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Associating image regions with text queries has been recently explored as anew way to bridge visual and linguistic representations. A few pioneeringapproaches have been proposed based on recurrent neural language models trainedgeneratively (e.g., generating captions), but achieving somewhat limitedlocalization accuracy. To better address natural-language-based visual entitylocalization, we propose a discriminative approach. We formulate adiscriminative bimodal neural network (DBNet), which can be trained by aclassifier with extensive use of negative samples. Our training objectiveencourages better localization on single images, incorporates text phrases in abroad range, and properly pairs image regions with text phrases into positiveand negative examples. Experiments on the Visual Genome dataset demonstrate theproposed DBNet significantly outperforms previous state-of-the-art methods bothfor localization on single images and for detection on multiple images. We wealso establish an evaluation protocol for natural-language visual detection.

Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries