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Weakly-supervised Visual Grounding of Phrases with Linguistic Structures
We propose a weakly-supervised approach that takes image-sentence pairs as
input and learns to visually ground (i.e., localize) arbitrary linguistic
phrases, in the form of spatial attention masks. Specifically, the model is
trained with images and their associated image-level captions, without any
explicit region-to-phrase correspondence annotations. To this end, we introduce
an end-to-end model which learns visual groundings of phrases with two types of
carefully designed loss functions. In addition to the standard discriminative
loss, which enforces that attended image regions and phrases are consistently
encoded, we propose a novel structural loss which makes use of the parse tree
structures induced by the sentences. In particular, we ensure complementarity
among the attention masks that correspond to sibling noun phrases, and
compositionality of attention masks among the children and parent phrases, as
defined by the sentence parse tree. We validate the effectiveness of our
approach on the Microsoft COCO and Visual Genome datasets.Comment: CVPR 201
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