HUMAN DETECTION IN SCENES
    12.
    发明申请

    公开(公告)号:US20210110147A1

    公开(公告)日:2021-04-15

    申请号:US17128565

    申请日:2020-12-21

    Abstract: Systems and methods for human 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 humans in one or more different scenes. 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.

    Long-tail large scale face recognition by non-linear feature level domain adaptation

    公开(公告)号:US10853627B2

    公开(公告)日:2020-12-01

    申请号:US16145537

    申请日:2018-09-28

    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.

    Distance metric learning with N-pair loss

    公开(公告)号:US10565496B2

    公开(公告)日:2020-02-18

    申请号:US15385283

    申请日:2016-12-20

    Inventor: Kihyuk Sohn

    Abstract: A method includes receiving N pairs of training examples and class labels therefor. Each pair includes a respective anchor example, and a respective non-anchor example capable of being a positive or a negative training example. The method further includes extracting features of the pairs by applying a DHCNN, and calculating, for each pair based on the features, a respective similarly measure between the respective anchor and no example. The method additionally includes calculating a similarity score based on the respective similarity measure for each pair. The score represents similarities between all anchor points and positive training examples in the pairs relative to similarities between all anchor points and negative training examples in the pairs. The method further includes maximizing the similarity score for the anchor example for each pair to pull together the training examples from a same class while pushing apart the training examples from different classes.

    Video surveillance system based on larger pose face frontalization

    公开(公告)号:US10474882B2

    公开(公告)日:2019-11-12

    申请号:US15888747

    申请日:2018-02-05

    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.

    RECOGNITION IN UNLABELED VIDEOS WITH DOMAIN ADVERSARIAL LEARNING AND KNOWLEDGE DISTILLATION

    公开(公告)号:US20180268265A1

    公开(公告)日:2018-09-20

    申请号:US15889846

    申请日:2018-02-06

    Abstract: An object recognition system is provided that includes a device configured to capture a video sequence formed from unlabeled testing video frames. The system includes a processor configured to pre-train a recognition engine formed from a reference set of CNNs on a still image domain that includes labeled training still image frames. The processor adapts the recognition engine to a video domain to form an adapted recognition engine, by applying a non-reference set of CNNs to a set of domains that include the still image and video domains and a degraded image domain. The degraded image domain includes labeled synthetically degraded versions of the labeled training still image frames included in the still image domain. The video domain includes random unlabeled training video frames. The processor recognizes, using the adapted engine, a set of objects in the video sequence. A display device displays the set of recognized objects.

    Construction zone segmentation
    19.
    发明授权

    公开(公告)号:US11580334B2

    公开(公告)日:2023-02-14

    申请号:US17128612

    申请日:2020-12-21

    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.

    Privacy-preserving visual recognition via adversarial learning

    公开(公告)号:US11520923B2

    公开(公告)日:2022-12-06

    申请号:US16674425

    申请日:2019-11-05

    Abstract: A method for protecting visual private data by preventing data reconstruction from latent representations of deep networks is presented. The method includes obtaining latent features from an input image and learning, via an adversarial reconstruction learning framework, privacy-preserving feature representations to maintain utility performance and prevent the data reconstruction by simulating a black-box model inversion attack by training a decoder to reconstruct the input image from the latent features and training an encoder to maximize a reconstruction error to prevent the decoder from inverting the latent features while minimizing the task loss.

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