Mining sufficient discriminative information is vital for effective feature representation in vehicle re-identification. Traditional methods mainly focus on the most salient features and neglect whether the explored information is sufficient. This paper tackles the above limitation by proposing a novel Salience-Navigated Vehicle Re-identification Network (SVRN) which explores diverse salient features at multi-scales. For mining sufficient salient features, we design SVRN from two aspects: 1) network architecture: we propose a novel salience-navigated vehicle re-identification network, which mines diverse features under a cascaded suppress-and-explore mode. 2) feature space: cross-space constraint enables the diversity from feature space, which restrains the cross-space features by vehicle and image identifications (IDs). Extensive experiments demonstrate our method’s effectiveness, and the overall results surpass all previous state-of-the-arts in three widely-used Vehicle ReID benchmarks (VeRi-776, VehicleID, and VERI-WILD), i.e., we achieve an 84.5% mAP on VeRi-776 benchmark that outperforms the second-best method by a large margin (3.5% mAP).


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    Title :

    Navigating Diverse Salient Features for Vehicle Re-Identification


    Contributors:
    Qian, Wen (author) / He, Zhiqun (author) / Chen, Chen (author) / Peng, Silong (author)

    Published in:

    Publication date :

    2022-12-01


    Size :

    1836528 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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