Abstract: Provided by the present application are a method and a device for constructing integrated space-terrestrial network, the method including: obtaining a target constellation and standardizing a relative motion between each target satellite in the target constellation and earth's surface according to a principle of making a satellite running in a recursive earth-repeat orbit have fixed satellite subpoint trajectories; obtaining a recursively extended topological structure according to the target constellation; according to the extended topological structure, dividing geographical cells and obtaining a mapping relationship between the geographical cells and the satellite subpoint trajectories to facilitate network addressing and networking management based on geographical location; and performing, based on a deployment rule, a recursive incremental deployment according to the extended satellite network topological structure, and realizing integrated space-terrestrial network.

Abstract: There is provided methods, devices and techniques to process an image using a deep learning model to achieve continuous effect simulation by a unified network where a simple (effect class) estimator is embedded into a regular encoder-decoder architecture. The estimator allows learning of model-estimated class embeddings of all effect classes (e.g. progressive degrees of the effect), thus representing the continuous effect information without manual efforts in selecting proper anchor effect groups. In an embodiment, given a target age class, there is derived a personalized age embedding which considers two aspects of face aging: 1) a personalized residual age embedding at a model-estimated age of the subject, preserving the subject's aging information; and 2) exemplar-face aging basis at the target age, encoding the shared aging patterns among the entire population.

Abstract: GANs based generators are useful to perform image to image translations. GANs models have large storage sizes and resource use requirements such that they are too large to be deployed directly on mobile devices. Systems and methods define through conditioning a student GANs model having a student generator that is scaled downwardly from a teacher GANs model (and generator) using knowledge distillation. A semantic relation knowledge distillation loss is used to transfer semantic knowledge from an intermediate layer of the teacher to an intermediate layer of the student. Student generators thus defined are stored and executed by mobile devices such as smartphones and laptops to provide augmented reality experiences. Effects are simulated on images, including makeup, hair, nail and age simulation effects.