Titlebook: Neural Information Processing; 26th International C Tom Gedeon,Kok Wai Wong,Minho Lee Conference proceedings 2019 Springer Nature Switzerla

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Image Denoising Networks with Residual Blocks and RReLUs application range is greatly restricted by the specialized task (i.e., a specific model is required for each considered noise level), which prompts us to train a single network to tackle the blind image denoising problem. To this end, we take the advantages of state-of-the-art progress in deep lear

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Shape Description and Retrieval in a Fused Scale Spaception. First, morphological operations and the Gaussian smoothing are jointly used to produce a fused scale-space description of the input shape, which is able to handle strong noise, intra-class shape variation and irregular deformation simultaneously. Then, the height-function features of the shap

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Only Image Cosine Embedding for Few-Shot Learningning, a method of learning to learn, is introduced into few-shot learning problem and has achieved pretty good results. But there is still a very big gap between the machine and our human in the few-shot learning tasks. We think it’s because the existing methods do not make full use of global knowle

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Deep 3D Segmentation and Classification of Point Clouds for Identifying AusRAP Attributesement. The major challenges are accurately segmenting and classifying AusRAP attributes. Researchers have focused on sematic segmentation and object classification to address the challenges mostly in 2D image setting, and few of them have recently extended techniques from 2D to 3D setting. However,

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