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.
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2.2K
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247
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2017–2026
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Michael Gygli, Helmut Gräbner, Hayko Riemenschneider et al. · 2013 · 185 citations · Full text
Deep Saliency Hashing for Fine-Grained Retrieval
Sheng Jin, Hongxun Yao, Xiaoshuai Sun et al. · IEEE Transactions on Image Processing · 2020 · 73 citations
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Universidade Tecnológica Federal do Paraná, Brazil
2 publications · 74 citations
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Chinese Academy of Sciences
Beijing, China
4 h-index
7 publications
159 citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
3 h-index
5 publications
317 citations

Beihang University
Beijing, China
3 h-index
8 publications
218 citations

Harbin Institute of Technology
Harbin, China
3 h-index
5 publications
250 citations
Xinjiang University
Ürümqi, China
2 h-index
6 publications
123 citations
Beijing Jiaotong University
Beijing, China
2 h-index
6 publications
87 citations

Shenzhen University
Shenzhen, China
2 h-index
8 publications
95 citations

Indian Institute of Technology Bombay
Mumbai, India
2 h-index
6 publications
141 citations
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5 publications · 419 citations
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Repository · 3 publications · 65 citations
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3 publications · 106 citations
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2 publications · 30 citations
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