A visual recognition system to process images includes a global sub-network including a convolutional layer and a first max pooling layer. A local sub-network is connected to receive data from the global sub-network, and includes at least two convolutional layers, each connected to a max pooling layer. A fusion network is connected to receive data from the local sub-network, and includes a plurality of fully connected layers that respectively determine local feature maps derived from images. A loss layer is connected to receive data from the fusion network, set filter parameters, and minimize ranking error.


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    Title :

    POINT TO SET SIMILARITY COMPARISON AND DEEP FEATURE LEARNING FOR VISUAL RECOGNITION


    Additional title:

    VERGLEICH VON POINT-TO-SET-ÄHNLICHKEIT UND TIEFENFUNKTIONSLERNEN ZUR VISUELLEN ERKENNUNG
    COMPARAISON DE SIMILARITÉS DE POINTS À UN ENSEMBLE ET APPRENTISSAGE PROFOND DE CARACTÉRISTIQUES PERMETTANT UNE RECONNAISSANCE VISUELLE


    Contributors:
    ZHOU SANPIN (author) / WANG JINJUN (author)

    Publication date :

    2020-09-30


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    B60R Fahrzeuge, Fahrzeugausstattung oder Fahrzeugteile, soweit nicht anderweitig vorgesehen , VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G06V



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