IEEE ICASSP 2020 Virtual Conference May 2020

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  • Multi-Task Learning Via Sa-Fpn And Ej-Head

    00:13:37
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    As a concise framework, Mask R-CNN achieves promising performance in object detection and instance segmentation. However, there is room for improvement in two aspects. One is that performing multi-task prediction needs more credible feature extraction and
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  • Upscaling Vector Approximate Message Passing

    00:10:30
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    In this paper we consider the problem of recovering a signal x of size N from noisy and compressed measurements y = A x + w of size M, where the measurement matrix A is right-orthogonally invariant (ROI). Vector Approximate Message Passing (VAMP) demonstr
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  • Mental Fatigue Prediction From Multi-Channel Ecog Signal

    00:13:28
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    Early detection of mental fatigue and changes in vigilance could be used to initiate neurostimulation to treat patients suffering from brain injury and mental disorders. In this study, we analyzed electrocorticography (ECoG) signals chronically recorded f
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  • High Dynamic Range Imaging Using Deep Image Priors

    00:15:34
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    Traditionally, dynamic range enhancement for images has involved a combination of contrast improvement (via gamma correction or histogram equalization) and a denoising operation to reduce the effects of photon noise. More recently, modulo-imaging methods
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  • Label Reuse For Efficient Semi-Supervised Learning

    00:14:26
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    In this paper, we propose a new learning strategy for semi-supervised deep learning algorithms, called label reuse, aiming to significantly reduce the expensive computational cost of pseudo label generation and the like for each unlabeled training instanc
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  • Lai-Net: Local-Ancestry Inference With Neural Networks

    00:16:24
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    Local-ancestry inference (LAI), also referred to as ancestry deconvolution, provides high-resolution ancestry estimation along the human genome. In both research and industry, LAI is emerging as a critical step in personalized DNA sequence analysis with a
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  • M-Estimators Of Scatter With Eigenvalue Shrinkage

    00:16:46
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    A popular regularized (shrinkage) covariance estimator is the shrinkage sample covariance matrix (SCM) which shares the same set of eigenvectors as the SCM but shrinks its eigenvalues toward its grand mean. In this paper, a more general approach is consid
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  • Deep Multi-Region Hashing

    00:13:55
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    Hashing has been widely used for large-scale approximate nearest neighbors retrieval own to its high efficiency. In the existing hashing methods, deep supervised hashing methods have achieved the best performance by utilizing the semantic labels on data w