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Abstract:The development of action recognition models has shown great performance on various video datasets. Nevertheless, because there is no rich data on target actions in existing datasets, it is insufficient to perform action recognition applications required by industries. To satisfy this requirement, datasets composed of target actions with high availability have been created, but it is difficult to capture various characteristics in actual environments because video data are generated in a specific environment. In this paper, we introduce a new ETRI-Activity3D-LivingLab dataset, which provides action sequences in actual environments and helps to handle a network generalization issue due to the dataset shift. When the action recognition model is trained on the ETRI-Activity3D and KIST SynADL datasets and evaluated on the ETRI-Activity3D-LivingLab dataset, the performance can be severely degraded because the datasets were captured in different environments domains. To reduce this dataset shift between training and testing datasets, we propose a close-up of maximum activation, which magnifies the most activated part of a video input in detail. In addition, we present various experimental results and analysis that show the dataset shift and demonstrate the effectiveness of the proposed method.Keywords: action recognition; dataset shift; self-attention map
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We investigate the linear stability of inviscid flows which are subject to a conservativebody force. This includes a broad range of familiar conservative systems, such asideal MHD, natural convection, flows driven by electrostatic forces and axisymmetric,swirling, recirculating flow. We provide a simple, unified, linear stability criterion validfor any conservative system. In particular, we establish a principle of maximum actionof the form 041b061a72