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Knowledge Mining with Scene Text for Fine-Grained Recognition
[article]
2022
arXiv
pre-print
Recently, the semantics of scene text has been proven to be essential in fine-grained image classification. However, the existing methods mainly exploit the literal meaning of scene text for fine-grained recognition, which might be irrelevant when it is not significantly related to objects/scenes. We propose an end-to-end trainable network that mines implicit contextual knowledge behind scene text image and enhance the semantics and correlation to fine-tune the image representation. Unlike the
arXiv:2203.14215v1
fatcat:q7nprbaqqfbjdcyglwq27s5s4i