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RefineCap: Concept-Aware Refinement for Image Captioning

arXiv 2109.03529 ↗First posted 8 Sep 2021cs.CLcited by 0

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Abstract (arXiv)

Automatically translating images to texts involves image scene understanding and language modeling. In this paper, we propose a novel model, termed RefineCap, that refines the output vocabulary of the language decoder using decoder-guided visual semantics, and implicitly learns the mapping between visual tag words and images. The proposed Visual-Concept Refinement method can allow the generator to attend to semantic details in the image, thereby generating more semantically descriptive captions. Our model achieves superior performance on the MS-COCO dataset in comparison with previous visual-concept based models.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 29 references that are in the map. First posted on arXiv 8 Sep 2021.

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References · 20

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8 more
  1. 2016SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioningcited by 219
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  3. 2019Levenshtein Transformercited by 199
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  7. 2017Incorporating Copying Mechanism in Image Captioning for Learning Novel Objectscited by 20
  8. 2017Skeleton Key: Image Captioning by Skeleton-Attribute Decompositioncited by 15

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