The paper introduces SQ-LLaVA, a framework that utilizes visual self-questioning to improve vision-language understanding. By training the model to ask high-quality questions based on image context, SQ-LLaVA achieves advanced levels of generalized visual understanding. The model outperforms traditional visual instruction tuning methods by leveraging overlooked contextual information within images. Through experiments, SQ-LLaVA demonstrates improved performance in various vision-language tasks, showcasing the effectiveness of self-questioning techniques in enhancing comprehension of visual content.
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by Guohao Sun,C... om arxiv.org 03-19-2024
https://arxiv.org/pdf/2403.11299.pdfDiepere vragen