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Extracting roads in aerial images has numerous applications in artificial intelligence and multimedia computing, including traffic pattern analysis and parking space planning. Learning deep neural networks, though very successful, demands vast amounts of high-quality annotations, of which acquisition is time-consuming and expensive. In this work, we propose a semi-supervised approach for image-based road extraction where only a small set of labeled images are available for training to address this challenge. We design a pixel-wise contrastive loss to self-supervise the network training to utilize the large corpus of unlabeled images. The key idea is to identify pairs of overlapping image regions (positive) or non-overlapping image regions (negative) and encourage the network to make similar outputs for positive pairs or dissimilar outputs for negative pairs. We also develop a negative sampling strategy to filter false negative samples during the process. An iterative procedure is introduced to apply the network over raw images to generate pseudo-labels, filter and select high-quality labels with the proposed contrastive loss, and re-train the network with the enlarged training dataset. We repeat these iterative steps until convergence. We validate the effectiveness of the proposed methods by performing extensive experiments on the public SpaceNet3 and DeepGlobe Road datasets. Results show that our proposed method achieves state-of-the-art results on public image segmentation benchmarks and significantly outperforms other semi-supervised methods.

 

In this paper, we propose a new design framework on Device-to-Device (D2D) coded caching networks with optimal communication load (rate) but significantly less file subpacketizations compared to that of the well-known D2D coded caching scheme proposed by Ji, Caire and Molisch (JCM). The proposed design framework is referred to as the Packet Type-based (PTB) design, where each file is partitioned into packets according to their pre-defined types while the cache placement and user multicast grouping are based on the packet types. This leads to the so-called raw packet saving gain for the subpacketization levels. By a careful selection of transmitters within each multicasting group, a so-called further splitting ratio gain of the subpacketizatios can also be achieved. By the joint effect of the raw packet saving gain and the further splitting ratio gain, an order-wise subpacketization reduction can be achieved compared to the JCM scheme while preserving the optimal rate. In addition, as the first time presented in the literature according to our knowledge, we find that unequal subpacketizaton is a key to achieve subpacketization reductions when the number of users is odd. As a by-product, instead of directly translating shared link caching schemes to D2D caching schemes, at least for the sake of subpackeitzation, a new design framework is indeed needed.