@inproceedings{
liu2024visual,
title={Visual Anchors Are Strong Information Aggregators For Multimodal Large Language Model},
author={Haogeng Liu and Quanzeng You and Xiaotian Han and Yongfei Liu and Huaibo Huang and Ran He and Hongxia Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=2YPdpWzEsF}
}
In the realm of Multimodal Large Language Models (MLLMs), vision-language connector plays a crucial role to link the pre-trained vision encoders with Large Language Models (LLMs). Despite its importance, the vision-language connector has been relatively less explored. In this study, we aim to propose a strong vision-language connector that enables MLLM to simultaneously achieve high accuracy and low computation cost. We first reveal the existence of the visual anchors in Vision Transformer and propose a cost-effective search algorithm to progressively extract them. Building on these findings, we introduce the Anchor Former (AcFormer), a novel vision-language connector designed to leverage the rich prior knowledge obtained from these visual anchors during pretraining, guiding the aggregation of information. 
Through extensive experimentation, we demonstrate that the proposed method significantly reduces computational costs by nearly two-thirds, while simultaneously outperforming baseline methods. This highlights the effectiveness and efficiency of AcFormer.
Visual Anchors Are Strong Information Aggregators For Multimodal Large Language Model
Haogeng Liu Quanzeng You Xiaotian Han Yongfei Liu Huaibo Huang Ran He Hongxia Yang