Cross Modal Fine-Grained Alignment via Granularity-Aware and Region-Uncertain Modeling

• URL: http://arxiv.org/abs/2511.07710v2
• Date: Wed, 19 Nov 2025 08:39:44 GMT
• Title: Cross Modal Fine-Grained Alignment via Granularity-Aware and Region-Uncertain Modeling
• Authors: Jiale Liu, Haoming Zhou, Yishu Zhu, Bingzhi Chen, Yuncheng Jiang,
• Abstract summary: Fine-grained image-text alignment is a pivotal challenge in multimodal learning.<n>We propose a unified approach that incorporates significance-aware and region-level uncertainty modeling.<n>Our approach achieves state-of-the-art performance across various backbone architectures.
• Score: 17.78769812974246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fine-grained image-text alignment is a pivotal challenge in multimodal learning, underpinning key applications such as visual question answering, image captioning, and vision-language navigation. Unlike global alignment, fine-grained alignment requires precise correspondence between localized visual regions and textual tokens, often hindered by noisy attention mechanisms and oversimplified modeling of cross-modal relationships. In this work, we identify two fundamental limitations of existing approaches: the lack of robust intra-modal mechanisms to assess the significance of visual and textual tokens, leading to poor generalization in complex scenes; and the absence of fine-grained uncertainty modeling, which fails to capture the one-to-many and many-to-one nature of region-word correspondences. To address these issues, we propose a unified approach that incorporates significance-aware and granularity-aware modeling and region-level uncertainty modeling. Our method leverages modality-specific biases to identify salient features without relying on brittle cross-modal attention, and represents region features as a mixture of Gaussian distributions to capture fine-grained uncertainty. Extensive experiments on Flickr30K and MS-COCO demonstrate that our approach achieves state-of-the-art performance across various backbone architectures, significantly enhancing the robustness and interpretability of fine-grained image-text alignment.

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