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The problem of language grounding has attracted much attention in recentyears due to its pivotal role in more general image-lingual high levelreasoning tasks (e.g., image captioning, VQA). Despite the tremendous progressin visual grounding, the performance of most approaches has been hindered bythe quality of bounding box proposals obtained in the early stages of allrecent pipelines. To address this limitation, we propose a general progressivequery-guided bounding box refinement architecture (OptiBox) that leveragesglobal image encoding for added context. We apply this architecture in thecontext of the GroundeR model, first introduced in 2016, which has a number ofunique and appealing properties, such as the ability to learn in thesemi-supervised setting by leveraging cyclic language-reconstruction. UsingGroundeR + OptiBox and a simple semantic language reconstruction loss that wepropose, we achieve state-of-the-art grounding performance in the supervisedsetting on Flickr30k Entities dataset. More importantly, we are able to surpassmany recent fully supervised models with only 50% of training data and performcompetitively with as low as 3%.

OptiBox: Breaking the Limits of Proposals for Visual Grounding