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Efficiently processing massive video data raises many difficult challenges. For example, it is hard to segment objects in a video sequence, which forms the basis for many tasks, such as object detection and tracking. Furthermore, some video datasets may be huge, making it hard to obtain an accurate segmentation. Whereas existing methods solve these problems almost completely, they may have a high cost or memory requirement due to computing a saliency map or a Gaussian filter for each frame. In this paper, we propose a new approach to video saliency estimation based on attention mechanisms, which leads to (i) much cleaner and more accurate saliency maps, (ii) more efficient computational cost and (iii) little extra memory requirement. The idea is to split segmentation in two steps. In the first step, a feature attention network is trained to focus on salient patches in video frames. As a result, subsequent segmentation is done on these patches. The saliency of an entire video is then inferred via aggregating the saliencies of all patches. We evaluate the efficiency and saliency qualities of this approach on three challenging datasets: Sintel, GOT-10K and Google Street View Dataset Challenge.

Finite Time Markov Decision Processes (MDPs) are central to a wide range of domains in artificial intelligence. They can be used either to model the evolution of systems, or to represent scenarios for which the optimality of instantaneous policies is not a necessity. In this work, we design a general finite-time algorithm for the moment closure solution of linear programs. We prove results about the stability of the resulting system of algebraic equations, and illustrate their usefulness in a transferability study of finite-time value functions. For this, we compare our results to an existing, state-of-the-art algorithm and observe that our approach produces comparable solutions that are more accurate and robust to initial conditions. In a scenario study involving portfolio optimization with stochastic returns, we demonstrate how the finite-time algorithm performs in practice. d2c66b5586


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