Attention-based Image Retrieval

Attention-Based Image Retrieval is an image retrieval framework that employs attention mechanisms to selectively emphasize salient visual features and their spatial relationships during the matching process, thereby improving the alignment between query semantics and relevant image content.

61

Publications

2.2K

Citations

247

Authors

98

Institutions

Publications per year

2017–2026

47

Authors

247

  1. YZ
    Yingying Zhu

    Shenzhen University, China

    2 publications · 23 citations

    2

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  2. OM
    Oge Marques

    Florida Atlantic University, United States

    2 publications · 74 citations

    2

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  3. AB
    Avik Bhattacharya

    Indian Institute of Technology Bombay, India

    2 publications · 47 citations

    2

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  4. FS
    Fred Stentiford

    University College London, United Kingdom

    2 publications · 88 citations

    2

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  5. HR
    Humberto Remigio Gamba

    Universidade Tecnológica Federal do Paraná, Brazil

    2 publications · 74 citations

    2

    h-index

1–5 of 247

Venues

  1. Pattern Recognition

    5 publications · 419 citations

    5

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  2. arXiv (Cornell University)

    Repository · 3 publications · 65 citations

    3

    h-index

  3. 3

    h-index

  4. 2

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  5. Knowledge-Based Systems

    2 publications · 38 citations

    2

    h-index

1–5