文献类型：期刊文献

英文题名：Learning multi -level domain invariant features for sketch re -identification

作者：Gui, Shaojun[1];Zhu, Yu[1];Qin, Xiangxiang[1];Ling, Xiaofeng[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Technol, Shanghai 200237, Peoples R China

年份：2020

卷号：403

起止页码：294

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20202008663925);WOS:【SCI-EXPANDED(收录号:WOS:000541447500012)】；

基金：The authors greatly appreciate the financial supports of the Shanghai Association for Science and Technology under Grant 17DZ1100808, Natural Science Foundation of Shanghai under Grant 19ZR1413400. Portions of the research in this paper use the Sketch Re-ID dataset collected under the sponsor of the National Natural Science Foundation of China.

语种：英文

外文关键词：Drawing (graphics)

摘要：Person re-identification (Re-ID) aims to match the photos of suspects in the gallery database by a query photo. Under Re-ID, the input is usually a query photo of the target person, however, in a realistic setting, such a photo is not always available. In this paper, we study the problem of Sketch Re-ID, which is not based on a person's photo but the professional sketch of the target person. We use a full-body sketch of a target person drawn by a professional to find the photo with the same ID in the gallery. This problem is very challenging because photos and sketches belong to two completely different domains. Specifically, a sketch is a highly abstract description of a person, which only contains some rough outline information. We address the Sketch Re-ID problem by proposing a novel framework to jointly model photos and sketches into a common embedding space. Our framework uses a triplet classification network as a base network. We propose to use a spatial attention module and combine high-level and mid-level output features of CNN to represent the input images. Moreover, we design a novel domain-invariant feature by using a gradient reverse layer (GRL) to solve the domain gap problem. We validated our approach on the Sketch Re-ID dataset, which contains 200 persons, each of whom has a sketch and two photos from different cameras associated. To evaluate the generalization of our method, we also performed experiments on some other public Sketch-based Image Retrieval (SBIR) datasets. The extensive experimental results show that our method can get higher performance than state-of-the-art models. ? 2020