How much do cross-modal related semantics benefit image captioning by weighting attributes and re-ranking sentences?

Abstract Although image description generated from attributes achieves big progress, there are still two main problems to be solved further: (1) How to obtain more accurate attributes? (2) How to mitigate the difference between sentence generation and evaluation. To address these issues, we propose a new method to incorporate the cross-modal related semantics into the encoder-decoder structure for image captioning. In the encoding stage, we utilize the salient words derived from cross-modal retrieval to improve the accuracy of attributes. In the decoding stage, we explore two ways to re-rank the sentences generated through beam search with the guidance of semantics acquired through a modified cross-modal retrieval method. The evaluation results on the benchmark dataset MS-COCO in both offline and online prove the benefits of cross-modal related semantics on image captioning.