  Performing data augmentation for learning deep neural networks is well known
to be important for training visual recognition systems. By artificially
increasing the number of training examples, it helps reducing overfitting and
improves generalization. For object detection, classical approaches for data
augmentation consist of generating images obtained by basic geometrical
transformations and color changes of original training images. In this work, we
go one step further and leverage segmentation annotations to increase the
number of object instances present on training data. For this approach to be
successful, we show that modeling appropriately the visual context surrounding
objects is crucial to place them in the right environment. Otherwise, we show
that the previous strategy actually hurts. With our context model, we achieve
significant mean average precision improvements when few labeled examples are
available on the VOC'12 benchmark.
