8 papers
StreamingAssistant: Efficient Visual Token Pruning for Accelerating Online Video Understanding
Xinqi Jin, Hanxun Yu, Bohan Yu +8
Online video understanding is essential for applications like public surveillance and AI glasses. However, applying Multimodal Large Language Models (MLLMs) to this domain is chall…
Active Perception Agent for Omnimodal Audio-Video Understanding
Keda Tao, Wenjie Du, Bohan Yu +3
Omnimodal large language models have made significant strides in unifying audio and visual modalities; however, they often face challenges in fine-grained cross-modal understanding…
Revisiting MLLM Token Technology through the Lens of Classical Visual Coding
Jinming Liu, Junyan Lin, Yuntao Wei +7
Classical visual coding and Multimodal Large Language Model (MLLM) token technology share the core objective - maximizing information fidelity while minimizing computational cost.…
A Survey of Token Compression for Efficient Multimodal Large Language Models
Kele Shao, Keda Tao, Kejia Zhang +7
Multimodal large language models (MLLMs) have made remarkable strides, largely driven by their ability to process increasingly long and complex contexts, such as high-resolution im…
HoliTom: Holistic Token Merging for Fast Video Large Language Models
Kele Shao, Keda Tao, Can Qin +3
Video large language models (video LLMs) excel at video comprehension but face significant computational inefficiency due to redundant video tokens. Existing token pruning methods…
Plug-and-Play 1.x-Bit KV Cache Quantization for Video Large Language Models
Keda Tao, Haoxuan You, Yang Sui +2
Video large language models (VideoLLMs) have demonstrated the capability to process longer video inputs and enable complex reasoning and analysis. However, due to the thousands of…