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In this paper, we introduce a contextual grounding approach that captures thecontext in corresponding text entities and image regions to improve thegrounding accuracy. Specifically, the proposed architecture accepts pre-trainedtext token embeddings and image object features from an off-the-shelf objectdetector as input. Additional encoding to capture the positional and spatialinformation can be added to enhance the feature quality. There are separatetext and image branches facilitating respective architectural refinements fordifferent modalities. The text branch is pre-trained on a large-scale maskedlanguage modeling task while the image branch is trained from scratch. Next,the model learns the contextual representations of the text tokens and imageobjects through layers of high-order interaction respectively. The finalgrounding head ranks the correspondence between the textual and visualrepresentations through cross-modal interaction. In the evaluation, we showthat our model achieves the state-of-the-art grounding accuracy of 71.36% overthe Flickr30K Entities dataset. No additional pre-training is necessary todeliver competitive results compared with related work that often requirestask-agnostic and task-specific pre-training on cross-modal dadasets. Theimplementation is publicly available at https://gitlab.com/necla-ml/grounding.

Contextual Grounding of Natural Language Entities in Images