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English(EN) Fine-grained CLIP fine-tuning with self-annotated region alignment

新方法增强CLIP的细粒度表示能力 · 跟踪4个来源

作者 PulseAugur 编辑部 · [5 个来源] ·

两篇新的研究论文提出了改进对比语言-图像预训练（CLIP）模型细粒度表示能力的方法。第一篇论文介绍了SFF-CLIP，它使用自标注区域对齐方案来增强细粒度特征，而无需额外的区域标注，从而保留了CLIP的全局表示能力。第二篇论文AspectCLIP通过重新制定一致性正则化来解决图像和文本之间的信息不对称问题，以尊重图像-标题对的一对多结构，从而获得更具结构化的表示空间。 AI

影响 这些方法旨在提高视觉-语言模型的细粒度理解能力，有望在需要详细图像-文本对齐的任务中取得更好的性能。

排序理由 两篇在arXiv上发表的学术论文，提出了微调CLIP模型的新方法。

在 arXiv cs.CV 阅读 →

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新方法增强CLIP的细粒度表示能力 · 跟踪4个来源

报道来源 [5]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Fine-grained CLIP fine-tuning with self-annotated region alignment

    Contrastive Language-Image Pre-training (CLIP) has been shown to have limitations in its fine-grained dense feature representation, due to its pre-training focusing on matching the whole image to a text description. Considering the large data and computational burden in pre-train…

  2. arXiv cs.CV TIER_1 English(EN) · Chenyang Zhao, Wei Lin, Antoni B. Chan, Janet H. Hsiao ·

    使用自标注区域对齐进行细粒度CLIP微调

    arXiv:2607.13661v1 Announce Type: new Abstract: Contrastive Language-Image Pre-training (CLIP) has been shown to have limitations in its fine-grained dense feature representation, due to its pre-training focusing on matching the whole image to a text description. Considering the …

  3. arXiv cs.CV TIER_1 English(EN) · Yiyang Yao, Shanglin Liu, Jianming Lv, Chengjun Wang, Jinyi Li, Yuchan Jie, Zhihua Jin ·

    AspectCLIP：通过面向方面的相干性正则化优化CLIP表示空间

    arXiv:2607.13805v1 Announce Type: new Abstract: Contrastive Language-Image Pretraining learns a shared representation space through large-scale contrastive learning. However, existing methods that enforce global consistency regularization overlook a key challenge: the inherent in…

  4. arXiv cs.CV TIER_1 English(EN) · Zhihua Jin ·

    AspectCLIP：通过面向方面的相干性正则化优化CLIP表示空间

    Contrastive Language-Image Pretraining learns a shared representation space through large-scale contrastive learning. However, existing methods that enforce global consistency regularization overlook a key challenge: the inherent information asymmetry between images and text: cap…

  5. arXiv cs.CV TIER_1 English(EN) · Janet H. Hsiao ·

    具有自标注区域对齐的细粒度CLIP微调

    Contrastive Language-Image Pre-training (CLIP) has been shown to have limitations in its fine-grained dense feature representation, due to its pre-training focusing on matching the whole image to a text description. Considering the large data and computational burden in pre-train…

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