Optimizing Vision-Language Consistency via Cross-Layer Regional Attention Alignment

2025-07-31

AI Summary

Vision-language models (VLMs) often suffer from insufficient cross-modal attention coordination, leading to semantic-visual misalignment, attention drift, and poor interpretability. To address this, we propose a cross-layer region-wise attention alignment framework featuring two core innovations: Layer-Patch-wise Cross Attention (LPWCA), which models fine-grained cross-attention between multi-layer semantic representations and image patches, and Progressive Attention Integration (PAI), which enforces consistency across layers via hierarchical attention fusion. Our method introduces only 3.55 million parameters yet achieves state-of-the-art performance across ten major vision-language benchmarks. It significantly improves regional focus precision and semantic-visual alignment quality while enhancing model interpretability—demonstrating that lightweight architectural refinements in cross-modal attention design can yield substantial gains in both accuracy and transparency.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Vision Language Models (VLMs) face challenges in effectively coordinating diverse attention mechanisms for cross-modal embedding learning, leading to mismatched attention and suboptimal performance. We propose Consistent Cross-layer Regional Alignment (CCRA), which introduces Layer-Patch-wise Cross Attention (LPWCA) to capture fine-grained regional-semantic correlations by jointly weighting patch and layer-wise embedding, and Progressive Attention Integration (PAI) that systematically coordinates LPWCA, layer-wise, and patch-wise attention mechanisms in sequence. This progressive design ensures consistency from semantic to regional levels while preventing attention drift and maximizing individual attention benefits. Experimental results on ten diverse vision-language benchmarks demonstrate that our CCRA-enhanced LLaVA-v1.5-7B model achieves state-of-the-art performance, outperforming all baseline methods with only 3.55M additional parameters, while providing enhanced interpretability through more regionally focused and semantically aligned attention patterns.

Problem

• Coordinating diverse attention mechanisms in Vision Language Models
• Addressing mismatched attention and suboptimal cross-modal embedding
• Enhancing regional-semantic alignment and interpretability in VLMs

Innovation

• Layer-Patch-wise Cross Attention for fine-grained correlations
• Progressive Attention Integration for systematic coordination
• Cross-layer alignment for consistent semantic-regional attention