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This paper focuses on enhancing the captions generated by image-captiongeneration systems. We propose an approach for improving caption generationsystems by choosing the most closely related output to the image rather thanthe most likely output produced by the model. Our model revises the languagegeneration output beam search from a visual context perspective. We employ avisual semantic measure in a word and sentence level manner to match the propercaption to the related information in the image. The proposed approach can beapplied to any caption system as a post-processing based method.

Word to Sentence Visual Semantic Similarity for Caption Generation: Lessons Learned