Xerox Corporation

Naver Corporation

Conduent Business Services, LLC

Naver Labs Corporation

Boris Chidlovskii

Gabriela Csurka

Stéphane Clinchant

Luis Rafael Ulloa Paredes

Loic Lecerf

Andre Bergholz

Cesar Roberto De Souza

Jean-Baptiste Faddoul

Leonid Antsfeld

Hervé Déjean

Stefania Castellani

Victor Ciriza

Frederic Roulland

Antonietta Grasso

Natalie S Glance

Philippe Weinzaepfel

Thierry Jacquin

Jose Antonio Rodriguez Serrano

Eduardo Cardenas Sanchez

Vincent Leroy

Loïc M Lecerf

Hervé Dejean

Jérôme Revaud

Jérôme Fuselier

Matthijs Hovelynck

Mohammad Ghufran

Nishant Vijayakumar

Mohamed Aymen Benzarti

Fabien Baradel

Giorgio Giannone

Assem Sadek

Emilio Sansano

A method of jointly training of a transferable feature extractor network, an ordinal regressor network, and an order classifier network in an ordinal regression unsupervised domain adaption network by providing a source of labeled source images and unlabeled target images; outputting image representations from a transferable feature extractor network by performing a minimax optimization procedure on the source of labeled source images and unlabeled target images; training a domain discriminator network, using the image representations from the transferable feature extractor network, to distinguish between source images and target images; training an ordinal regressor network using a full set of source images from the transferable feature extractor network; and training an order classifier network using a full set of source images from said transferable feature extractor network.

Method of unsupervised domain adaptation in ordinal regression

A system includes: a depth module including an encoder and a decoder and configured to: receive a first image from a first time from a camera; and based on the first image, generate a depth map including depths between the camera and objects in the first image; a pose module configured to: generate a first pose of the camera based on the first image; generate a second pose of the camera for a second time based on a second image; and generate a third pose of the camera for a third time based on a third image; and a motion module configured to: determine a first motion of the camera between the second and first times based on the first and second poses; and determine a second motion of the camera between the second and third times based on the second and third poses.

Self-supervised attention learning for depth and motion estimation

A computer-implemented method of determining a position of a portable electronic device in an indoor environment includes: at a first rate, updating an absolute position of a portable electronic device within the indoor environment based on at least one of radio signal data and magnetic field data captured using the portable electronic device; at a second rate that is different than the first rate, selectively updating an estimated displacement of the portable electronic device within the indoor environment, the updating the estimated displacement comprising generating an estimated displacement, by a neural network module, based on inertial sensor data of the portable electronic device; and determining a present position of the portable electronic device within the indoor environment by updating a previous position based on at least one of (a) the estimated displacement and (b) the absolute position.

Deep smartphone sensors fusion for indoor positioning and tracking

A pose estimation training system includes: a first model configured to generate a first 6 degrees of freedom (DoF) pose of a first camera that captured a first image from a first domain; a second model configured to generate a second 6 DoF pose of a second camera that captured a second image from a second domain, where the second domain is different than the first domain; a discriminator module configured to, based on first and second outputs from the first and second encoder modules, generate a discriminator output indicative of whether the first and second images are from the same domain; and a training control module configured to, based on the discriminator output, selectively adjust at least one weight value shared by the first model and the second model.

Adversarial scene adaptation for camera pose regression

A pre-trained source encoder generates a source encoder representation for each image of a labeled set of source images from a source domain. A target encoder generates a target encoder representation for each image of an unlabeled set of target images from a target domain. A generative adversarial network outputs a first prediction indicating whether each of the source encoder representations and each of the target encoder representations originate from the source domain or the target domain. The generative adversarial network outputs a second prediction of the latent code for each of the source encoder representations and each of the target encoder representations. The target encoder and the generative adversarial network are trained by repeatedly updating parameters of the target encoder and the generative adversarial network until one or more predetermined stopping conditions occur.

Latent code for unsupervised domain adaptation

A method of training a predictor to predict a location of a computing device in an indoor environment incudes: receiving training data including strength of signals received from wireless access points at positions of an indoor environment, where the training data includes: a subset of labeled data including signal strength values and location labels; and a subset of unlabeled data including signal strength values and not including labels indicative of locations; training a variational autoencoder to minimize a reconstruction loss of the signal strength values of the training data, where the variational autoencoder includes encoder neural networks and decoder neural networks; and training a classification neural network to minimize a prediction loss on the labeled data, where the classification neural network generates a predicted location based on the latent variable, and where the encoder neural networks and the classification neural network form the predictor.

Semi-supervised variational autoencoder for indoor localization

A computer-implemented method for generating a semantically segmented image and a depth completion image using a convolutional neural network (CNN) from an input visible image and/or an input depth image. A central component of the CNN for semantic segmentation and depth completion is a common representation that allows both tasks to be performed when given any of these combinations of input images (i) both an input visible image and an input depth image, (ii) only an input visible image, or (iii) only an input depth image.

Method and apparatus for semantic segmentation and depth completion using a convolutional neural network

A method and system are disclosed for dynamically estimating an origin-destination matrix. An origin-destination matrix is initialized with a set of origin stops and destination stops. Validation sequences are acquired for a set of travelers on a transportation system which include a plurality of the origin stops and respective timestamps. Corresponding destination stops may be known or inferred. For each validation sequence, a set of subsequences is generated, each including a respective one of the origin stops and the associated timestamp. Subsequences which, in combination, constitute a valid transfer trip are identified. For a combination of subsequences constituting a valid transfer trip, the method includes determining whether the valid transfer trip is a multi-goal trip for which there is least a first destination stop with an intermediate goal and a second destination stop with a final goal. The origin-destination matrix is updated, based on the determination.

System and method for estimating a dynamic origin-destination matrix

A classification method includes receiving a collection of samples, each sample comprising a multidimensional feature representation. A class label prediction for each sample in the collection is generated with one or more pretrained classifiers. For at least one iteration, each multidimensional feature representation is augmented with a respective class label prediction to form an augmented representation, a set of corrupted samples is generated from the augmented representations, and a transformation that minimizes a reconstruction error for the set of corrupted samples is learned. An adapted class label prediction for at least one of the samples in the collection is generated using the learned transformation and information is output, based on the adapted class label prediction. The method is useful in predicting labels for target samples where there is no access to source domain samples that are used to train the classifier and no access to target domain training data.

Transductive adaptation of classifiers without source data

A domain-adapted classification system and method are disclosed. The method includes mapping an input set of representations to generate an output set of representations, using a learned transformation. The input set of representations includes a set of target samples from a target domain. The input set also includes, for each of a plurality of source domains, a class representation for each of a plurality of classes. The class representations are representative of a respective set of source samples from the respective source domain labeled with a respective class. The output set of representations includes an adapted representation of each of the target samples and an adapted class representation for each of the classes for each of the source domains. A class label is predicted for at least one of the target samples based on the output set of representations and information based on the predicted class label is output.

Adapted domain specific class means classifier

A method and apparatus for classifying an image. In one example, the method may include receiving one or more images associated with a source domain and one or more images associated with a target domain, identifying one or more source domain features based on the one or more images associated with the source domain, identifying one or more target domain features based on the one or more images associated with the target domain, training a conditional maximum mean discrepancy (CMMD) engine based on a difference between the one or more source domain features and the one or more target domain features, applying the CMMD engine to the one or more images associated with the target domain to generate one or more labels for each unlabeled target image of the one or more images associated with the target domain and classifying each one of the one or more images in the target domain using the one or more labels.

Conditional adaptation network for image classification

Methods and systems for interpretable user behavior profiling in off-street parking applications. To render user profiles easy to interpret by decision makers, the semi-automatic discovery and tagging of user profiles can be implemented. Transaction data from one or more (and geographically close) off-street parking installations can be implemented. An analysis of spatio-temporal behavioral patterns can be implemented based on representation of any parking episode by a set of heterogeneous features, the use of clustering methods for automatic pattern discovery, an assessment of obtained clusters, semi-automatic identification/tagging of space-temporal patterns, and a user-friendly interpretation of obtained patterns.

Methods and systems for interpretable user behavior profiling in off-street parking

A method and system are disclosed for re-ranking trips from a journey planner using real traveler preferences. A trip request is received that includes an origin, a destination and a departure time. An associated journey planner retrieves a list of candidate trips that correspond to the request. A ranking function, ascertained from actual trips that match the trip request and from which are determined real-world traveler preferences, is applied to the list of candidate trips output by the journey planner, thereby re-ranking the list of candidate trips to reflect real-world traveler's experiences.

Trip reranking for a journey planner

A method and system are disclosed for generating a list of trips on an associated transportation network, the list ranked in accordance with time-dependent modeling of passenger preferences. User preferences of choosing a specific public transportation service or change point are modeled by a set of latent variables. Any actual trip on the network is converted into a set of pairwise preferences implicitly made by the passenger during the trip. Sequences of services matrices and change points matrices from the retrieved set of trips and non-negative factorization of the services and change points matrices is performed to smooth the matrices. The set of pairwise preferences are used to learn a ranking function and the output of a journey planner is re-ranked using the ranking function.

Smoothed dynamic modeling of user traveling preferences in a public transportation system

A machine learning method operates on training instances from a plurality of domains including one or more source domains and a target domain. Each training instance is represented by values for a set of features. Domain adaptation is performed using stacked marginalized denoising autoencoding (mSDA) operating on the training instances to generate a stack of domain adaptation transform layers. Each iteration of the domain adaptation includes corrupting the training instances in accord with feature corruption probabilities that are non-uniform over at least one of the set of features and the domains. A classifier is learned on the training instances transformed using the stack of domain adaptation transform layers. Thereafter, a label prediction is generated for an input instance from the target domain represented by values for the set of features by applying the classifier to the input instance transformed using the stack of domain adaptation transform domains.

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Meylan, France

Boris Chidlovskii