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Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples...
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate...
Active attacks threat the security of communication dramatically. In this paper, we propose a secure transmission scheme based on random orthogonal pilots to detect the presence of active eavesdropping attacks on channel estimation in time-division-duplex (TDD) systems. The scheme only needs to replace some regular pilot sequences with random orthogonal pilot sequences occasionally without any prior...
Timely and accurate estimation of rice planting area would greatly optimize our prediction of rice production, which provides invaluable information for government in formulating policies with regard to national food security. Previous studies have shown great potential of optical remote sensing as an effective way to map rice planting area. Commonly used classification techniques, which mainly focus...
In this paper, we tackle the problem of common object (multiple classes) discovery from a set of input images, where we assume the presence of one object class in each image. This problem is, loosely speaking, unsupervised since we do not know a priori about the object type, location, and scale in each image. We observe that the general task of object class discovery in a fully unsupervised manner...
Discovering object classes from images in a fully unsupervised way is an intrinsically ambiguous task; saliency detection approaches however ease the burden on unsupervised learning. We develop an algorithm for simultaneously localizing objects and discovering object classes via bottom-up (saliency-guided) multiple class learning (bMCL), and make the following contributions: (1) saliency detection...
This paper proposes a statistical generative model to generate sentences from an annotated picture. The images are segmented into regions (using Graph-based algorithms) and then features are computed over each of these regions. Given a training set of images with annotations, we parse the image to get position information. We use SVM to get the probabilities of combinations between labels and prepositions,...
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