VLTP: Vision-Language Guided Token Pruning for Task-Oriented Segmentation

TL;DR

VLTP introduces a vision-language guided token pruning method that significantly reduces computational costs in task-oriented segmentation models based on Vision Transformers, without substantial performance loss.

Contribution

The paper proposes a novel token pruning mechanism guided by vision-language models specifically designed for complex task-oriented segmentation tasks.

Findings

Reduces ViT computational costs by approximately 25% without performance loss.

Achieves around 40% reduction with only 1% performance drop.

Effective for multi-modal large language model guided segmentation.

Abstract

Vision Transformers (ViTs) have emerged as the backbone of many segmentation models, consistently achieving state-of-the-art (SOTA) performance. However, their success comes at a significant computational cost. Image token pruning is one of the most effective strategies to address this complexity. However, previous approaches fall short when applied to more complex task-oriented segmentation (TOS), where the class of each image patch is not predefined but dependent on the specific input task. This work introduces the Vision Language Guided Token Pruning (VLTP), a novel token pruning mechanism that can accelerate ViT-based segmentation models, particularly for TOS guided by multi-modal large language model (MLLM). We argue that ViT does not need to process every image token through all of its layers -- only the tokens related to reasoning tasks are necessary. We design a new pruning decoder to take both image tokens and vision-language guidance as input to predict the relevance of each image token to the task. Only image tokens with high relevance are passed to deeper layers of the ViT. Experiments show that the VLTP framework reduces the computational costs of ViT by approximately 25% without performance degradation and by around 40% with only a 1% performance drop. The code associated with this study can be found at this URL.