  Image and sentence matching has made great progress recently, but it remains
challenging due to the large visual-semantic discrepancy. This mainly arises
from that the representation of pixel-level image usually lacks of high-level
semantic information as in its matched sentence. In this work, we propose a
semantic-enhanced image and sentence matching model, which can improve the
image representation by learning semantic concepts and then organizing them in
a correct semantic order. Given an image, we first use a multi-regional
multi-label CNN to predict its semantic concepts, including objects,
properties, actions, etc. Then, considering that different orders of semantic
concepts lead to diverse semantic meanings, we use a context-gated sentence
generation scheme for semantic order learning. It simultaneously uses the image
global context containing concept relations as reference and the groundtruth
semantic order in the matched sentence as supervision. After obtaining the
improved image representation, we learn the sentence representation with a
conventional LSTM, and then jointly perform image and sentence matching and
sentence generation for model learning. Extensive experiments demonstrate the
effectiveness of our learned semantic concepts and order, by achieving the
state-of-the-art results on two public benchmark datasets.

Computer Vision and Pattern Recognition