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arXiv · 2508.00945

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

Yifan Wang·Hongfeng Ai·Quangao Liu·Maowei Jiang·Ruiyuan Kang·Ruiqi Li·Jiahua Dong·Mengting Xiao·Cheng Jiang·Chenzhong Li
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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.

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Yifan Wang, Hongfeng Ai, Quangao Liu, Maowei Jiang, Ruiyuan Kang, Ruiqi Li, Jiahua Dong, Mengting Xiao, Cheng Jiang, Chenzhong Li. 2025-07-31. Optimizing Vision-Language Consistency via Cross-Layer Regional Attention Alignment. https://arxiv.org/abs/2508.00945

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