• ACNN extracts spatial features, and strengthens relationships between channels. • ACNN equipped with characteristics of offline training and online learning performs outstandingly against state-of-the-art methods. • We have studied relations between the performance of the tracker and the number of layers. The moderate network achieves a good balance.


    Access

    Download


    Export, share and cite



    Title :

    Attentional convolutional neural networks for object tracking


    Contributors:


    Publication date :

    2018-04-01


    Size :

    2086428 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Attentional convolutional neural networks for object tracking

    Kong, Xiangdong / Zhang, Baochang / Yue, Lei et al. | IEEE | 2018


    Convolutional Neural Networks for Object Detection

    Romão, Bruno / Fagotto, Eric | SAE Technical Papers | 2024


    NEURAL NETWORKS WITH ATTENTIONAL BOTTLENECKS FOR TRAJECTORY PLANNING

    BANSAL MAYANK / KIM JINKYU | European Patent Office | 2021

    Free access

    Convolutional Neural Networks for Inference of Space Object Attitude Status

    Badura, Gregory | British Library Conference Proceedings | 2020


    Convolutional Neural Networks

    Habibi Aghdam, Hamed / Jahani Heravi, Elnaz | Springer Verlag | 2017