  We address the problem of phrase grounding by lear ing a multi-level common
semantic space shared by the textual and visual modalities. We exploit multiple
levels of feature maps of a Deep Convolutional Neural Network, as well as
contextualized word and sentence embeddings extracted from a character-based
language model. Following dedicated non-linear mappings for visual features at
each level, word, and sentence embeddings, we obtain multiple instantiations of
our common semantic space in which comparisons between any target text and the
visual content is performed with cosine similarity. We guide the model by a
multi-level multimodal attention mechanism which outputs attended visual
features at each level. The best level is chosen to be compared with text
content for maximizing the pertinence scores of image-sentence pairs of the
ground truth. Experiments conducted on three publicly available datasets show
significant performance gains (20%-60% relative) over the state-of-the-art in
phrase localization and set a new performance record on those datasets. We
provide a detailed ablation study to show the contribution of each element of
our approach and release our code on GitHub.
