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    HUMAN DETECTION IN SCENES
    162.
    发明申请
    HUMAN DETECTION IN SCENES 有权

    公开(公告)号：US20210110147A1

    公开(公告)日：2021-04-15

    申请号：US17128565

    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 ,  G06K9/32 ,  G06N3/08 ,  G06N3/04

    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.

    SPATIO-TEMPORAL INTERACTIONS FOR VIDEO UNDERSTANDING
    163.
    发明申请
    SPATIO-TEMPORAL INTERACTIONS FOR VIDEO UNDERSTANDING 有权

    公开(公告)号：US20210081672A1

    公开(公告)日：2021-03-18

    申请号：US17016240

    申请日：2020-09-09

    Applicant: NEC Laboratories America, Inc.

    Inventor： Asim KADAV ,  Farley LAI ,  Chhavi SHARMA

    IPC: G06K9/00 ,  G06K9/62 ,  G06N3/08 ,  G06N3/04

    Abstract: Aspects of the present disclosure describe systems, methods and structures including a network that recognizes action(s) from learned relationship(s) between various objects in video(s). Interaction(s) of objects over space and time is learned from a series of frames of the video. Object-like representations are learned directly from various 2D CNN layers by capturing the 2D CNN channels, resizing them to an appropriate dimension and then providing them to a transformer network that learns higher-order relationship(s) between them. To effectively learn object-like representations, we 1) combine channels from a first and last convolutional layer in the 2D CNN, and 2) optionally cluster the channel (feature map) representations so that channels representing the same object type are grouped together.

    ELECTRONIC CONTROL UNIT TESTING OPTIMIZATION
    165.
    发明申请
    ELECTRONIC CONTROL UNIT TESTING OPTIMIZATION 有权

    公开(公告)号：US20210078589A1

    公开(公告)日：2021-03-18

    申请号：US17015239

    申请日：2020-09-09

    Applicant: NEC Laboratories America, Inc.

    Inventor： Jianwu Xu ,  Haifeng Chen

    IPC: B60W50/02 ,  G01R31/317 ,  B60R16/023 ,  B60W50/06 ,  G07C5/08 ,  B60W60/00 ,  G06N3/08

    Abstract: A computer-implemented method for implementing electronic control unit (ECU) testing optimization includes capturing, within a neural network model, input-output relationships of a plurality of ECUs operatively coupled to a controller area network (CAN) bus within a CAN bus framework, including generating the neural network model by pruning a fully-connected neural network model based on comparisons of maximum values of neuron weights to a threshold, reducing signal connections of a plurality of collected input signals and a plurality of collected output signals based on connection weight importance, ranking importance of the plurality of collected input signals based on the neural network model, generating, based on the ranking, a test case execution sequence for testing a system including the plurality of ECUs to identify flaws in the system, and initiating the test case execution sequence for testing the system.

    ADVERSARIAL COOPERATIVE IMITATION LEARNING FOR DYNAMIC TREATMENT
    166.
    发明申请
    ADVERSARIAL COOPERATIVE IMITATION LEARNING FOR DYNAMIC TREATMENT 有权

    公开(公告)号：US20210065009A1

    公开(公告)日：2021-03-04

    申请号：US16998228

    申请日：2020-08-20

    Applicant: NEC Laboratories America, Inc.

    Inventor： Wenchao Yu ,  Haifeng Chen

    IPC: G06N3/08 ,  G06N3/04 ,  G06N5/04 ,  G06N20/20 ,  G16H50/30

    Abstract: Methods and systems for responding to changing conditions include training a model, using a processor, using trajectories that resulted in a positive outcome and trajectories that resulted in a negative outcome. Training is performed using an adversarial discriminator to train the model to generate trajectories that are similar to historical trajectories that resulted in a positive outcome, and using a cooperative discriminator to train the model to generate trajectories that are dissimilar to historical trajectories that resulted in a negative outcome. A dynamic response regime is generated using the trained model and environment information. A response to changing environment conditions is performed in accordance with the dynamic response regime.

    TENSORIZED LSTM WITH ADAPTIVE SHARED MEMORY FOR LEARNING TRENDS IN MULTIVARIATE TIME SERIES
    167.
    发明申请
    TENSORIZED LSTM WITH ADAPTIVE SHARED MEMORY FOR LEARNING TRENDS IN MULTIVARIATE TIME SERIES 有权

    公开(公告)号：US20210064998A1

    公开(公告)日：2021-03-04

    申请号：US16987789

    申请日：2020-08-07

    Applicant: NEC Laboratories America, Inc.

    Inventor： Wei Cheng ,  Haifeng Chen ,  Jingchao Ni ,  Dongkuan Xu ,  Wenchao Yu

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A method for executing a multi-task deep learning model for learning trends in multivariate time series is presented. The method includes collecting multi-variate time series data from a plurality of sensors, jointly learning both local and global contextual features for predicting a trend of the multivariate time series by employing a tensorized long short-term memory (LSTM) with adaptive shared memory (TLASM) to learn historical dependency of historical trends, and employing a multi-task one-dimensional convolutional neural network (1dCNN) to extract salient features from local raw time series data to model a short-term dependency between local time series data and subsequent trends.

    Anomaly detection in streaming networks
    169.
    发明授权
    Anomaly detection in streaming networks 有权

    公开(公告)号：US10929722B2

    公开(公告)日：2021-02-23

    申请号：US15981087

    申请日：2018-05-16

    Applicant: NEC Laboratories America, Inc.

    Inventor： Wei Cheng ,  Haifeng Chen ,  Kenji Yoshihira

    IPC: G06K9/62 ,  G06K9/00 ,  G06F16/901

    Abstract: A computer-implemented method, system, and computer program product are provided for anomaly detection system in streaming networks. The method includes receiving, by a processor, a plurality of vertices and edges from a streaming graph. The method also includes generating, by the processor, graph codes for the plurality of vertices and edges. The method additionally includes determining, by the processor, edge codes in real-time responsive to the graph codes. The method further includes identifying, by the processor, an anomaly based on a distance between edge codes and all current cluster centers. The method also includes controlling an operation of a processor-based machine to change a state of the processor-based machine, responsive to the anomaly.

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