Researchers at Leiden University and the National University of Defense Technology (NUDT), in China, have recently developed a new approach for image-text matching, called CycleMatch. Their approach, presented in a paper published in Elsevier's Pattern Recognition journal, is based on cycle-consistent learning, a technique that is sometimes used to train artificial neural networks on image-to-image translation tasks. The general idea behind cycle-consistency is that when transforming source data into target data and then vice versa, one should finally obtain the original source samples.
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