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We propose a simple, fast, and accurate one-stage approach to visualgrounding, inspired by the following insight. The performances of existingpropose-and-rank two-stage methods are capped by the quality of the regioncandidates they propose in the first stage --- if none of the candidates couldcover the ground truth region, there is no hope in the second stage to rank theright region to the top. To avoid this caveat, we propose a one-stage modelthat enables end-to-end joint optimization. The main idea is as straightforwardas fusing a text query's embedding into the YOLOv3 object detector, augmentedby spatial features so as to account for spatial mentions in the query. Despitebeing simple, this one-stage approach shows great potential in terms of bothaccuracy and speed for both phrase localization and referring expressioncomprehension, according to our experiments. Given these results along withcareful investigations into some popular region proposals, we advocate forvisual grounding a paradigm shift from the conventional two-stage methods tothe one-stage framework.

A Fast and Accurate One-Stage Approach to Visual Grounding