MLLMs and achieved remarkable progress in image-language tasks. How…
Scaling the Long Video Understanding of Multimodal Large Language Models via Visual Memory Mechanism
Tao Chen, Kun Zhang, Qiong Wu +5
Long video understanding is a key challenge that plagues the advancement of \emph{Multimodal Large language Models} (MLLMs). In this paper, we study this problem from the perspecti…
Towards Effective Long Video Understanding of Multimodal Large Language Models via One-shot Clip Retrieval
Tao Chen, Shaobo Ju, Qiong Wu +6
Due to excessive memory overhead, most Multimodal Large Language Models (MLLMs) can only process videos of limited frames. In this paper, we propose an effective and efficient para…