A dual contrastive framework

• URL: http://arxiv.org/abs/2412.10348v1
• Date: Fri, 13 Dec 2024 18:45:18 GMT
• Title: A dual contrastive framework
• Authors: Yuan Sun, Zhao Zhang, Jorge Ortiz,
• Abstract summary: Region-level visual understanding presents significant challenges for large-scale vision-language models. We propose AlignCap, a framework designed to enhance region-level understanding through fine-grained alignment of latent spaces.
• Score: 7.358205057611624
• License:
• Abstract: In current multimodal tasks, models typically freeze the encoder and decoder while adapting intermediate layers to task-specific goals, such as region captioning. Region-level visual understanding presents significant challenges for large-scale vision-language models. While limited spatial awareness is a known issue, coarse-grained pretraining, in particular, exacerbates the difficulty of optimizing latent representations for effective encoder-decoder alignment. We propose AlignCap, a framework designed to enhance region-level understanding through fine-grained alignment of latent spaces. Our approach introduces a novel latent feature refinement module that enhances conditioned latent space representations to improve region-level captioning performance. We also propose an innovative alignment strategy, the semantic space alignment module, which boosts the quality of multimodal representations. Additionally, we incorporate contrastive learning in a novel manner within both modules to further enhance region-level captioning performance. To address spatial limitations, we employ a General Object Detection (GOD) method as a data preprocessing pipeline that enhances spatial reasoning at the regional level. Extensive experiments demonstrate that our approach significantly improves region-level captioning performance across various tasks

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