Top Rank Supervised Binary Coding for Visual Search

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A Survey on Learning to Hash (2016, background): “Top rank supervised binary coding [132] presents another form of triplet loss in order to penalize the samples that are incorrectly ranked at the top of a Hamming-distance ranking list more than those at the bottom.”
Central Similarity Quantization for Efficient Image and Video Retrieval (2019, methodology): “Different from previous works [41, 25, 23, 26, 38, 31, 32, 19, 34, 16, 17, 45], this work proposes a new central similarity metric and use it to model the relationships between similar and dissimilar pairs for improving the discriminability of generated hash codes.”
Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval (2018, methodology): “Most existing shallow cross-modal hashing methods (in both unsupervised [2, 10, 14, 18] and supervised settings [7, 17, 19, 26, 30, 40, 33]), always capture the semantic relevance in a common Hamming space.”
A Decade Survey of Content Based Image Retrieval Using Deep Learning (2020, background): “[26], Semi-supervised distance metric learning [27], Hamming distance metric learning [28], [29], and Rank based metric learning [30], [31].”