Vehicle re-identification (Re-ID) distinguishes between the same vehicle and other vehicles in images. It is challenging due to significant intra-instance differences between identical vehicles from different views and subtle inter-instance differences of similar vehicles. Researchers have tried to address this problem by extracting features robust to variations of viewpoints and environments and using additional metadata such as key points, orientation, and temporal information, which require expensive annotations. More recently, many efforts have been made to improve performance by adopting the attention mechanism. In this paper, we propose a multiple soft attention network to provide part-aware attention weights and extract more representative and robust features for vehicle Re-ID. This model uses multiple spatial and channel-wise attention branches to allow soft partitioning of images and extracting more representative and robust features for vehicle Re-Id.Our approach achieved state-of-the-art (SOTA) performance among the approaches that did not use metadata and showed comparable performance to the methods that used metadata in the experiments using the VehicleID and VeRi-776 datasets.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multiple Soft Attention Network for Vehicle Re-Identification


    Contributors:
    Lee, Sangrok (author) / Woo, Taekang (author) / Lee, Sang Hun (author)


    Publication date :

    2022-10-08


    Size :

    1462123 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Multi-dimensional Attention Network for Vehicle Re-identification

    Gao, Yunlong / Lu, Liping / Xu, Bingrong et al. | IEEE | 2022


    Vehicle identification statistical method based on multi-attention mechanism network

    LIU HAOTING / CHEN SHUAI / DING KAI et al. | European Patent Office | 2023

    Free access

    Source-attention features fusion network for person re-identification

    He, Linbing / Fu, Yiming / Du, Haishun | British Library Conference Proceedings | 2022


    Siamese Multiple Attention Temporal Convolution Networks for Human Mobility Signature Identification

    Zheng, Zhipeng / Jiang, Yuchen / Zhang, Shiyao et al. | IEEE | 2024


    A Novel Dual-pooling Attention Module for UAV Vehicle Re-identification

    Guo, Xiaoyan / Yang, Jie / Jia, Xinyu et al. | ArXiv | 2023

    Free access