In this paper, we propose a novel, convolutional neural network model to extract highly precise depth maps from missing viewpoints, especially well applicable to generate holographic 3D contents.
Despite the data labeling cost for the object detection tasks being substantially more than that of the classification tasks, semi-supervised learning methods for object detection have not been studied much.
Making a precise annotation in a large dataset is crucial to the performance of object detection.
In the source domain, we fully train an object detector and the RRPN with full supervision of HOI.
While the conventional methods cannot be applied to the new SSL problems where the separated data do not share the classes, our method does not show any performance degradation even if the classes of unlabeled data are different from those of the labeled data.
They are much faster than two stage detectors that use region proposal networks (RPN) without much degradation in the detection performances.
Semantic segmentation, like other fields of computer vision, has seen a remarkable performance advance by the use of deep convolution neural networks.
In this paper, we propose and analyze how to use feature maps effectively to improve the performance of the conventional SSD.