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Stephen J. Maybank

Researcher at Birkbeck, University of London

Publications -  172
Citations -  17723

Stephen J. Maybank is an academic researcher from Birkbeck, University of London. The author has contributed to research in topics: Video tracking & Motion estimation. The author has an hindex of 50, co-authored 166 publications receiving 15225 citations. Previous affiliations of Stephen J. Maybank include University of Oxford & University of Reading.

Papers
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Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking

TL;DR: Experimental evaluations against state-of-the-art algorithms demonstrate the promise and effectiveness of the proposed incremental tensor subspace learning algorithm, and its applications to foreground segmentation and object tracking.
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Robust Detection of Degenerate Configurations while Estimating the Fundamental Matrix

TL;DR: It is demonstrated that proper modeling of degeneracy in the presence of outliers enables the detection of mismatches which would otherwise be missed and is a generalization of the robust estimator RANSAC.
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Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection

TL;DR: A distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm, and an algorithm based on particle swarm optimization (PSO) and support vector machines is used to detect intrusions.
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Asymmetric 3D Convolutional Neural Networks for action recognition

TL;DR: The asymmetric 3D-CNN model outperforms all the traditional 3D -CNN models in both effectiveness and efficiency, and its performance is comparable with that of recent state-of-the-art action recognition methods on both benchmarks.
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Algorithm-Dependent Generalization Bounds for Multi-Task Learning

TL;DR: These theoretical analyses naturally show that the similarity of feature structures in MTL will lead to specific regularizations for predicting, which enables the learning algorithms to generalize fast and correctly from a few examples.