DAPE: Dynamic Non-uniform Alignment and Progressive Detail Enhancement Techniques for Improving the Performance of Efficient Visual Language Models

2026-05-09

AI Summary

This work addresses the limitations of prevailing vision-language models that employ uniform alignment strategies, which overlook the dynamic disparities in information density and semantic scope between images and text, often resulting in the loss of fine-grained semantics and excessive computational overhead. To overcome these issues, the authors propose a dynamic non-uniform cross-modal alignment framework that leverages a learnable matching function to adaptively assign image regions to textual segments based on their information density. The framework further integrates progressive high-resolution visual feature enhancement with an efficient attention mechanism to achieve precise yet computationally frugal alignment. Extensive experiments demonstrate that the proposed method significantly improves performance on multiple downstream tasks while reducing computational costs, effectively balancing accuracy and efficiency.

Abstract

In recent years, pre-trained visual-linguistic models have demonstrated tremendous potential, becoming a crucial foundational framework for numerous downstream tasks. However, the information density between text and images is not uniformly distributed. Existing methods often overlook the inherent and dynamic differences in information density and semantic scope between text tags and image blocks. These common uniform alignment strategies result in coarse-grained cross-modal interactions and loss of fine semantic details. Moreover, pursuing finer alignment typically requires substantial computational overhead, limiting practical model deployment. To address this challenge, this paper proposes a novel framework for dynamic cross-modal alignment with continuous detail introduction. First, we design a dynamically adaptive cross-modal matching mechanism that uses a learnable matching function to dynamically assign varying numbers and sizes of image tags to text tags of the same size but different information density, enabling more precise attention interaction. Second, we develop a continuous detail introduction module to progressively incorporate high-resolution visual feature enhancement into the alignment process. Extensive experiments across multiple benchmarks demonstrate significant improvements in the accuracy of various downstream tasks while reducing computational overhead.

Problem

• cross-modal alignment
• information density
• semantic detail
• computational overhead
• visual language models

Innovation

• Dynamic Cross-modal Alignment
• Non-uniform Matching
• Progressive Detail Enhancement
• Efficient Visual Language Models
• Information Density Awareness