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• W2972262504 abstract "In this paper we consider the problem of video-based person re-identification, which is the task of associating videos of the same person captured by different and non-overlapping cameras. We propose a Siamese framework in which video frames of the person to re-identify and of the candidate one are processed by two identical networks which produce a similarity score. We introduce an attention mechanisms to capture the relevant information both at frame level (spatial information) and at video level (temporal information given by the importance of a specific frame within the sequence). One of the novelties of our approach is given by a joint concurrent processing of both frame and video levels, providing in such a way a very simple architecture. Despite this fact, out approach achieves better performance than the state-of-the-art on the challenging iLIDS-VID dataset." @default.
• W2972262504 created "2019-09-12" @default.
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• W2972262504 date "2019-01-01" @default.
• W2972262504 modified "2023-10-18" @default.
• W2972262504 title "Video-Based Convolutional Attention for Person Re-Identification" @default.
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• W2972262504 doi "https://doi.org/10.1007/978-3-030-30642-7_1" @default.
• W2972262504 hasPublicationYear "2019" @default.
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