Color is an essential and stable feature of vehicles. It serves as a reliable cue in a wide range of applications of intelligent transportation systems. In this paper, we present deep learning models for the recognition of color and vehicles. The vehicle color captioning model introduces a wide variety of color names mimicking human-level color name annotations. Unlike the existing color recognition models, which tag color names from a limited set of pre-defined color names, the proposed color recognition model employs a generative model to give color captions to vehicles. The generative model is a sequence-to-sequence model that considers color naming as a machine translation problem. The proposed model for color naming does not need a labelled ground-truth dataset. Rather, it generates color captions from mean values of the pixels and adopts color name adjectives like ‘dark’, ‘light’, ‘dull’, etc. Along with color recognition, we also present a pre-trained model vehicle recognition, which recognizes the vehicle in transportation.


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    Titel :

    Deep Generative Model for Vehicle Color Recognition


    Beteiligte:


    Erscheinungsdatum :

    17.12.2024


    Format / Umfang :

    1742750 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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