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English(EN) RankVR: Low-Rank Structure Perception and Value Recalibration for Robust Composed Image Retrieval

RankVR框架通过过滤噪声数据增强图像检索能力

研究人员推出RankVR,一个旨在改进组合图像检索(CIR)模型的新框架。RankVR通过采用全局结构一致性感知模块,根据相关矩阵秩识别和移除噪声样本,从而解决大型数据集中存在的噪声三元组对应等挑战。此外,自适应语义值校准模块有助于区分有价值的难样本,以进行更有效的训练。在基准数据集上的实验表明,RankVR在噪声环境中显著优于现有方法。 AI

影响 提高了图像检索模型在噪声数据集中的鲁棒性,有望带来更准确的搜索结果。

排序理由 这是一篇描述一种新的图像检索框架的研究论文。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

RankVR框架通过过滤噪声数据增强图像检索能力

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这是一篇描述一种新的图像检索框架的研究论文。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Jiale Huang, Zixu Li, Zhiheng Fu, Zhiwei Chen, Qinlei Huang, Yupeng Hu ·

    RankVR:低秩结构感知与价值重校准,用于鲁棒的组合图像检索

    arXiv:2606.11689v1 Announce Type: new Abstract: Composed Image Retrieval (CIR) constitutes a pivotal paradigm requiring models to perform joint reasoning on reference images and modification texts. However, the prevalence of Noisy Triplet Correspondence (NTC) in large-scale datas…

  2. arXiv cs.CV TIER_1 English(EN) · Yupeng Hu ·

    RankVR:低秩结构感知与价值重校准,实现鲁棒的组合图像检索

    Composed Image Retrieval (CIR) constitutes a pivotal paradigm requiring models to perform joint reasoning on reference images and modification texts. However, the prevalence of Noisy Triplet Correspondence (NTC) in large-scale datasets severely constrains model performance. Exist…