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List-wise learning-to-rank with convolutional neural networks for person re-identification

Abstract : In this paper, we present a novel machine learning-based image ranking approach using Convolutional Neural Networks (CNN). Our proposed method relies on a similarity metric learning algorithm operating on lists of image examples and a loss function taking into account the ranking in these lists with respect to different query images. This comprises two major contributions: (1) Rank lists instead of image pairs or triplets are used for training, thus integrating more explicitly the order of similarity and relations between sets of images. (2) A weighting is introduced in the loss function based on two evaluation measures: the mean average precision and the rank 1 score. We evaluated our approach on two different computer vision applications that are commonly formulated as ranking problems: person re-identification and image retrieval with several public benchmarks and showed that our new loss function outperforms other common functions and that our method achieves state-of-the-art performance compared to existing approaches from the literature.
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Contributor : Stefan Duffner Connect in order to contact the contributor
Submitted on : Wednesday, March 3, 2021 - 11:48:08 AM
Last modification on : Monday, April 4, 2022 - 10:40:42 AM


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Yiqiang Chen, Stefan Duffner, Andrei Stoian, Jean-Yves Dufour, Atilla Baskurt. List-wise learning-to-rank with convolutional neural networks for person re-identification. Machine Vision and Applications, Springer Verlag, 2021, 32 (2), ⟨10.1007/s00138-021-01170-0⟩. ⟨hal-03157567⟩



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