Biblio

Despite significant recent progress, the best available computer vision algorithms still lag far behind human capabilities, even for recognizing individual discrete objects under various poses, illuminations, and backgrounds. Here we present a new approach to using object pose information to improve deep network learning. While existing large-scale datasets, e.g. ImageNet, do not have pose information, we leverage the newly published turntable dataset, iLab-20M, which has 22M images of 704 object instances shot under different lightings, camera viewpoints and turntable rotations, to do more controlled object recognition experiments. We introduce a new convolutional neural network architecture, what/where CNN (2W-CNN), built on a linear-chain feedforward CNN (e.g., AlexNet), augmented by hierarchical layers regularized by object poses. Pose information is only used as feedback signal during training, in addition to category information, but is not needed during test. To validate the approach, we train both 2W-CNN and AlexNet using a fraction of the dataset, and 2W-CNN achieves 6 percent performance improvement in category prediction. We show mathematically that 2W-CNN has inherent advantages over AlexNet under the stochastic gradient descent (SGD) optimization procedure. Furthermore, we fine-tune object recognition on ImageNet by using the pretrained 2W-CNN and AlexNet features on iLab-20M, results show significant improvement compared with training AlexNet from scratch. Moreover, fine-tuning 2W-CNN features performs even better than fine-tuning the pretrained AlexNet features. These results show that pretrained features on iLab-20M generalize well to natural image datasets, and 2W-CNN learns better features for object recognition than AlexNet.