Active learning is a machine learning or artificial intelligence method that aims to achieve optimal model performance by actively selecting and labeling the most valuable samples, using the fewest high-quality labeled samples. Relevant works have already been conducted using information uncertainty to select valuable samples. However, its effectiveness for semantic segmentation tasks is relatively poor. In this paper, we propose an active learning framework based on the Generative Adversarial Networks (GANs) architecture for semantic segmentation task, which achieves efficient data selection while simultaneously enhancing semantic segmentation performance. The proposed framework combines the generator (i.e., semantic segmentation network) and the discriminator (i.e., data selection network) for jointly optimizing of both semantic segmentation and data selection tasks. Extensive experiments were conducted on the dataset, which was collected from real-world autonomous driving scenes. Experimental results demonstrate that our method achieved competitive results compared to other SOTA methods.


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    Titel :

    A Generative Active Learning Framework for Semantic Segmentation in Autonomous Driving


    Beteiligte:
    Wu, Ye (Autor:in) / Wei, Qinggong (Autor:in) / Song, Ruiqi (Autor:in) / Cui, Chenglin (Autor:in) / Li, Xinqing (Autor:in) / Zhu, Fenghua (Autor:in) / Ai, Yunfeng (Autor:in)


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    1433820 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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