Recoverable Compression: A Multimodal Vision Token Recovery Mechanism Guided by Text Information

TL;DR

This paper introduces a text-guided dynamic visual token recovery method for multimodal models that maintains performance while significantly reducing visual tokens, enhancing efficiency without retraining.

Contribution

It proposes a novel, training-free token recovery mechanism guided by text similarity, improving token compression in multimodal vision-language models.

Findings

Achieves 90% reduction in visual tokens on average.

Maintains comparable performance to original models.

Demonstrates effectiveness across various visual tasks.

Abstract

With the advancement of large-scale language modeling techniques, large multimodal models combining visual encoders with large language models have demonstrated exceptional performance in various visual tasks. Most of the current large-scale multimodal models achieve this by mapping visual features obtained from the visual encoder into a large language model and using them as inputs alongside text for downstream tasks. Therefore, the number of visual tokens directly affects the training and inference speed of the model. There has been significant work on token pruning for visual transformers, but for large multimodal models, only relying on visual information for token pruning or compression may lead to significant loss of important information. On the other hand, the textual input in the form of a question may contain valuable information that can aid in answering the question, providing additional knowledge to the model. To address the potential oversimplification and excessive pruning that can occur with most purely visual token pruning methods, we propose a text information-guided dynamic visual token recovery mechanism that does not require training. This mechanism leverages the similarity between the question text and visual tokens to recover visually meaningful tokens with important text information while merging other less important tokens. Experimental results demonstrate that our proposed method achieves comparable performance to the original approach while compressing the visual tokens to an average of 10% of the original quantity. Our source code will be made publicly available following acceptance.