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公开(公告)号：US20240037188A1
公开(公告)日：2024-02-01
申请号：US18484839
申请日：2023-10-11
Applicant: NEC Laboratories America, Inc.
Inventor： Yi-Hsuan Tsai , Xiang Yu , Bingbing Zhuang , Manmohan Chandraker , Donghyun Kim
IPC: G06F18/213 , G06N3/08 , G06V10/75 , G06F18/22 , G06F18/214
CPC classification number: G06F18/213 , G06N3/08 , G06V10/751 , G06F18/22 , G06F18/2155
Abstract: Video methods and systems include extracting features of a first modality and a second modality from a labeled first training dataset in a first domain and an unlabeled second training dataset in a second domain. A video analysis model is trained using contrastive learning on the extracted features, including optimization of a loss function that includes a cross-domain regularization part and a cross-modality regularization part.
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公开(公告)号：US20230196122A1
公开(公告)日：2023-06-22
申请号：US17899913
申请日：2022-08-31
Applicant: NEC Laboratories America, Inc.
Inventor： Yumin Suh , Samuel Schulter , Xiang Yu , Masoud Faraki , Manmohan Chandraker , Dripta Raychaudhuri
IPC: G06N3/0985
CPC classification number: G06N3/0985
Abstract: Systems and methods for generating a hypernetwork configured to be trained for a plurality of tasks; receiving a task preference vector identifying a hierarchical priority for the plurality of tasks, and a resource constraint as a tuple; finding tree sub-structures and the corresponding modulation of features for every tuple within an N-stream anchor network; optimizing a branching regularized loss function to train an edge hypernet; and training a weight hypernet, keeping the anchor net and the edge hypernet fixed.
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公开(公告)号：US20220108226A1
公开(公告)日：2022-04-07
申请号：US17491663
申请日：2021-10-01
Applicant: NEC Laboratories America, Inc.
Inventor： Xiang Yu , Yi-Hsuan Tsai , Francesco Pittaluga , Masoud Faraki , Manmohan Chandraker , Yuqing Zhu
Abstract: A method for employing a general label space voting-based differentially private federated learning (DPFL) framework is presented. The method includes labeling a first subset of unlabeled data from a first global server, to generate first pseudo-labeled data, by employing a first voting-based DPFL computation where each agent trains a local agent model by using private local data associated with the agent, labeling a second subset of unlabeled data from a second global server, to generate second pseudo-labeled data, by employing a second voting-based DPFL computation where each agent maintains a data-independent feature extractor, and training a global model by using the first and second pseudo-labeled data to provide provable differential privacy (DP) guarantees for both instance-level and agent-level privacy regimes.
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公开(公告)号：US11250282B2
公开(公告)日：2022-02-15
申请号：US17091140
申请日：2020-11-06
Applicant: NEC Laboratories America, Inc.
Inventor： Xiang Yu , Buyu Liu , Manmohan Chandraker , Junru Wu
Abstract: A computer-implemented method for implementing face spoofing detection using a physical-cue-guided multi-source multi-channel framework includes receiving a set of data including face recognition data, liveness data and material data associated with at least one face image, obtaining a shared feature from the set of data using a backbone neural network structure, performing, based on the shared feature, a pretext task corresponding to face recognition, a first proxy task corresponding to depth estimation, a liveness detection task, and a second proxy task corresponding to material prediction, and aggregating outputs of the pretext task, the first proxy task, the liveness detection task and the second proxy task using an attention mechanism to boost face spoofing detection performance.
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公开(公告)号：US20210150240A1
公开(公告)日：2021-05-20
申请号：US17091140
申请日：2020-11-06
Applicant: NEC Laboratories America, Inc.
Inventor： Xiang Yu , Buyu Liu , Manmohan Chandraker , Junru Wu
Abstract: A computer-implemented method for implementing face spoofing detection using a physical-cue-guided multi-source multi-channel framework includes receiving a set of data including face recognition data, liveness data and material data associated with at least one face image, obtaining a shared feature from the set of data using a backbone neural network structure, performing, based on the shared feature, a pretext task corresponding to face recognition, a first proxy task corresponding to depth estimation, a liveness detection task, and a second proxy task corresponding to material prediction, and aggregating outputs of the pretext task, the first proxy task, the liveness detection task and the second proxy task using an attention mechanism to boost face spoofing detection performance.
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Patent Agency Ranking
公开(公告)号：US10853627B2
公开(公告)日：2020-12-01
申请号：US16145537
申请日：2018-09-28
Applicant: NEC Laboratories America, Inc.
Inventor： Xiang Yu , Xi Yin , Kihyuk Sohn , Manmohan Chandraker
Abstract: A computer-implemented method, system, and computer program product are provided for facial recognition. The method includes receiving, by a processor device, a plurality of images. The method also includes extracting, by the processor device with a feature extractor utilizing a convolutional neural network (CNN) with an enlarged intra-class variance of long-tail classes, feature vectors for each of the plurality of images. The method additionally includes generating, by the processor device with a feature generator, discriminative feature vectors for each of the feature vectors. The method further includes classifying, by the processor device utilizing a fully connected classifier, an identity from the discriminative feature vector. The method also includes control an operation of a processor-based machine to react in accordance with the identity.
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公开(公告)号：US10474882B2
公开(公告)日：2019-11-12
申请号：US15888747
申请日：2018-02-05
Applicant: NEC Laboratories America, Inc.
Inventor： Xiang Yu , Kihyuk Sohn , Manmohan Chandraker
Abstract: A video surveillance system is provided. The system includes a device configured to capture an input image of a subject located in an area. The system further includes a processor. The processor estimates, using a three-dimensional Morphable Model (3DMM) conditioned Generative Adversarial Network, 3DMM coefficients for the subject of the input image. The subject varies from an ideal front pose. The processor produces, using an image generator, a synthetic frontal face image of the subject of the input image based on the input image and coefficients. An area spanning the frontal face of the subject is made larger in the synthetic than in the input image. The processor provides, using a discriminator, a decision of whether the subject of the synthetic image is an actual person. The processor provides, using a face recognition engine, an identity of the subject in the input image based on the synthetic and input images.
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公开(公告)号：US10331974B2
公开(公告)日：2019-06-25
申请号：US15709897
申请日：2017-09-20
Applicant: NEC Laboratories America, Inc.
Inventor： Muhammad Zeeshan Zia , Quoc-Huy Tran , Xiang Yu , Manmohan Chandraker , Chi Li
IPC: G06K9/00 , G06K9/62 , G06F17/50 , G06N3/02 , G06T11/60 , G06T15/40 , G05D1/02 , G08G1/16 , G06T7/73 , G06N3/08 , G06T15/10 , B60W30/00 , G08G1/0962 , G06T7/55 , G06K9/46
Abstract: An action recognition system and method are provided. The action recognition system includes an image capture device configured to capture an actual image depicting an object. The action recognition system includes a processor configured to render, based on a set of 3D CAD models, synthetic images with corresponding intermediate shape concept labels. The processor is configured to form a multi-layer CNN which jointly models multiple intermediate shape concepts, based on the rendered synthetic images. The processor is configured to perform an intra-class appearance variation-aware and occlusion-aware 3D object parsing on the actual image by applying the CNN thereto to generate an image pair including a 2D and 3D geometric structure of the object. The processor is configured to control a device to perform a response action in response to an identification of an action performed by the object, wherein the identification of the action is based on the image pair.
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9.发明申请公开(公告)号：US20190065853A1
公开(公告)日：2019-02-28
申请号：US16051980
申请日：2018-08-01
Applicant: NEC Laboratories America, Inc.
Inventor： Kihyuk Sohn , Luan Tran , Xiang Yu , Manmohan Chandraker
Abstract: Systems and methods for vehicle surveillance include a camera for capturing target images of vehicles. An object recognition system is in communication with the camera, the object recognition system including a processor for executing a synthesizer module for generating a plurality of viewpoints of a vehicle depicted in a source image, and a domain adaptation module for performing domain adaptation between the viewpoints of the vehicle and the target images to classifying vehicles of the target images regardless of the viewpoint represented in the target images. A display is in communication with the object recognition system for displaying each of the target images with labels corresponding to the vehicles of the target images.
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公开(公告)号：US20180268292A1
公开(公告)日：2018-09-20
申请号：US15908870
申请日：2018-03-01
Applicant: NEC Laboratories America, Inc.
Inventor： Wongun Choi , Manmohan Chandraker , Guobin Chen , Xiang Yu
CPC classification number: G06N3/08 , G06K9/00684 , G06K9/4628 , G06K9/6217 , G06K9/6264 , G06K9/6274 , G06K9/66 , G06N3/0454 , G06N3/0481 , G06N3/084
Abstract: A computer-implemented method executed by at least one processor for training fast models for real-time object detection with knowledge transfer is presented. The method includes employing a Faster Region-based Convolutional Neural Network (R-CNN) as an objection detection framework for performing the real-time object detection, inputting a plurality of images into the Faster R-CNN, and training the Faster R-CNN by learning a student model from a teacher model by employing a weighted cross-entropy loss layer for classification accounting for an imbalance between background classes and object classes, employing a boundary loss layer to enable transfer of knowledge of bounding box regression from the teacher model to the student model, and employing a confidence-weighted binary activation loss layer to train intermediate layers of the student model to achieve similar distribution of neurons as achieved by the teacher model.
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