Patents by Inventor Jérôme Revaud
Jérôme Revaud has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240144019Abstract: A training system includes: a model; and a training module configured to: construct a first pair of images of at least a first portion of a first human captured at different times; construct a second pair of images of at least a second portion of a second human captured at the same time from different points of view; input the first and second pairs of images to the model; the model configured to: generate first and second reconstructed images of the at least the first portion of the first human based on the first and second pairs, respectively, and the training module is configured to selectively adjust one or more parameters of the model based on: the first reconstructed image and the second reconstructed image.Type: ApplicationFiled: August 29, 2023Publication date: May 2, 2024Applicants: NAVER CORPORATION, NAVER LABS CORPORATIONInventors: Philippe WEINZAEPFEL, Vincent Leroy, Romain Brègier, Yohann Cabon, Thomas Lucas, Leonid Antsfield, Boris Chidlovskii, Gabriela Csurka Khedari, Jèrôme Revaud, Matthieu Armando, Fabien Baradel, Salma Galaaoui, Gregory Rogez
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Publication number: 20240135695Abstract: A method includes: performing unsupervised pre-training of a model, the model including and a decoder including: obtaining a first image and a second image under different conditions or from different viewpoints; encoding, by the encoder, the first image into a representation of the first image and the second image into a representation of the second image; transforming the representation of the first image into a transformed representation; decoding, by the decoder, the transformed representation into a reconstructed image, where the transforming of the representation of the first image and the decoding of the transformed representation is based on the representation of the first image and the representation of the second image; and adjusting one or more parameters of at least one of the encoder and the decoder based on minimizing a loss; and fine-tuning the model, initialized with a set of task specific encoder parameters, for a geometric vision task.Type: ApplicationFiled: August 4, 2023Publication date: April 25, 2024Applicants: NAVER CORPORATION, NAVER LABS CORPORATIONInventors: Romain BRÉGIER, Yohann CABON, Thomas LUCAS, Jérôme REVAUD, Philippe WEINZAEPFEL, Boris CHIDLOVSKII, Vincent LEROY, Leonid ANTSFELD, Gabriela CSURKA KHEDARI
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Publication number: 20230306718Abstract: A computer-implemented method includes: obtaining a pair of images depicting a same scene, the pair of images including a first image with a first pixel grid and a second image with a second pixel grid, the first pixel grid different than the second pixel grid; by a neural network module having a first set of parameters: generating a first feature map based on the first image; and generating a second feature map based on the second image; determining a first correlation volume based on the first and second feature maps; iteratively determining a second correlation volume based on the first correlation volume; determining a loss for the first and second feature maps based on the second correlation volume; generating a second set of the parameters based on minimizing a loss function using the loss; and updating the neural network module to include the second set of parameters.Type: ApplicationFiled: January 30, 2023Publication date: September 28, 2023Applicant: NAVER CORPORATIONInventors: Jérome REVAUD, Vincent LEROY, Philippe WEINZAEPFEL, Boris CHIDLOVSKII
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Patent number: 11715215Abstract: A speed estimation system includes: a detection module configured to determine bounding boxes of an object moving on a surface in images, respectively, captured using a camera; a solver module configured to, based on the bounding boxes, determine a homography of the surface by solving an optimization problem, where the solver module is not trained; and a speed module configured to, using the homography, determine a speed that the object is moving on the surface.Type: GrantFiled: July 1, 2021Date of Patent: August 1, 2023Assignee: NAVER CORPORATIONInventors: Jérome Revaud, Yohann Cabon
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Publication number: 20230169332Abstract: A computer-implemented method for training an artificial neural network with training data including samples and corresponding labels for performing a task includes: pre-training the artificial neural network to generate matrix representations that are invariant to a predetermined set of data augmentations applied to a sample, where the artificial neural network includes an encoder module and a projection module configured to generate the matrix representations based on ones of the samples, respectively; and after the pre-training, fine-tune training the artificial neural network using a loss function, wherein fine-tuning the artificial neural network includes adjusting, based on the labels, one or more weights of the projection module while maintaining constant weights of the encoder module, and where the loss function is based on a logit adjustment loss that is based on logits that are adjusted based on a class distribution of the training data.Type: ApplicationFiled: June 1, 2022Publication date: June 1, 2023Applicant: NAVER CORPORATIONInventors: Shyamgopal Karthik, Jérome Revaud, Boris Chidlovskii
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Publication number: 20230032420Abstract: A speed estimation system includes: a detection module configured to determine bounding boxes of an object moving on a surface in images, respectively, captured using a camera; a solver module configured to, based on the bounding boxes, determine a homography of the surface by solving an optimization problem, where the solver module is not trained; and a speed module configured to, using the homography, determine a speed that the object is moving on the surface.Type: ApplicationFiled: July 1, 2021Publication date: February 2, 2023Applicant: NAVER CORPORATIONInventors: Jérome REVAUD, Yohann CABON
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Publication number: 20230019731Abstract: A speed estimation system includes: a detection module configured to: detect an object on a surface in an image captured using a camera; and generate a bounding box around the object; a Jacobian module configured to generate a Jacobian for the object based on the bounding box; and a speed module configured to determine a speed that the object is traveling on the surface based on the Jacobian.Type: ApplicationFiled: July 1, 2021Publication date: January 19, 2023Applicant: NAVER CORPORATIONInventor: Jérome REVAUD
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Patent number: 11521072Abstract: A method of performing image retrieval includes: obtaining a query image; generating a global feature descriptor of the query image by inputting the query image into a convolutional neural network (CNN) and obtaining the global feature descriptor as an output of the CNN, where parameters of the CNN are learned during training of the CNN on a batch of training images using a listwise ranking loss function and optimizing a quantized mean average precision ranking evaluation metric; determining similarities between the query image and other images based on distances between the global feature descriptor of the query image and global feature descriptors of the other images, respectively; ranking the other images based on the similarities, respectively; and selecting a set of the other images based on the similarities between the query image and the other images.Type: GrantFiled: February 11, 2020Date of Patent: December 6, 2022Assignee: NAVER CORPORATIONInventors: Jérome Revaud, Jon Almazan, Cesar De Souza, Rafael Sampaio De Rezende
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Publication number: 20220245831Abstract: A speed estimation system includes: a detection module having a neural network configured to: receive a time series of images, the images including a surface having a local geometry; detect an object in the time series of images on the surface; determine pixel coordinates of the object in the time series of images, respectively; determine bounding boxes around the object in the time series of images, respectively; determine local mappings, which are not a function of global parameters describing the local geometry of the surface, between pixel coordinates and distance coordinates for the time series of images based on the bounding boxes around the object in the time series of images, respectively; and a speed module configured to determine a speed of the object traveling relative to the surface based on the distance coordinates determined for the time series of images.Type: ApplicationFiled: January 29, 2021Publication date: August 4, 2022Applicant: NAVER CORPORATIONInventors: Jérome REVAUD, Yohann CABON, Julien MORAT
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Publication number: 20220114444Abstract: A computer-implemented method for training a neural network to perform a data processing task includes: for each data sample of a set of labeled data samples: by a first loss function for the data processing task, computing a first loss for that data sample; and by a second loss function, automatically computing a weight value for the data sample based on the first loss, the weight value indicative of a reliability of a label of the data sample predicted by the neural network for the data sample and dictating the extent to which that data sample impacts training of the neural network; and training the neural network with the set of labelled data samples according to their respective weight value.Type: ApplicationFiled: July 23, 2021Publication date: April 14, 2022Applicant: NAVER CORPORATIONInventors: Philippe WEINZAEPFEL, Jérome REVAUD, Thibault CASTELLS
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Patent number: 11176425Abstract: A system for detecting and describing keypoints in images is described. A camera is configured to capture an image including a plurality of pixels. A fully convolutional network is configured to jointly and concurrently: generate descriptors for each of the pixels, respectively; generate reliability scores for each of the pixels, respectively; and generate repeatability scores for each of the pixels, respectively. A scoring module is configured to generate scores for the pixels, respectively, based on the reliability scores and the repeatability scores of the pixels, respectively. A keypoint list module is configured to: select X of the pixels having the X highest scores, where X is an integer greater than 1; and generate a keypoint list including: locations of the selected X pixels; and the descriptors of the selected X pixels.Type: GrantFiled: December 11, 2019Date of Patent: November 16, 2021Assignees: NAVER CORPORATION, NAVER LABS CORPORATIONInventors: Jérome Revaud, Cesar De Souza, Martin Humenberger, Philippe Weinzaepfel
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Patent number: 11055569Abstract: A method for detecting a point of interest (POI) change in a pair of inputted POI images. A first processor of the method: generates triplets of training POI images using a base of training POI images and trains a convolutional neural network (CNN) of three-stream Siamese type based on the triplets of training POI images. A second processor of the method: computes, for each image of the pair of inputted POI images, a descriptor of that image using a stream of the CNN of three-stream Siamese type, computes a similarity score based on the descriptors of the images of the pair of inputted POI images using a similarity score function, and selectively detects the POI change based on the similarity score. A third processor of the method generates the base of training POI images to include an initial set of POI images and a set of synthetic POI images.Type: GrantFiled: September 19, 2019Date of Patent: July 6, 2021Assignee: NAVER CORPORATIONInventors: Jérôme Revaud, Rafael Sampaio De Rezende
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Publication number: 20210182626Abstract: A system for detecting and describing keypoints in images is described. A camera is configured to capture an image including a plurality of pixels. A fully convolutional network is configured to jointly and concurrently: generate descriptors for each of the pixels, respectively; generate reliability scores for each of the pixels, respectively; and generate repeatability scores for each of the pixels, respectively. A scoring module is configured to generate scores for the pixels, respectively, based on the reliability scores and the repeatability scores of the pixels, respectively. A keypoint list module is configured to: select X of the pixels having the X highest scores, where X is an integer greater than 1; and generate a keypoint list including: locations of the selected X pixels; and the descriptors of the selected X pixels.Type: ApplicationFiled: December 11, 2019Publication date: June 17, 2021Applicants: Naver Corporation, Naver Labs CorporationInventors: Jérome Revaud, Cesar De Souza, Martin Humenberger, Philippe Weinzaepfel
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Patent number: 10867184Abstract: A method for training a convolutional neural network for classification of actions performed by subjects in a video is realized by (a) for each frame of the video, for each key point of the subject, generating a heat map of the key point representing a position estimation of the key point within the frame; (b) colorizing each heat map as a function of the relative time of the corresponding frame in the video; (c) for each key point, aggregating all the colorized heat maps of the key point into at least one image representing the evolution of the position estimation of the key point during the video; and training the convolutional neural network using as input the sets associated to each training video of images representing the evolution of the position estimation of each key point during the video.Type: GrantFiled: March 20, 2019Date of Patent: December 15, 2020Inventors: Vasileios Choutas, Philippe Weinzaepfel, Jérôme Revaud, Cordelia Schmid
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Publication number: 20200342328Abstract: A method of performing image retrieval includes: obtaining a query image; generating a global feature descriptor of the query image by inputting the query image into a convolutional neural network (CNN) and obtaining the global feature descriptor as an output of the CNN, where parameters of the CNN are learned during training of the CNN on a batch of training images using a listwise ranking loss function and optimizing a quantized mean average precision ranking evaluation metric; determining similarities between the query image and other images based on distances between the global feature descriptor of the query image and global feature descriptors of the other images, respectively; ranking the other images based on the similarities, respectively; and selecting a set of the other images based on the similarities between the query image and the other images.Type: ApplicationFiled: February 11, 2020Publication date: October 29, 2020Applicant: NAVER CORPORATIONInventors: Jérome REVAUD, Jon ALMAZAN, Cesar DE SOUZA, Rafael SAMPAIO DE REZENDE
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Patent number: 10678846Abstract: In a method for detecting an object in an input image, an input image vector representing the input image is generated by performing a regional maximum activations of convolutions (R-MAC) using a convolutional neural network (CNN) applied to the input image and using regions for the R-MAC defined by applying a region proposal network (RPN) to the output of the CNN applied to the input image. Likewise, a reference image vector representing a reference image depicting the object is generated by performing the R-MAC using the CNN applied to the reference image and using regions for the R MAC defined by applying the RPN to the output of the CNN applied to the reference image. A similarity metric between the input image vector and the reference image vector is computed, and the object is detected as present in the input image if the similarity metric satisfies a detection criterion.Type: GrantFiled: March 10, 2017Date of Patent: June 9, 2020Assignee: Xerox CorporationInventors: Albert Gordo Soldevila, Jon Almazan, Jerome Revaud, Diane Larlus-Larrondo
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Publication number: 20200110966Abstract: A method for detecting a point of interest (POI) change in a pair of inputted POI images. A first processor of the method: generates triplets of training POI images using a base of training POI images and trains a convolutional neural network (CNN) of three-stream Siamese type based on the triplets of training POI images. A second processor of the method: computes, for each image of the pair of inputted POI images, a descriptor of that image using a stream of the CNN of three-stream Siamese type, computes a similarity score based on the descriptors of the images of the pair of inputted POI images using a similarity score function, and selectively detects the POI change based on the similarity score. A third processor of the method generates the base of training POI images to include an initial set of POI images and a set of synthetic POI images.Type: ApplicationFiled: September 19, 2019Publication date: April 9, 2020Applicant: Naver CorporationInventors: Jérôme Revaud, Rafael Sampaio De Rezende
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Publication number: 20190303677Abstract: A method for training a convolutional neural network for classification of actions performed by subjects in a video is realized by (a) for each frame of the video, for each key point of the subject, generating a heat map of the key point representing a position estimation of the key point within the frame; (b) colorizing each heat map as a function of the relative time of the corresponding frame in the video; (c) for each key point, aggregating all the colorized heat maps of the key point into at least one image representing the evolution of the position estimation of the key point during the video; and training the convolutional neural network using as input the sets associated to each training video of images representing the evolution of the position estimation of each key point during the video.Type: ApplicationFiled: March 20, 2019Publication date: October 3, 2019Applicant: Naver CorporationInventors: Vasileios Choutas, Philippe Weinzaepfel, Jérôme Revaud, Cordelia Schmid
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Publication number: 20180260415Abstract: In a method for detecting an object in an input image, an input image vector representing the input image is generated by performing a regional maximum activations of convolutions (R-MAC) using a convolutional neural network (CNN) applied to the input image and using regions for the R-MAC defined by applying a region proposal network (RPN) to the output of the CNN applied to the input image. Likewise, a reference image vector representing a reference image depicting the object is generated by performing the R-MAC using the CNN applied to the reference image and using regions for the R MAC defined by applying the RPN to the output of the CNN applied to the reference image. A similarity metric between the input image vector and the reference image vector is computed, and the object is detected as present in the input image if the similarity metric satisfies a detection criterion.Type: ApplicationFiled: March 10, 2017Publication date: September 13, 2018Applicant: Xerox CorporationInventors: Albert Gordo Soldevila, Jon Almazan, Jerome Revaud, Diane Larlus-Larrondo
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Patent number: 9928440Abstract: A method, non-transitory computer readable medium and apparatus for detecting a window in a display are disclosed. For example, the method includes scanning pixels within each frame of a plurality of frames that is displayed via a sliding window, extracting one or more features from each pixel within the sliding window, applying a classification function to classify a subset of potential pixels comprising the each pixel within the sliding window as corner pixels based on the one or more features that match predefined features associated with the corner pixels and detecting the window based on the corner pixels and additional pixels within a boundary defined by the corner pixels.Type: GrantFiled: July 14, 2016Date of Patent: March 27, 2018Assignee: CONDUENT BUSINESS MACHINES SERVICES, LLCInventor: Jerome Revaud