HIST: Hierarchical and sequential transformer for image captioning

Image captioning aims to automatically generate a natural language description of a given image, and most state-of-the-art models have adopted an encoder-decoder transformer framework. Such transformer structures, however, show two main limitations in the task of image captioning. Firstly, the traditional transformer obtains high-level fusion features to decode while ignoring other-level features, resulting in losses of image content. Secondly, the transformer is weak in modelling the natural order characteristics of language. To address theseissues, the authors propose a HIerarchical and Sequential Transformer (HIST) structure, which forces each layer of the encoder and decoder to focus on features of different granularities, and strengthen the sequentially semantic information. Specifically, to capture the details of different levels of features in the image, the authors combine the visual features of multiple regions and divide them into multiple levels differently. In addition, to enhance the sequential information, the sequential enhancement module in each decoder layer block extracts different levels of features for sequentially semantic extraction and expression. Extensive experiments on the public datasets MS-COCO and Flickr30k have demonstrated the effectiveness of our proposed method, and show that the authors' method outperforms most of previous state of the arts. The authors propose hierarchical encoder-decoder blocks in the authors' novel hierarchical and sequential transformer for capturing multi-granularity image information and combining it with a sequential enhancement module to generate rich and smooth image descriptions. The authors's method demonstrated good performance by comparing it with numerous SOTA methods on the MSCOCO dataset. image