Extending Phrase Grounding with Pronouns in Visual Dialogues
This work addresses a limitation in cross-modal visual language understanding for researchers by extending phrase grounding to include pronouns, though it is incremental as it builds on existing methods.
The paper tackles the problem of extending phrase grounding to include pronouns in visual dialogues, constructing a new dataset and enhancing a baseline model with coreference information using graph convolutional networks. The results show that pronouns are easier to ground than noun phrases, and the final model significantly boosts grounding performance for both.
Conventional phrase grounding aims to localize noun phrases mentioned in a given caption to their corresponding image regions, which has achieved great success recently. Apparently, sole noun phrase grounding is not enough for cross-modal visual language understanding. Here we extend the task by considering pronouns as well. First, we construct a dataset of phrase grounding with both noun phrases and pronouns to image regions. Based on the dataset, we test the performance of phrase grounding by using a state-of-the-art literature model of this line. Then, we enhance the baseline grounding model with coreference information which should help our task potentially, modeling the coreference structures with graph convolutional networks. Experiments on our dataset, interestingly, show that pronouns are easier to ground than noun phrases, where the possible reason might be that these pronouns are much less ambiguous. Additionally, our final model with coreference information can significantly boost the grounding performance of both noun phrases and pronouns.