332 research outputs found
Generalized conditional gradient: analysis of convergence and applications
The objectives of this technical report is to provide additional results on
the generalized conditional gradient methods introduced by Bredies et al.
[BLM05]. Indeed , when the objective function is smooth, we provide a novel
certificate of optimality and we show that the algorithm has a linear
convergence rate. Applications of this algorithm are also discussed
Joint Distribution Optimal Transportation for Domain Adaptation
This paper deals with the unsupervised domain adaptation problem, where one
wants to estimate a prediction function in a given target domain without
any labeled sample by exploiting the knowledge available from a source domain
where labels are known. Our work makes the following assumption: there exists a
non-linear transformation between the joint feature/label space distributions
of the two domain and . We propose a solution of
this problem with optimal transport, that allows to recover an estimated target
by optimizing simultaneously the optimal coupling
and . We show that our method corresponds to the minimization of a bound on
the target error, and provide an efficient algorithmic solution, for which
convergence is proved. The versatility of our approach, both in terms of class
of hypothesis or loss functions is demonstrated with real world classification
and regression problems, for which we reach or surpass state-of-the-art
results.Comment: Accepted for publication at NIPS 201
Optimal Transport for Domain Adaptation
Domain adaptation from one data space (or domain) to another is one of the
most challenging tasks of modern data analytics. If the adaptation is done
correctly, models built on a specific data space become more robust when
confronted to data depicting the same semantic concepts (the classes), but
observed by another observation system with its own specificities. Among the
many strategies proposed to adapt a domain to another, finding a common
representation has shown excellent properties: by finding a common
representation for both domains, a single classifier can be effective in both
and use labelled samples from the source domain to predict the unlabelled
samples of the target domain. In this paper, we propose a regularized
unsupervised optimal transportation model to perform the alignment of the
representations in the source and target domains. We learn a transportation
plan matching both PDFs, which constrains labelled samples in the source domain
to remain close during transport. This way, we exploit at the same time the few
labeled information in the source and the unlabelled distributions observed in
both domains. Experiments in toy and challenging real visual adaptation
examples show the interest of the method, that consistently outperforms state
of the art approaches
Data-Driven Animation of Crowds
International audienceIn this paper we propose an original method to animate a crowd of virtual beings in a virtual environment. Instead of relying on models to describe the motions of people along time, we suggest to use {\em a priori} knowledge on the dynamic of the crowd acquired from videos of real crowd situations. In our method this information is expressed as a time-varying motion field which accounts for a continuous flow of people along time. This motion descriptor is obtained through optical flow estimation with a specific second order regularization. Obtained motion fields are then used in a classical fixed step size integration scheme that allows to animate a virtual crowd in real-time. The power of our technique is demonstrated through various examples and possible follow-ups to this work are also described
Data-Driven Animation of Crowds
International audienceIn this paper we propose an original method to animate a crowd of virtual beings in a virtual environment. Instead of relying on models to describe the motions of people along time, we suggest to use {\em a priori} knowledge on the dynamic of the crowd acquired from videos of real crowd situations. In our method this information is expressed as a time-varying motion field which accounts for a continuous flow of people along time. This motion descriptor is obtained through optical flow estimation with a specific second order regularization. Obtained motion fields are then used in a classical fixed step size integration scheme that allows to animate a virtual crowd in real-time. The power of our technique is demonstrated through various examples and possible follow-ups to this work are also described
Potential of power recovery of a subsonic axial fan in windmilling operation
During the last decades, efforts to find efficient green energy solutions have been widely increased in response to environmental concerns. Among all renewable energies, this paper is focused on wind power generation. To this end, a windmilling axial fan in turbine operation is experimentally and numerically investigated. Under specific conditions, the studied fan is naturally freewheeling. Consequently, the main objective of this analysis is to determine whether or not this intrinsic windmilling behavior can be optimized for power generation. A preliminary study of the fan is dedicated to the knowledge of the fan characteristics in normal operating conditions. Then, two windmilling configurations (direct and reverse flow direction) are tested and compared on the basis of the output power. An analysis of the velocity triangle gives the opportunity to evaluate the energy recovery potential of both solutions. Of the two, the reversed configuration showed a higher level of output power than the direct one
AGORASET: a dataset for crowd video analysis
International audienceThe ability of efficient computer vision tools (detec- tion of pedestrians, tracking, ...) as well as advanced rendering techniques have enabled both the analysis of crowd phenomena and the simulation of realistic sce- narios. A recurrent problem lies in the evaluation of those methods since few common benchmark are avail- able to compare and evaluate the techniques is avail- able. This paper proposes a dataset of crowd sequences with associated ground truths (individual trajectories of each pedestrians inside the crowd, related continuous quantities of the scene such as the density and the veloc- ity field). We chosed to rely on realistic image synthesis to achieve our goal. As contributions of this paper, a typology of sequences relevant to the computer vision analysis problem is proposed, along with images of se- quences available in the database
Optimal crowd editing
International audienceSimulating realistic crowd behaviors is a challenging problem in computer graphics. Yet, several satisfying simulation models exhibiting natural pedestrians or group emerging behaviors exist. Choosing among these model generally depends on the considered crowd density or the topology of the environment. Conversely, achieving a user-desired kinematic or dynamic pattern at a given instant of the simulation reveals to be much more tedious. In this paper, a novel generic control methodology is proposed to solve this crowd editing issue. Our method relies on an adjoint formulation of the underlying optimization procedure. It is independent to a certain extent of the choice of the simulation model, and is designed to handle several forms of constraints. A variety of examples attesting the benefits of our approach are proposed, along with quantitative performance measures
Network-based correlated correspondence for unsupervised domain adaptation of hyperspectral satellite images
International audienceAdapting a model to changes in the data distribution is a relevant problem in machine learning and pattern recognition since such changes degrade the performances of classifiers trained on undistorted samples. This paper tackles the problem of domain adaptation in the context of hyperspectral satellite image analysis. We propose a new correlated correspondence algorithm based on network analysis. The algorithm finds a matching between two distributions, which preserves the geometrical and topological information of the corresponding graphs. We evaluate the performance of the algorithm on a shadow compensation problem in hyperspectral image analysis: the land use classification obtained with the compensated data is improve
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