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Grabber: A tool to improve convergence in interactive image segmentation

Authors:  JordãoBragantini, BrunoMoura, AlexandreXavierFalcão....
Published date-12/01/2020
Tasks:  SemanticSegmentation

Abstract: Interactive image segmentation has considerably evolved from techniques that do not learn the parameters of the model to methods that pre-train a model and adapt it from user inputs during …
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Baxter Permutation Process

Authors:  MasahiroNakano, AkisatoKimura, TakeshiYamada....
Published date-12/01/2020
Tasks:  BayesianInference

Abstract: In this paper, a Bayesian nonparametric (BNP) model for Baxter permutations (BPs), termed BP process (BPP) is proposed and applied to relational data analysis. The BPs are a well-studied class …
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Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning​

Authors:  ShreyasFadnavis, JoshuaBatson, EleftheriosGaryfallidis....
Published date-12/01/2020
Tasks:  Denoising, Self-SupervisedLearning

Abstract: Diffusion-weighted magnetic resonance imaging (DWI) is the only non-invasive method for quantifying microstructure and reconstructing white-matter pathways in the living human brain. Fluctuations from multiple sources create significant noise in …
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Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency

Authors:  FangZhao, ShengcaiLiao, KaihaoZhang....
Published date-12/01/2020
Tasks:  HumanParsing, SemanticParsing, TextureSynthesis

Abstract: This paper proposes a human parsing based texture transfer model via cross-view consistency learning to generate the texture of 3D human body from a single image. We use the semantic …
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Efficient Low Rank Gaussian Variational Inference for Neural Networks

Authors:  MarcinTomczak, SiddharthSwaroop, RichardTurner....
Published date-12/01/2020
Tasks:  VariationalInference

Abstract: Bayesian neural networks are enjoying a renaissance driven in part by recent advances in variational inference (VI). The most common form of VI employs a fully factorized or mean-field distribution, …
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How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods

Authors:  JeyaVikranthJeyakumar, JosephNoor, Yu-HsiCheng....
Published date-12/01/2020
Tasks:  SentimentAnalysis

Abstract: Explaining the inner workings of deep neural network models have received considerable attention in recent years. Researchers have attempted to provide human parseable explanations justifying why a model performed a …
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