Beyond Deep

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Beyond the Deep: Deadly Descent into the World's Most Treacherous Cave

Email Address. Sign In. Access provided by: anon Sign Out. Building semantic understanding beyond deep learning from sound and vision Abstract: Deep learning-based models have recently been widely successful at outperforming traditional approaches in several computer vision applications such as image classification, object recognition and action recognition.

However, those models are not naturally designed to learn structural information that can be important to tasks such as human pose estimation and structured semantic interpretation of video events. In this paper, we demonstrate how to build structured semantic understanding of audio-video events by reasoning on multiple-label decisions of deep visual models and auditory models using Grenander's structures for imposing semantic consistency. The proposed structured model does not require joint training of the structural semantic dependencies and deep models.

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Instead they are independent components linked by Grenander's structures. Furthermore, we exploited Grenander's structures as a means to facilitate and enrich the model with fusion of multimodal sensory data; in particular, auditory features with visual features. Upcoming SlideShare.

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Beyond Deep Learning

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Deep residual learning based on wavelet transform 2. Advantages of Wavelet decomposition 1. Make more simple data 2.

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Reduction of patch size efficiently 1 Reduction of network run time and learning time 2 Reduction of minimal receptive if the training data is simple Emphasize various frequencies due to wavelet transform Be careful not to be biased on one frequency [ Down-sampled image ] [ Original image ] pixel pixel 40 pixel 80 pixel pixel [ Original image ] [ Patch size between decomposed and original] Paper Day for CVPR Comparison by depth of filter 1. The deeper the filter depth, the more expressive and powerful.

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The deeper the layer, the more expressive and powerful. Comparison by method : PQF 1.

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Because the networks are exactly the same, accurate comparisons are possible. Network power and learning level are important for accurate comparison. Learning for Gaussian denoising 1. Initialization using improved Xavier method 3. Data augmentation : flip, rotation, random cropping 5.