Abstract

We introduce new approaches for augmenting annotated training datasets used for object detection tasks that serve achieving two goals: reduce the effort needed for collecting and manually annotating huge datasets and introduce novel variations to the initial dataset that help the learning algorithms. The methods presented in this work aim at relocating objects using their segmentation masks to new backgrounds. These variations comprise changes in properties of objects such as spatial location in the image, surrounding context and scale. We propose a model selection approach to arbitrate between the constructed model on a per class basis. Experimental results show gains that can be harvested using the proposed approach.

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