Optimizing Vision-Language Consistency via Cross-Layer Regional Attention Alignment
This addresses attention coordination issues in VLMs for enhanced cross-modal learning, representing a strong specific gain rather than a foundational breakthrough.
The paper tackled the problem of mismatched attention in Vision Language Models (VLMs) by proposing Consistent Cross-layer Regional Alignment (CCRA), which improved performance on ten benchmarks, achieving state-of-the-art results with only 3.55M additional parameters.
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.