Situation Recognition With Graph Neural Networks

Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 4173-4182


We address the problem of recognizing situations in images. Given an image, the task is to predict the most salient verb (action), and fill its semantic roles such as who is performing the action, what is the source and target of the action, etc. Different verbs have different roles (e.g. attacking has weapon), and each role can take on many possible values (nouns). We propose a model based on Graph Neural Networks that allows us to efficiently capture joint dependencies between roles using neural networks defined on a graph. Experiments with different graph connectivities show that our approach that propagates information between roles significantly outperforms existing work, as well as multiple baselines. We obtain roughly 3-5% improvement over previous work in predicting the full situation. We also provide a thorough qualitative analysis of our model and influence of different roles in the verbs.

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[pdf] [supp] [arXiv]
author = {Li, Ruiyu and Tapaswi, Makarand and Liao, Renjie and Jia, Jiaya and Urtasun, Raquel and Fidler, Sanja},
title = {Situation Recognition With Graph Neural Networks},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}