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Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2

作者： Mohammad Saberian Nuno Vasconcelos Yahoo! Labs Statistical Visual Computing Laboratory University of California San Diego

An algorithm for learning fast multiclass object detection cascades is introduced. It produces multi-resolution (MRes) cascades, whose early stages are binary target vs. non-target detectors that eliminate false positives, late stages multiclass classifiers that finely discriminate target classes, and middle stages have intermediate numbers of classes, determined in a data-driven manner. This MRes structure is achieved with a new structurally biased boosting algorithm (SBBoost). SBBost extends previous multiclass boosting approaches, whose boosting mechanisms are shown to implement two complementary data-driven biases: 1) the standard bias towards examples difficult to classify, and 2) a bias towards difficult classes. It is shown that structural biases can be implemented by generalizing this class-based bias, so as to encourage the desired MRes structure. This is accomplished through a generalized definition of multiclass margin, which includes a set of bias parameters. SBBoost is a boosting algorithm for maximization of this margin. It can also be interpreted as standard multiclass boosting algorithm augmented with margin thresholds or a cost-sensitive boosting algorithm with costs defined by the bias parameters. A stage adaptive bias policy is then introduced to determine bias parameters in a data driven manner. This is shown to produce MRes cascades that have high detection rate and are computationally efficient. Experiments on multiclass object detection show improved performance over previous solutions.

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A view of margin losses as regularizers of probability estimates

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2015年第1期16卷

作者： Hamed Masnadi-Shirazi Nuno Vasconcelos School of Electrical and Computer Engineering Shiraz University Shiraz Iran Statistical Visual Computing Laboratory University of California San Diego La Jolla CA

Regularization is commonly used in classifier design, to assure good generalization. Classical regularization enforces a cost on classifier complexity, by constraining parameters. This is usually combined with a margin loss, which favors large-margin decision rules. A novel and unified view of this architecture is proposed, by showing that margin losses act as regularizers of posterior class probabilities, in a way that amplifies classical parameter regularization. The problem of controlling the regularization strength of a margin loss is considered, using a decomposition of the loss in terms of a link and a binding function. The link function is shown to be responsible for the regularization strength of the loss, while the binding function determines its outlier robustness. A large class of losses is then categorized into equivalence classes of identical regularization strength or outlier robustness. It is shown that losses in the same regularization class can be parameterized so as to have tunable regularization strength. This parameterization is finally used to derive boosting algorithms with loss regularization (BoostLR). Three classes of tunable regularization losses are considered in detail. Canonical losses can implement all regularization behaviors but have no flexibility in terms of outlier modeling. Shrinkage losses support equally parameterized link and binding functions, leading to boosting algorithms that implement the popular shrinkage procedure. This offers a new explanation for shrinkage as a special case of loss-based regularization. Finally, α-tunable losses enable the independent parameterization of link and binding functions, leading to boosting algorithms of great exibility. This is illustrated by the derivation of an algorithm that generalizes both AdaBoost and LogitBoost, behaving as either one when that best suits the data to classify. Various experiments provide evidence of the benefits of probability regularization for both classification a

关键词： binding functions boosting classification generalization link functions loss functions margin losses probability elicitation regularization shrinkage

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Boosting algorithms for detector cascade learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2014年第1期15卷

作者： Kevin Murphy Bernhard Schölkopf Mohammad Saberian Nuno Vasconcelos Google Statistical Visual Computing Laboratory University of California San Diego La Jolla CA

The problem of learning classifier cascades is considered. A new cascade boosting algorithm, fast cascade boosting (FCBoost), is proposed. FCBoost is shown to have a number of interesting properties, namely that it 1) minimizes a Lagrangian risk that jointly accounts for classification accuracy and speed, 2) generalizes adaboost, 3) can be made cost-sensitive to support the design of high detection rate cascades, and 4) is compatible with many predictor structures suitable for sequential decision making. It is shown that a rich family of such structures can be derived recursively from cascade predictors of two stages, denoted cascade generators. Generators are then proposed for two new cascade families, last-stage and multiplicative cascades, that generalize the two most popular cascade architectures in the literature. The concept of neutral predictors is finally introduced, enabling FCBoost to automatically determine the cascade configuration, i.e., number of stages and number of weak learners per stage, for the learned cascades. Experiments on face and pedestrian detection show that the resulting cascades outperform current state-of-the-art methods in both detection accuracy and speed.

关键词： boosting complexity-constrained learning cost-sensitive learning detector cascades ensemble methods real-time object detection sequential decision-making

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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On the connections between saliency and tracking

On the connections between saliency and tracking

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26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012

作者： Mahadevan, Vijay Vasconcelos, Nuno Yahoo Labs Bangalore India Statistical Visual Computing Laboratory UC San Diego San Diego CA 92092 United States

ISBN: (纸本)9781627480031

A model connecting visual tracking and saliency has recently been proposed. This model is based on the saliency hypothesis for tracking which postulates that tracking is achieved by the top-down tuning, based on target features, of discriminant center-surround saliency mechanisms over time. In this work, we identify three main predictions that must hold if the hypothesis were true: 1) tracking reliability should be larger for salient than for non-salient targets, 2) tracking reliability should have a dependence on the defining variables of saliency, namely feature contrast and distractor heterogeneity, and must replicate the dependence of saliency on these variables, and 3) saliency and tracking can be implemented with common low level neural mechanisms. We confirm that the first two predictions hold by reporting results from a set of human behavior studies on the connection between saliency and tracking. We also show that the third prediction holds by constructing a common neurophysiologically plausible architecture that can computationally solve both saliency and tracking. This architecture is fully compliant with the standard physiological models of V1 and MT, and with what is known about attentional control in area LIP, while explaining the results of the human behavior experiments.

关键词： Forecasting

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On the connections between saliency and tracking 12

On the connections between saliency and tracking

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Annual Conference on Neural Information Processing Systems

作者： Vijay Mahadevan Nuno Vasconcelos Yahoo! Labs Bangalore India Statistical Visual Computing Laboratory UC San Diego La Jolla CA 92092

ISBN: (纸本)9781627480031

A model connecting visual tracking and saliency has recently been proposed. This model is based on the saliency hypothesis for tracking which postulates that tracking is achieved by the top-down tuning, based on target features, of discriminant center-surround saliency mechanisms over time. In this work, we identify three main predictions that must hold if the hypothesis were true: 1) tracking reliability should be larger for salient than for non-salient targets, 2) tracking reliability should have a dependence on the defining variables of saliency, namely feature contrast and distractor heterogeneity, and must replicate the dependence of saliency on these variables, and 3) saliency and tracking can be implemented with common low level neural mechanisms. We confirm that the first two predictions hold by reporting results from a set of human behavior studies on the connection between saliency and tracking. We also show that the third prediction holds by constructing a common neurophysiologically plausible architecture that can computationally solve both saliency and tracking. This architecture is fully compliant with the standard physiological models of VI and MT, and with what is known about attentional control in area LIP, while explaining the results of the human behavior experiments.

关键词： tracking Connections Human behavior visual tracking

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Multiclass boosting: Theory and algorithms

Multiclass boosting: Theory and algorithms

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25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011

作者： Saberian, Mohammad J. Vasconcelos, Nuno Statistical Visual Computing Laboratory University of California San Diego United States

ISBN: (纸本)9781618395993

The problem of multi-class boosting is considered. A new framework, based on multi-dimensional codewords and predictors is introduced. The optimal set of codewords is derived, and a margin enforcing loss proposed. The resulting risk is minimized by gradient descent on a multidimensional functional space. Two algorithms are proposed: 1) CD-MCBoost, based on coordinate descent, updates one predictor component at a time, 2) GD-MCBoost, based on gradient descent, updates all components jointly. The algorithms differ in the weak learners that they support but are both shown to be 1) Bayes consistent, 2) margin enforcing, and 3) convergent to the global minimum of the risk. They also reduce to AdaBoost when there are only two classes. Experiments show that both methods outperform previous multiclass boosting approaches on a number of datasets.

关键词： Adaptive boosting

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Multiclass Boosting: Theory and Algorithms 11

Multiclass Boosting: Theory and Algorithms

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Annual Conference on Neural Information Processing Systems

作者： Mohammad J. Saberian Nuno Vasconcelos Statistical Visual Computing Laboratory University of California San Diego

ISBN: (纸本)9781618395993

关键词： multi-class algorithms Boost descent Global Minimum Dataset codeword

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Risk minimization, probability elicitation, and cost-sensitive SVMs

Risk minimization, probability elicitation, and cost-sensiti...

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27th International Conference on Machine Learning, ICML 2010

作者： Masnadi-Shirazi, Hamed Vasconcelos, Nuno Statistical Visual Computing Laboratory University of California San Diego San Diego CA 92039 United States

ISBN: (纸本)9781605589077

A new procedure for learning cost-sensitive SVM classifiers is proposed. The SVM hinge loss is extended to the cost sensitive setting, and the cost-sensitive SVM is derived as the mini-mizer of the associated risk. The extension of the hinge loss draws on recent connections between risk minimization and probability elicitation. These connections are generalized to cost-sensitive classification, in a manner that guar-antees consistency with the cost-sensitive Bayes risk, and associated Bayes decision rule. This ensures that optimal decision rules, under the new hinge loss, implement the Bayes-optimal cost-sensitive classification boundary. Minimization of the new hinge loss is shown to be a generalization of the classic SVM optimization problem, and can be solved by identical procedures. The resulting algorithm avoids the shortcomings of previous approaches to cost-sensitive SVM design, and has superior experimental performance. Copyright 2010 by the author(s)/owner(s).

关键词： Costs

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Variable margin losses for classifier design

Variable margin losses for classifier design

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24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

作者： Masnadi-Shirazi, Hamed Vasconcelos, Nuno Statistical Visual Computing Laboratory University of California San Diego San Diego CA 92039 United States

ISBN: (纸本)9781617823800

The problem of controlling the margin of a classifier is studied. A detailed analytical study is presented on how properties of the classification risk, such as its optimal link and minimum risk functions, are related to the shape of the loss, and its margin enforcing properties. It is shown that for a class of risks, denoted canonical risks, asymptotic Bayes consistency is compatible with simple analytical relationships between these functions. These enable a precise characterization of the loss for a popular class of link functions. It is shown that, when the risk is in canonical form and the link is inverse sigmoidal, the margin properties of the loss are determined by a single parameter. Novel families of Bayes consistent loss functions, of variable margin, are derived. These families are then used to design boosting style algorithms with explicit control of the classification margin. The new algorithms generalize well established approaches, such as LogitBoost. Experimental results show that the proposed variable margin losses outperform the fixed margin counterparts used by existing algorithms. Finally, it is shown that best performance can be achieved by cross-validating the margin parameter.

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