MSRC

Given an image and a natural language query phrase, a grounding system localizes the mentioned objects in the image according to the query's specifications. State-of-the-art methods address the problem by ranking a set of proposal bounding boxes according to the query's semantics, which makes them dependent on the performance of proposal generation systems. Besides, query phrases in one sentence may be semantically related in one sentence and can provide useful cues to ground objects. We propose a novel Multimodal Spatial Regression with semantic Context (MSRC) system which not only predicts the location of ground truth based on proposal bounding boxes, but also refines prediction results by penalizing similarities of different queries coming from same sentences. The advantages of MSRC are twofold: first, it removes the limitation of performance from proposal generation algorithms by using a spatial regression network. Second, MSRC not only encodes the semantics of a query phrase, but also deals with its relation with other queries in the same sentence (i.e., context) by a context refinement network. Experiments show MSRC system provides a significant improvement in accuracy on two popular datasets: Flickr30K Entities and Refer-it Game, with 6.64% and 5.28% increase over the state-of-the-arts respectively.