AIC-AB NET: A Neural Network for Image Captioning with Spatial Attention
  and Text Attributes
Guoyun Tu Ying Liu Vladimir Vlassov
Image captioning is a significant field across computer vision and natural
language processing. We propose and present AIC-AB NET, a novel
Attribute-Information-Combined Attention-Based Network that combines spatial
attention architecture and text attributes in an encoder-decoder. For caption
generation, adaptive spatial attention determines which image region best
represents the image and whether to attend to the visual features or the visual
sentinel. Text attribute information is synchronously fed into the decoder to
help image recognition and reduce uncertainty. We have tested and evaluated our
AICAB NET on the MS COCO dataset and a new proposed Fashion dataset. The
Fashion dataset is employed as a benchmark of single-object images. The results
show the superior performance of the proposed model compared to the
state-of-the-art baseline and ablated models on both the images from MSCOCO and
our single-object images. Our AIC-AB NET outperforms the baseline adaptive
attention network by 0.017 (CIDEr score) on the MS COCO dataset and 0.095
(CIDEr score) on the Fashion dataset.