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RankVR framework enhances image retrieval by filtering noisy data

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced RankVR, a new framework designed to improve Composed Image Retrieval (CIR) models. RankVR addresses challenges in large datasets, specifically noisy triplet correspondence, by employing a Global Structure Consistency Perception module to identify and remove noisy samples based on correlation matrix rank. Additionally, an Adaptive Semantic Value Calibration module helps distinguish valuable hard samples for more effective training. Experiments on benchmark datasets show RankVR significantly outperforms existing methods in noisy environments. AI

IMPACT Improves robustness of image retrieval models in noisy datasets, potentially leading to more accurate search results.

RANK_REASON This is a research paper describing a new framework for image retrieval.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

RankVR framework enhances image retrieval by filtering noisy data

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COVERAGE [2]

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

    RankVR: Low-Rank Structure Perception and Value Recalibration for Robust Composed Image Retrieval

    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: Low-Rank Structure Perception and Value Recalibration for Robust Composed Image Retrieval

    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…

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