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公开(公告)号：US20210150281A1
公开(公告)日：2021-05-20
申请号：US17094139
申请日：2020-11-10
Applicant: NEC Laboratories America, Inc.
Inventor： Yi-Hsuan Tsai , Samuel Schulter , Manmohan Chandraker , Sujoy Paul
Abstract: Systems and methods for adapting semantic segmentation across domains is provided. The method includes inputting a source image into a segmentation network, and inputting a target image into the segmentation network. The method further includes identifying category wise features for the source image and the target image using category wise pooling, and discriminating between the category wise features for the source image and the target image. The method further includes training the segmentation network with a pixel-wise cross-entropy loss on the source image, and a weak image classification loss and an adversarial loss on the target image, and outputting a semantically segmented target image.
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公开(公告)号：US20210142046A1
公开(公告)日：2021-05-13
申请号：US17091066
申请日：2020-11-06
Applicant: NEC Laboratories America, Inc.
Inventor： Xiang Yu , Manmohan Chandraker , Kihyuk Sohn , Aruni RoyChowdhury
Abstract: A computer-implemented method for implementing face recognition includes obtaining a face recognition model trained on labeled face data, separating, using a mixture of probability distributions, a plurality of unlabeled faces corresponding to unlabeled face data into a set of one or more overlapping unlabeled faces that include overlapping identities to those in the labeled face data and a set of one or more disjoint unlabeled faces that include disjoint identities to those in the labeled face data, clustering the one or more disjoint unlabeled faces using a graph convolutional network to generate one or more cluster assignments, generating a clustering uncertainty associated with the one or more cluster assignments, and retraining the face recognition model on the labeled face data and the unlabeled face data to improve face recognition performance by incorporating the clustering uncertainty.
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公开(公告)号：US20210110209A1
公开(公告)日：2021-04-15
申请号：US17128612
申请日：2020-12-21
Applicant: NEC Laboratories America, Inc.
Inventor： Yi-Hsuan Tsai , Kihyuk Sohn , Buyu Liu , Manmohan Chandraker , Jong-Chyi Su
Abstract: Systems and methods for construction zone segmentation are provided. The system aligns image level features between a source domain and a target domain based on an adversarial learning process while training a domain discriminator. The target domain includes construction zones scenes having various objects. The system selects, using the domain discriminator, unlabeled samples from the target domain that are far away from existing annotated samples from the target domain. The system selects, based on a prediction score of each of the unlabeled samples, samples with lower prediction scores. The system annotates the samples with the lower prediction scores.
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公开(公告)号：US20210110178A1
公开(公告)日：2021-04-15
申请号：US17128492
申请日：2020-12-21
Applicant: NEC Laboratories America, Inc.
Inventor： Yi-Hsuan Tsai , Kihyuk Sohn , Buyu Liu , Manmohan Chandraker , Jong-Chyi Su
IPC: G06K9/00 , G06K9/62 , B60W30/095 , B60W30/09 , B60W10/18 , B60W10/20 , G08G1/16 , B60W50/00 , G06N3/08 , G06N3/04
Abstract: Systems and methods for obstacle detection are provided. The system aligns image level features between a source domain and a target domain based on an adversarial learning process while training a domain discriminator. The target domain includes one or more road scenes having obstacles. The system selects, using the domain discriminator, unlabeled samples from the target domain that are far away from existing annotated samples from the target domain. The system selects, based on a prediction score of each of the unlabeled samples, samples with lower prediction scores. The system annotates the samples with the lower prediction scores.
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公开(公告)号：US20200151457A1
公开(公告)日：2020-05-14
申请号：US16673156
申请日：2019-11-04
Applicant: NEC Laboratories America, Inc.
Inventor： Gaurav Sharma , Manmohan Chandraker
Abstract: A computer-implemented method is provided for domain adaptation between a source domain and a target domain. The method includes applying, by a hardware processor, an attention network to features extracted from images included in the source and target domains to provide attended features relating to a given task to be domain adapted between the source and target domains. The method further includes applying, by the hardware processor, a deformation network to at least some of the attended features to align the attended features between the source and target domains using warping to provide attended and warped features. The method also includes training, by the hardware processor, a target domain classifier using the images from the source domain. The method additionally includes classifying, by the hardware processor using the trained target domain classifier, at least one image from the target domain.
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Patent Agency Ranking
公开(公告)号：US20200094824A1
公开(公告)日：2020-03-26
申请号：US16696087
申请日：2019-11-26
Applicant: NEC Laboratories America, Inc.
Inventor： Samuel Schulter , Nataniel Ruiz , Manmohan Chandraker
IPC: B60W30/095 , G06N3/08 , G06K9/00 , G06F17/50
Abstract: A method is provided for danger prediction. The method includes generating fully-annotated simulated training data for a machine learning model responsive to receiving a set of computer-selected simulator-adjusting parameters. The method further includes training the machine learning model using reinforcement learning on the fully-annotated simulated training data. The method also includes measuring an accuracy of the trained machine learning model relative to learning a discriminative function for a given task. The discriminative function predicts a given label for a given image from the fully-annotated simulated training data. The method additionally includes adjusting the computer-selected simulator-adjusting parameters and repeating said training and measuring steps responsive to the accuracy being below a threshold accuracy. The method further includes predicting a dangerous condition relative to a motor vehicle and providing a warning to an entity regarding the dangerous condition by applying the trained machine learning model to actual unlabeled data for the vehicle.
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公开(公告)号：US20200065975A1
公开(公告)日：2020-02-27
申请号：US16515713
申请日：2019-07-18
Applicant: NEC Laboratories America, Inc.
Inventor： Gaurav Sharma , Manmohan Chandraker , Jinwoo Choi
Abstract: A method is provided for drone-video-based action recognition. The method learns a transformation for each of target video clips taken from a set of target videos, responsive to original features extracted from the target video clips. The transformation corrects differences between a target drone domain corresponding to the target video clips and a source non-drone domain corresponding to source video clips taken from a set of source videos. The method adapts the target to the source domain by applying the transformation to the original features to obtain transformed features for the target video clips. The method converts the original and transformed features of same ones of the target video clips into a single classification feature for each of the target videos. The method classifies a human action in a new target video relative to the set of source videos using the single classification feature for each of the target videos.
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108.发明申请公开(公告)号：US20200058156A1
公开(公告)日：2020-02-20
申请号：US16526306
申请日：2019-07-30
Applicant: NEC Laboratories America, Inc.
Inventor： Quoc-Huy Tran , Mohammed E. Fathy Salem , Muhammad Zeeshan Zia , Paul Vernaza , Manmohan Chandraker
Abstract: A method for estimating dense 3D geometric correspondences between two input point clouds by employing a 3D convolutional neural network (CNN) architecture is presented. The method includes, during a training phase, transforming the two input point clouds into truncated distance function voxel grid representations, feeding the truncated distance function voxel grid representations into individual feature extraction layers with tied weights, extracting low-level features from a first feature extraction layer, extracting high-level features from a second feature extraction layer, normalizing the extracted low-level features and high-level features, and applying deep supervision of multiple contrastive losses and multiple hard negative mining modules at the first and second feature extraction layers. The method further includes, during a testing phase, employing the high-level features capturing high-level semantic information to obtain coarse matching locations, and refining the coarse matching locations with the low-level features to capture low-level geometric information for estimating precise matching locations.
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公开(公告)号：US20190354807A1
公开(公告)日：2019-11-21
申请号：US16400376
申请日：2019-05-01
Applicant: NEC Laboratories America, Inc.
Inventor： Yi-Hsuan Tsai , Samuel Schulter , Kihyuk Sohn , Manmohan Chandraker
Abstract: Systems and methods for domain adaptation for structured output via disentangled representations are provided. The system receives a ground truth of a source domain. The ground truth is used in a task loss function for a first convolutional neural network that predicts at least one output based on inputs from the source domain and a target domain. The system clusters the ground truth of the source domain into a predetermined number of clusters, and predicts, via a second convolutional neural network, a structure of label patches. The structure includes an assignment of each of the at least one output of the first convolutional neural network to the predetermined number of clusters. A cluster loss is computed for the predicted structure of label patches, and an adversarial loss function is applied to the predicted structure of label patches to align the source domain and the target domain on a structural level.
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公开(公告)号：US10474929B2
公开(公告)日：2019-11-12
申请号：US15906710
申请日：2018-02-27
Applicant: NEC Laboratories America, Inc.
Inventor： Wongun Choi , Samuel Schulter , Kihyuk Sohn , Manmohan Chandraker
Abstract: A system is provided for unsupervised cross-domain image generation relative to a first and second image domain that each include real images. A first generator generates synthetic images similar to real images in the second domain while including a semantic content of real images in the first domain. A second generator generates synthetic images similar to real images in the first domain while including a semantic content of real images in the second domain. A first discriminator discriminates real images in the first domain against synthetic images generated by the second generator. A second discriminator discriminates real images in the second domain against synthetic images generated by the first generator. The discriminators and generators are deep neural networks and respectively form a generative network and a discriminative network in a cyclic GAN framework configured to increase an error rate of the discriminative network to improve synthetic image quality.
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