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公开(公告)号：US11222238B2
公开(公告)日：2022-01-11
申请号：US17094261
申请日：2020-11-10
Applicant: NEC Laboratories America, Inc.
Inventor： Samuel Schulter , Gaurav Sharma , Yi-Hsuan Tsai , Manmohan Chandraker , Xiangyun Zhao
Abstract: Methods and systems for object detection include training dataset-specific object detectors using respective annotated datasets, each of the annotated datasets including annotations for a respective set of one or more object classes. The annotated datasets are cross-annotated using the dataset-specific object detectors. A unified object detector is trained, using the cross-annotated datasets, to detect all of the object classes of the annotated datasets. Objects are detected in an input image using the unified object detector.
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2.发明申请公开(公告)号：US20210374481A1
公开(公告)日：2021-12-02
申请号：US17317202
申请日：2021-05-11
Applicant: NEC Laboratories America, Inc.
Inventor： Gaurav Sharma , Jinwoo Choi
Abstract: A method is provided for Cross Video Temporal Difference (CVTD) learning. The method adapts a source domain video to a target domain video using a CVTD loss. The source domain video is annotated, and the target domain video is unannotated. The CVTD loss is computed by quantizing clips derived from the source and target domain videos by dividing the source domain video into source domain clips and the target domain video into target domain clips. The CVTD loss is further computed by sampling two clips from each of the source domain clips and the target domain clips to obtain four sampled clips including a first source domain clip, a second source domain clip, a first target domain clip, and a second target domain clip. The CVTD loss is computed as |(second source domain clip−first source domain clip)−(second target domain clip−first target domain clip)|.
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公开(公告)号：US20200065617A1
公开(公告)日：2020-02-27
申请号：US16515593
申请日：2019-07-18
Applicant: NEC Laboratories America, Inc.
Inventor： Gaurav Sharma , Manmohan Chandraker , Jinwoo Choi
Abstract: A method is provided for unsupervised domain adaptation for video classification. The method learns a transformation for each 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 domain corresponding to target video clips and a source 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 extracted to obtain transformed features for the plurality of 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 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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公开(公告)号：US11710346B2
公开(公告)日：2023-07-25
申请号：US17330832
申请日：2021-05-26
Applicant: NEC Laboratories America, Inc.
Inventor： Manmohan Chandraker , Ting Wang , Xiang Xu , Francesco Pittaluga , Gaurav Sharma , Yi-Hsuan Tsai , Masoud Faraki , Yuheng Chen , Yue Tian , Ming-Fang Huang , Jian Fang
IPC: G06V40/16 , G06T3/00 , G06V10/774
CPC classification number: G06V40/172 , G06T3/0006 , G06V10/774 , G06V40/171
Abstract: Methods and systems for training a neural network include generate an image of a mask. A copy of an image is generated from an original set of training data. The copy is altered to add the image of a mask to a face detected within the copy. An augmented set of training data is generated that includes the original set of training data and the altered copy. A neural network model is trained to recognize masked faces using the augmented set of training data.
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公开(公告)号：US11468680B2
公开(公告)日：2022-10-11
申请号：US16998404
申请日：2020-08-20
Applicant: NEC Laboratories America, Inc.
Inventor： Gaurav Sharma , Samuel Schulter , Jinwoo Choi
Abstract: A method for performing video domain adaptation for human action recognition is presented. The method includes using annotated source data from a source video and unannotated target data from a target video in an unsupervised domain adaptation setting, identifying and aligning discriminative clips in the source and target videos via an attention mechanism, and learning spatial-background invariant human action representations by employing a self-supervised clip order prediction loss for both the annotated source data and the unannotated target data.
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Patent Agency Ranking
公开(公告)号：US11222210B2
公开(公告)日：2022-01-11
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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公开(公告)号：US20210150275A1
公开(公告)日：2021-05-20
申请号：US17094261
申请日：2020-11-10
Applicant: NEC Laboratories America, Inc.
Inventor： Samuel Schulter , Gaurav Sharma , Yi-Hsuan Tsai , Manmohan Chandraker , Xiangyun Zhao
Abstract: Methods and systems for object detection include training dataset-specific object detectors using respective annotated datasets, each of the annotated datasets including annotations for a respective set of one or more object classes. The annotated datasets are cross-annotated using the dataset-specific object detectors. A unified object detector is trained, using the cross-annotated datasets, to detect all of the object classes of the annotated datasets. Objects are detected in an input image using the unified object detector.
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8.发明授权公开(公告)号：US11676370B2
公开(公告)日：2023-06-13
申请号：US17317202
申请日：2021-05-11
Applicant: NEC Laboratories America, Inc.
Inventor： Gaurav Sharma , Jinwoo Choi
IPC: G06V10/764 , G06N3/08 , G06N3/04 , G06V20/40 , G06V10/774 , G06F18/21 , G06F18/24 , G06F18/211 , G06F18/25 , G06F18/214
CPC classification number: G06V10/765 , G06F18/211 , G06F18/217 , G06F18/2155 , G06F18/24 , G06F18/253 , G06N3/04 , G06N3/08 , G06V10/764 , G06V10/7753 , G06V20/46 , G06V20/49
Abstract: A method is provided for Cross Video Temporal Difference (CVTD) learning. The method adapts a source domain video to a target domain video using a CVTD loss. The source domain video is annotated, and the target domain video is unannotated. The CVTD loss is computed by quantizing clips derived from the source and target domain videos by dividing the source domain video into source domain clips and the target domain video into target domain clips. The CVTD loss is further computed by sampling two clips from each of the source domain clips and the target domain clips to obtain four sampled clips including a first source domain clip, a second source domain clip, a first target domain clip, and a second target domain clip. The CVTD loss is computed as |(second source domain clip−first source domain clip)−(second target domain clip−first target domain clip)|.
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公开(公告)号：US11301716B2
公开(公告)日：2022-04-12
申请号：US16515593
申请日：2019-07-18
Applicant: NEC Laboratories America, Inc.
Inventor： Gaurav Sharma , Manmohan Chandraker , Jinwoo Choi
Abstract: A method is provided for unsupervised domain adaptation for video classification. The method learns a transformation for each 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 domain corresponding to target video clips and a source 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 extracted to obtain transformed features for the plurality of 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 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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公开(公告)号：US20210374468A1
公开(公告)日：2021-12-02
申请号：US17330832
申请日：2021-05-26
Applicant: NEC Laboratories America, Inc.
Inventor： Manmohan Chandraker , Ting Wang , Xiang Xu , Francesco Pittaluga , Gaurav Sharma , Yi-Hsuan Tsai , Masoud Faraki , Yuheng Chen , Yue Tian , Ming-Fang Huang , Jian Fang
Abstract: Methods and systems for training a neural network include generate an image of a mask. A copy of an image is generated from an original set of training data. The copy is altered to add the image of a mask to a face detected within the copy. An augmented set of training data is generated that includes the original set of training data and the altered copy. A neural network model is trained to recognize masked faces using the augmented set of training data.
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