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Text-based image captioning (TextCap) which aims to read and reason imageswith texts is crucial for a machine to understand a detailed and complex sceneenvironment, considering that texts are omnipresent in daily life. This task,however, is very challenging because an image often contains complex texts andvisual information that is hard to be described comprehensively. Existingmethods attempt to extend the traditional image captioning methods to solvethis task, which focus on describing the overall scene of images by one globalcaption. This is infeasible because the complex text and visual informationcannot be described well within one caption. To resolve this difficulty, weseek to generate multiple captions that accurately describe different parts ofan image in detail. To achieve this purpose, there are three key challenges: 1)it is hard to decide which parts of the texts of images to copy or paraphrase;2) it is non-trivial to capture the complex relationship between diverse textsin an image; 3) how to generate multiple captions with diverse content is stillan open problem. To conquer these, we propose a novel Anchor-Captioner method.Specifically, we first find the important tokens which are supposed to be paidmore attention to and consider them as anchors. Then, for each chosen anchor,we group its relevant texts to construct the corresponding anchor-centred graph(ACG). Last, based on different ACGs, we conduct multi-view caption generationto improve the content diversity of generated captions. Experimental resultsshow that our method not only achieves SOTA performance but also generatesdiverse captions to describe images.

Towards Accurate Text-based Image Captioning with Content Diversity Exploration