Image Captioning for Nantong Blue Calico Through Stacked Local-Global Channel Attention Network

Nantong blue calico, a Chinese folk hand-made printing and dyeing craft, has become one of intangible cultural heritages (ICHs) in China. To inherite and promote the ICH of Nantong blue calico, this study applies the image captioning technology to explaining blue-calico images. For this purpose, a novel image captioning method, called the stacked local-global channel attention network (SLGCAN), is proposed. This new network focuses on extracting important features from blue-calico images so that it can generate more accurate captions for blue-calico images. SLGCAN contains three parts, residual network (ResNet), stacked local-global channel attention module (SLGCAM), and Transformer. First, the pre-trained ResNet-101 model is used to extract rough features from blue-calico images and then, SLGCAM is to obtain the fine-grained information from rough image features. Eventually, SLGCAN adopts Transformer to encode and decode the fine-grained information of blue-calico images to predict the word information for generating accurate image captions. Experiments are conducted on a collected blue-calico image dataset. In experiments, we compare our SLGCAN with baseline models and show that that the proposed model is feasible and effective.