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ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The ICASSP 2020 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website.
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Tele-wide camera system with different Field of View (FoV) lenses becomes very popular in recent mobile devices. Usually it is difficult to obtain full-FoV depth based on traditional stereo-matching methods. Pure Deep Neural Network (DNN) based depth estimation methods can obtain full-FoV depth, but have low robustness for scenarios which are not covered by training dataset.
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- Read more about AN ATTENTION ENHANCED MULTI-TASK MODEL FOR OBJECTIVE SPEECH ASSESSMENT IN REAL-WORLD ENVIRONMENTS
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slide_1603.pdf
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- Read more about Allocation of Computing Tasks in Distributed MEC Servers Co-Powered by Renewable Sources and the Power Grid
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- Read more about NON-UNIFORM VIDEO TIME-LAPSE METHOD BASED ON MOTION SCENARIO AND STABILIZATION CONSTRAINT
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Time-lapse of user captured video becomes popular in many applications recently, non-uniform sampling and digital video stabilization (VS) are usually two independent steps to keep meaningful contents and provide stabilized output. However, non-uniform sampling may produce large sampling interval and then result in larger motion, this would beyond the stabilization ability of VS and produce unpleasant output.
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- Read more about Deep geometric knowledge distillation with graphs
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- Read more about INTERPRETABLE SELF-ATTENTION TEMPORAL REASONING FOR DRIVING BEHAVIOR UNDERSTANDING
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Performing driving behaviors based on causal reasoning is essential to ensure driving safety. In this work, we investigated how state-of-the-art 3D Convolutional Neural Networks (CNNs) perform on classifying driving behaviors based on causal reasoning. We proposed a perturbation-based visual explanation method to inspect the models' performance visually. By examining the video attention saliency, we found that existing models could not precisely capture the causes (e.g., traffic light) of the specific action (e.g., stopping).
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- Read more about Parsing Map Guided Multi-Scale Attention Network For Face Hallucination
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4929wang.pdf
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- Read more about Automatic Classification of Volumes of Water using Swallow Sounds from Cervical Auscultation
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The signatures of swallowing vary depending on the volume of bolus swallowed. Among existing instrumental methods, cervical auscultation (CA) captures the acoustic signatures of the swallow sound. Although many features present in the literature can characterize volumes of swallow using CA, they require manual annotations of the different components in the sound.
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- Read more about Decentralized Optimization with Non-Identical Sampling in Presence of Stragglers
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We consider decentralized consensus optimization when workers sample data from non-identical distributions and perform variable amounts of work due to slow nodes known as stragglers. The problem of non-identical distributions and the problem of variable amount of work have been previously studied separately. In our work we analyse them together under a unified system model. We propose to combine worker outputs weighted by the amount of work completed by each.
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- Read more about LEARNING GEOMETRIC FEATURES WITH DUAL-STREAM CNN FOR 3D ACTION RECOGNITION
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Recently, regarding several beneficial properties of depth camera, numerous 3D action recognition frameworks have studied high-level features by exploiting deep learning techniques, but nevertheless they cannot seize the meaningful characteristics of static human pose and dynamic action motion of a whole action. This paper introduces a deep network configured by two parallel streams of convolutional stacks for fully learning the deep intra-frame joint associations and inter-frame joint correlations, wherein the structure of each stream is learned from Inception-v3.
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