Fine-grained ship image recognition is to discriminate different subcategories of ship categories. Because of the lack of ship data sets and the particularity of the identification task, fine-grained ship recognition is a challenging task. We designed a part assignment module, which has the function of part assignment and extracting import part information. Then, we added the module to the SimCLR contrastive learning framework. This method uses the module to assignment the information in the feature map, extract the key information of key regions, increase the learning ability of contrast learning for key information, in the end, the accuracy of fine-grained classification can be improved.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Contrastive Learning with Part Assignment for Fine-grained Ship Image Recognition


    Beteiligte:
    Zhang, Zhilin (Autor:in) / Zhang, Ting (Autor:in) / Liu, Zhaoying (Autor:in) / Li, Yujian (Autor:in)


    Erscheinungsdatum :

    01.03.2023


    Format / Umfang :

    1179183 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch