基于通道信息对齐的素描行人重识别
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作者单位:

昆明理工大学

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中图分类号:

TP273

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Channel Information Alignment for Sketch Re-identification
Author:
Affiliation:

Kunming University of Science and Technology

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    素描行人重识别任务要求在彩色图像库中寻找与给定素描图像相同身份的行人。由于行人的素描图像和彩色图像之间的姿态、视角等信息不同,两个模态在相同的空间位置往往具有不同的语义信息,导致提取得到的特征不具备鲁棒性。以往的研究着重于行人不随着模态信息变化的特征提取,而忽略了不同模态间语义不对齐的问题,进而导致最终编码的特征受到摄像机视角、人体姿态或者遮挡等干扰,不利于图像的匹配。本文提出基于通道信息对齐的素描行人重识别模型,其中语义信息一致性学习模块,引导网络在特征的相同通道上形成固定编码的语义信息,降低语义信息不对齐所带来的影响;其中差异性特征注意力模块,辅助网络编码具有差异性的身份相关信息,并设计空间差异正则化项防止网络仅关注局部特征。两个模块互相配合,强化网络对语义信息的感知和对齐。本文所提出的方法在挑战性数据集Sketch Re-ID、QMUL-ShoeV2上Rank1和mAP分别达到60.0%和59.3%、33.5%和46.1%,验证了方法的有效性。

    Abstract:

    The sketch person re-identification requires to search for pedestrians with the same identity as the given sketch image in the color image gallery. Due to the difference of posture and viewpoint between the sketch image and the color image, the two images from two different modes often have different semantic information in the same spatial position, which leads to the lack of robustness of the extracted features. Previous studies focused on pedestrian feature extraction modal-invariant information, but ignored the issue of semantic misalignment between different modal images, which led to the interference of the features by camera viewpoint, human posture or occlusion, which was not good for image matching. In this paper, a sketch pedestrian re-identification model based on channel information alignment is proposed, in which the semantic information alignment learning module guides the network to coded semantic information on the same channel of the feature to reduce the impact of misalignment of semantic information. Among them, the variant feature attention module assists the network to encode the variant identity related information, and designs the spatial variant regularization term to prevent the network from only paying attention to local features. The two modules cooperate with each other to strengthen the network"s perception and alignment of semantic information. Rank1and mAP of the proposed method in the challenging data set Sketch Re-ID and QMUL-ShoeV2 reach 60.0% and 59.3%, 33.5% and 46.1%, respectively, which verifies the effectiveness of the method.

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历史
  • 收稿日期:2021-06-06
  • 最后修改日期:2021-08-30
  • 录用日期:2021-09-10
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