Vidit Kumar1,*, Hemant Petwal2, Ajay Krishan Gairola1, Pareshwar Prasad Barmola1
Computer Systems Science and Engineering, Vol.46, No.3, pp. 2711-2724, 2023, DOI:10.32604/csse.2023.032047
Abstract Fine-grained image search is one of the most challenging tasks
in computer vision that aims to retrieve similar images at the fine-grained
level for a given query image. The key objective is to learn discriminative
fine-grained features by training deep models such that similar images are
clustered, and dissimilar images are separated in the low embedding space.
Previous works primarily focused on defining local structure loss functions
like triplet loss, pairwise loss, etc. However, training via these approaches
takes a long training time, and they have poor accuracy. Additionally, representations learned through it tend to tighten up in the embedded… More >
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