Semantic-Preserving Cross-Style Visual Reasoning for Robust Multi-Modal Understanding in Large Vision-Language Models

• URL: http://arxiv.org/abs/2510.22838v1
• Date: Sun, 26 Oct 2025 21:11:46 GMT
• Title: Semantic-Preserving Cross-Style Visual Reasoning for Robust Multi-Modal Understanding in Large Vision-Language Models
• Authors: Aya Nakayama, Brian Wong, Yuji Nishimura, Kaito Tanaka,
• Abstract summary: Semantic-Preserving Cross-Style Visual Reasoner (SP-CSVR) is a novel framework for stable semantic understanding and adaptive cross-style visual reasoning.<n>SP-CSVR integrates a Cross-Style Feature (CSFE) for style-content disentanglement, a Semantic-Aligned In-Context Decoder (SAICD) for efficient few-shot style adaptation, and an Adaptive Semantic Consistency Module (ASCM) to enforce cross-style semantic invariance.
• Score: 0.2833003196854753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The "style trap" poses a significant challenge for Large Vision-Language Models (LVLMs), hindering robust semantic understanding across diverse visual styles, especially in in-context learning (ICL). Existing methods often fail to effectively decouple style from content, hindering generalization. To address this, we propose the Semantic-Preserving Cross-Style Visual Reasoner (SP-CSVR), a novel framework for stable semantic understanding and adaptive cross-style visual reasoning. SP-CSVR integrates a Cross-Style Feature Encoder (CSFE) for style-content disentanglement, a Semantic-Aligned In-Context Decoder (SAICD) for efficient few-shot style adaptation, and an Adaptive Semantic Consistency Module (ASCM) employing multi-task contrastive learning to enforce cross-style semantic invariance. Extensive experiments on a challenging multi-style dataset demonstrate SP-CSVR's state-of-the-art performance across visual captioning, visual question answering, and in-context style adaptation. Comprehensive evaluations, including ablation studies and generalization analysis, confirm SP-CSVR's efficacy in enhancing robustness, generalization, and efficiency across diverse visual styles.

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