The content discusses a novel approach to weakly supervised grounded image captioning, emphasizing the importance of relation semantics in generating accurate captions and improving grounding performance. The proposed method outperforms existing two-stage solutions by directly processing RGB images for captioning and grounding.
Recent advances in image captioning have led to the development of grounded image captioners that localize object words while generating captions, enhancing interpretability. The proposed one-stage weakly supervised method eliminates the need for bounding box annotations, achieving state-of-the-art grounding performance on challenging datasets.
The study introduces a top-down vision transformer-based encoder to encode raw images, incorporating a recurrent grounding module for precise visual-language attention maps generation. By injecting relation semantic information into the model, it significantly benefits both caption generation and object localization.
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arxiv.org
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by Chen Cai,Suc... ב- arxiv.org 03-05-2024
https://arxiv.org/pdf/2306.07490.pdfשאלות מעמיקות