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They are therefore<jats:italic>not scalable<\/jats:italic>to large scale re-id deployment scenarios with the need of processing a large amount of surveillance video data, due to the lengthy inference process with high computing costs. In this work, we address this limitation via jointly learning re-id attention selection. Specifically, we formulate a novel<jats:italic>harmonious attention network<\/jats:italic>(HAN) framework to jointly learn soft pixel attention and hard region attention alongside simultaneous deep feature representation learning, particularly enabling more discriminative re-id matching by<jats:italic>efficient<\/jats:italic>networks with more scalable model inference and feature matching. Extensive evaluations validate the cost-effectiveness superiority of the proposed HAN approach for person re-id against a wide variety of state-of-the-art methods on four large benchmark datasets: CUHK03, Market-1501, DukeMTMC, and MSMT17.<\/jats:p>","DOI":"10.1007\/s11263-019-01274-1","type":"journal-article","created":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T15:02:39Z","timestamp":1577113359000},"page":"1635-1653","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Scalable Person Re-Identification by Harmonious Attention"],"prefix":"10.1007","volume":"128","author":[{"given":"Wei","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9284-2955","authenticated-orcid":false,"given":"Xiatian","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Shaogang","family":"Gong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,23]]},"reference":[{"key":"1274_CR1","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., & Isard, M., et\u00a0al. 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