<ul><li>Few-Shot Class-Incremental Learning (FSCIL) is a cutting-edge paradigm within machine learning.</li><li>FSCIL empowers models to assimilate new classes of data with limited examples while safeguarding existing knowledge.</li><li>The paper presents different solutions and extensive experiments to evaluate and compare methods for FSCIL.</li><li>An experimental approach aims to improve FSCIL performance by replacing the visual-language model with a superior one.</li></ul>

An experimental approach on Few Shot Class Incremental Learning

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