  With the bloom of Large Language Models (LLMs), Multimodal Large Language
Models (MLLMs) that incorporate LLMs with pre-trained vision models have
recently demonstrated impressive performance across diverse vision-language
tasks. However, they fall short to comprehend context involving multiple
images. A primary reason for this shortcoming is that the visual features for
each images are encoded individually by frozen encoders before feeding into the
LLM backbone, lacking awareness of other images and the multimodal
instructions. We term this issue as prior-LLM modality isolation and propose a
two phase paradigm, browse-and-concentrate, to enable in-depth multimodal
context fusion prior to feeding the features into LLMs. This paradigm initially
"browses" through the inputs for essential insights, and then revisits the
inputs to "concentrate" on crucial details, guided by these insights, to
achieve a more comprehensive understanding of the multimodal inputs.
Additionally, we develop training strategies specifically to enhance the
understanding of multi-image inputs. Our method markedly boosts the performance
on 7 multi-image scenarios, contributing to increments on average accuracy by
2.13% and 7.60% against strong MLLMs baselines with 3B and 11B LLMs,
respectively.
