A Unified Cycle-Consistent Neural Model for Text and Image Retrieval

Text-image retrieval has been recently becoming a hot-spot research field, thanks to the development of deeply-learnable architectures which can retrieve visual items given textual queries and vice-versa. The key idea of many state-of-the-art approaches has been that of learning a joint multi-modal embedding space in which text and images could be projected and compared. Here we take a different approach and reformulate the problem of text-image retrieval as that of learning a translation between the textual and visual domain. Our proposal leverages an end-to-end trainable architecture that can translate text into image features and vice versa and regularizes this mapping with a cycle-consistency criterion. Experimental evaluations for text-to-image and image-to-text retrieval, conducted on small, medium and large-scale datasets show consistent improvements over the baselines, thus confirming the appropriateness of using a cycle-consistent constrain for the text-image matching task.