mlsanityData to 7 Oct 2026

Learning Fine-grained Image Similarity with Deep Ranking

arXiv 1404.4661 ↗First posted 17 Apr 2014cs.CVcited by 189

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Abstract (arXiv)

Learning fine-grained image similarity is a challenging task. It needs to capture between-class and within-class image differences. This paper proposes a deep ranking model that employs deep learning techniques to learn similarity metric directly from images.It has higher learning capability than models based on hand-crafted features. A novel multiscale network structure has been developed to describe the images effectively. An efficient triplet sampling algorithm is proposed to learn the model with distributed asynchronized stochastic gradient. Extensive experiments show that the proposed algorithm outperforms models based on hand-crafted visual features and deep classification models.

Key facts. Cited by 189 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Apr 2014.

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Citing papers per year: 2021: 65; 2022: 45; 2023: 41; 2024: 17; 2025: 16; 2026: 5.

189 papers.

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