  Performing data augmentation for learning deep neural networks is known to be
important for training visual recognition systems. By artificially increasing
the number of training examples, it helps reducing overfitting and improves
generalization. While simple image transformations can already improve
predictive performance in most vision tasks, larger gains can be obtained by
leveraging task-specific prior knowledge. In this work, we consider object
detection, semantic and instance segmentation and augment the training images
by blending objects in existing scenes, using instance segmentation
annotations. We observe that randomly pasting objects on images hurts the
performance, unless the object is placed in the right context. To resolve this
issue, we propose an explicit context model by using a convolutional neural
network, which predicts whether an image region is suitable for placing a given
object or not. In our experiments, we show that our approach is able to improve
object detection, semantic and instance segmentation on the PASCAL VOC12 and
COCO datasets, with significant gains in a limited annotation scenario, i.e.
when only one category is annotated. We also show that the method is not
limited to datasets that come with expensive pixel-wise instance annotations
and can be used when only bounding boxes are available, by employing
weakly-supervised learning for instance masks approximation.
