UCLA

We are interested in training complementary networks for large-scale image classification. This is motivated by the observation that deep neural networks learn different visual concepts from the foreground and background regions (defined by the object bounding box) of natural images. We construct complementary training image sets for this purpose. That is to say, in each training image, either the foreground or background region, defined by object bounding boxes, is removed. We train deep neural networks on these modified datasets, and show the possibility of image classification even using a network trained on pure background visual contents. We visualize the neural networks, and demonstrate that networks trained with different datasets capture complementary information. These complementary networks are combined at the testing stage on two conditions with and without bounding box(es), producing remarkable gain in recognition accuracy.