Generative Compositional Augmentations for Scene Graph Prediction

@article{Knyazev2021GenerativeCA,
  title={Generative Compositional Augmentations for Scene Graph Prediction},
  author={Boris Knyazev and Harm de Vries and Cătălina Cangea and Graham W. Taylor and Aaron C. Courville and Eugene Belilovsky},
  journal={2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021},
  pages={15807-15817}
}
Inferring objects and their relationships from an image in the form of a scene graph is useful in many applications at the intersection of vision and language. We consider a challenging problem of compositional generalization that emerges in this task due to a long tail data distribution. Current scene graph generation models are trained on a tiny fraction of the distribution corresponding to the most frequent compositions, e.g. . However, test images might contain zero- and few-shot… 
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