# Exploring and Distilling Cross-Modal Information for Image Captioning Source: [Exploring and Distilling Cross-Modal Information for Image Captioning]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:08:20.129Z Authors: Fenglin Liu, Xuancheng Ren, Yuanxin Liu, Kai Lei, Xu Sun Published: 2020-02-28T07:46:48.000Z ## Abstract Recently, attention-based encoder-decoder models have been used extensively in image captioning. Yet there is still great difficulty for the current methods to achieve deep image understanding. In this work, we argue that such understanding requires visual attention to correlated image regions and semantic attention to coherent attributes of interest. Based on the Transformer, to perform effective attention, we explore image captioning from a cross-modal perspective and propose the Global-and-Local Information Exploring-and-Distilling approach that explores and distills the source information in vision and language. It globally provides the aspect vector, a spatial and relational representation of images based on caption contexts, through the extraction of salient region groupings and attribute collocations, and locally extracts the fine-grained regions and attributes in reference to the aspect vector for word selection. Our Transformer-based model achieves a CIDEr score of 129.3 in offline COCO evaluation on the COCO testing set with remarkable efficiency in terms of accuracy, speed, and parameter budget. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Captioning on MSCOCO (test server)](): Image Captioning; MSCOCO (test server); BLEU-4 (c5): 38.5. ## Original sources and implementation [Original paper]() [PDF]()