Abstract
Same-style clothes retrieval is a task to search images which contain exactly the same designing style clothes. For such a task, too limited training data makes the problem of how to gain suitable same-style feature representations challenging but significant. In this paper, we adopt a memory-augmented deep neural network, also called as a few-shot learning model, to collect possibly same-style images. Besides, we present an object-aware clothes retrieval framework to further enhance the same-style feature representations, in which object focusing regions through object detection are first obtained, and a multi-task Siamese network is designed for ranking feature learning provided with some same-style or non-same-style image pairs. Experiments results show that our proposed solution is effective to discover more same-style images precisely, and further achieve the satisfied performance on same-style clothes retrieval.
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Acknowledgments
This work was supported by National Natural Science Foundation of China (61332016 and 61472422).
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Shen, Z., Fang, Z., Liu, J. (2018). Same-Style Products Mining for Clothes Retrieval. In: Huet, B., Nie, L., Hong, R. (eds) Internet Multimedia Computing and Service. ICIMCS 2017. Communications in Computer and Information Science, vol 819. Springer, Singapore. https://doi.org/10.1007/978-981-10-8530-7_44
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DOI: https://doi.org/10.1007/978-981-10-8530-7_44
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