In this paper, we propose multimodal convolutional neural networks (m-CNNs)
for matching image and sentence. Our m-CNN provides an end-to-end framework
with convolutional architectures to exploit image representation, word
composition, and the matching relations between the two modalities. More
specifically, it consists of one image CNN encoding the image content, and one
matching CNN learning the joint representation of image and sentence. The
matching CNN composes words to different semantic fragments and learns the
inter-modal relations between image and the composed fragments at different
levels, thus fully exploit the matching relations between image and sentence.
Experimental results on benchmark databases of bidirectional image and sentence
retrieval demonstrate that the proposed m-CNNs can effectively capture the
information necessary for image and sentence matching. Specifically, our
proposed m-CNNs for bidirectional image and sentence retrieval on Flickr30K and
Microsoft COCO databases achieve the state-of-the-art performances.Comment: Accepted by ICCV 201