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公开(公告)号：US20240161902A1
公开(公告)日：2024-05-16
申请号：US18505761
申请日：2023-11-09
Applicant: NEC Laboratories America, Inc.
Inventor： Deep Patel , Alexandru Niculescu-Mizil , Iain Melvin , Seonghyeon Moon
CPC classification number: G16H20/30 , G06T7/292 , G16H50/20 , G06T2207/10016 , G06T2207/30196
Abstract: Methods and systems for tracking movement include performing person detection in frames from multiple video streams to identify detection images. Visual and location information from the detection images are combined to generate scores for pairs of detection images across the multiple video streams and across frames of respective video streams. A pairwise detection graph is generated using the detection images as nodes and the scores as weighted edges. A current view of the multiple video streams is changed to a next view of the multiple video streams, responsive to a determination that a score between consecutive frames of the view is below a threshold value and that a score between coincident frames of the current view and the next view is above the threshold value.
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公开(公告)号：US10296796B2
公开(公告)日：2019-05-21
申请号：US15478886
申请日：2017-04-04
Applicant: NEC Laboratories America, Inc.
Inventor： Eric Cosatto , Iain Melvin , Hans Peter Graf
Abstract: A video device for predicting driving situations while a person drives a car is presented. The video device includes multi-modal sensors and knowledge data for extracting feature maps, a deep neural network trained with training data to recognize real-time traffic scenes (TSs) from a viewpoint of the car, and a user interface (UI) for displaying the real-time TSs. The real-time TSs are compared to predetermined TSs to predict the driving situations. The video device can be a video camera. The video camera can be mounted to a windshield of the car. Alternatively, the video camera can be incorporated into the dashboard or console area of the car. The video camera can calculate speed, velocity, type, and/or position information related to other cars within the real-time TS. The video camera can also include warning indicators, such as light emitting diodes (LEDs) that emit different colors for the different driving situations.
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3.发明授权公开(公告)号：US09336495B2
公开(公告)日：2016-05-10
申请号：US14064949
申请日：2013-10-28
Applicant: NEC Laboratories America, Inc.
Inventor： Bing Bai , Christopher Malon , Iain Melvin
CPC classification number: G06N99/005 , G06F17/30864
Abstract: Semantic indexing methods and systems are disclosed. One such method is directed to training a semantic indexing model by employing an expanded query. The query can be expanded by merging the query with documents that are relevant to the query for purposes of compensating for a lack of training data. In accordance with another exemplary aspect, time difference features can be incorporated into a semantic indexing model to account for changes in query distributions over time.
Abstract translation: 公开了语义索引方法和系统。 一种这样的方法旨在通过使用扩展查询来训练语义索引模型。 通过将查询与与查询相关的文档合并，可以扩展查询，以补偿缺乏培训数据。 根据另一示例性方面，可以将时差特征并入到语义索引模型中以考虑随时间的查询分布的变化。
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4.发明授权公开(公告)号：US09224106B2
公开(公告)日：2015-12-29
申请号：US14077400
申请日：2013-11-12
Applicant: NEC Laboratories America, Inc.
Inventor： Eric Cosatto , Pierre-Francois Laquerre , Christopher Malon , Hans-Peter Graf , Iain Melvin
CPC classification number: G06N99/005 , G06K9/00147 , G06K9/4652 , G06K9/623 , G06K9/6262 , G06K9/6857 , G06T7/0012 , G06T2207/10056 , G06T2207/20081 , G06T2207/30024
Abstract: Systems and methods are disclosed for classifying histological tissues or specimens with two phases. In a first phase, the method includes providing off-line training using a processor during which one or more classifiers are trained based on examples, including: finding a split of features into sets of increasing computational cost, assigning a computational cost to each set; training for each set of features a classifier using training examples; training for each classifier, a utility function that scores a usefulness of extracting the next feature set for a given tissue unit using the training examples. In a second phase, the method includes applying the classifiers to an unknown tissue sample with extracting the first set of features for all tissue units; deciding for which tissue unit to extract the next set of features by finding the tissue unit for which a score: S=U−h*C is maximized, where U is a utility function, C is a cost of acquiring the feature and h is a weighting parameter; iterating until a stopping criterion is met or no more feature can be computed; and issuing a tissue-level decision based on a current state.
Abstract translation: 公开了用于对两个阶段的组织学组织或标本进行分类的系统和方法。 在第一阶段中，该方法包括使用处理器提供离线训练，在该训练期间，基于示例对一个或多个分类器进行训练，包括：将特征分组发现成增加计算成本的集合，为每个集合分配计算成本; 训练每组功能一个分类器使用训练样例; 每个分类器的训练，一个效用函数，其使用训练示例评估为给定组织单位提取下一个特征集的有用性。 在第二阶段中，该方法包括通过提取所有组织单元的第一组特征将分类器应用于未知组织样本; 决定哪个组织单元通过找到最大化分数S = U-h * C的组织单位来提取下一组特征，其中U是效用函数，C是获取特征的成本，h是 加权参数; 迭代直到满足停止标准或不能计算更多的特征; 以及基于当前状态发布组织级决定。
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公开(公告)号：US20250148624A1
公开(公告)日：2025-05-08
申请号：US18934512
申请日：2024-11-01
Applicant: NEC Laboratories America, Inc.
Inventor： Deep Patel , Iain Melvin , Alexandru Niculescu-Mizil
Abstract: Systems and methods for a multi-entity tracking transformer model (MCTR). To train the MCTR, processing track embeddings and detection embeddings of video feeds obtained from multiple cameras to generate updated track embeddings with a tracking module. The updated track embeddings can be associated with the detection embeddings to generate track-detection associations (TDA) for each camera view and camera frame with an association module. A cost module can calculate a differentiable loss from the TDA by combining a detection loss, a track loss and an auxiliary track loss. A model trainer can train the MCTR using the differentiable loss and contiguous video segments sampled from a training dataset to track multiple objects with multiple cameras.
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Patent Agency Ranking
公开(公告)号：US20250053774A1
公开(公告)日：2025-02-13
申请号：US18776926
申请日：2024-07-18
Applicant: NEC Laboratories America, Inc.
Inventor： Christopher Malon , Christopher A White , Renqiang Min , Iain Melvin
Abstract: Methods and systems for answering a query include generating first tokens in response to an input query using a language model, the first tokens including a retrieval rule. A retrieval rule is used to search for information to generate dynamic tokens. The retrieval rule in the first tokens is replaced with the dynamic tokens to generate a dynamic partial response. Second tokens are generated in response to the input query. The second tokens are appended to the dynamic partial response to generate an output responsive to the input query.
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公开(公告)号：US11055605B2
公开(公告)日：2021-07-06
申请号：US15785796
申请日：2017-10-17
Applicant: NEC Laboratories America, Inc.
Inventor： Hans Peter Graf , Eric Cosatto , Iain Melvin
IPC: G06N3/08 , G06N3/04 , G01S7/41 , G01S13/931
Abstract: A computer-implemented method executed by a processor for training a neural network to recognize driving scenes from sensor data received from vehicle radar is presented. The computer-implemented method includes extracting substructures from the sensor data received from the vehicle radar to define a graph having a plurality of nodes and a plurality of edges, constructing a neural network for each extracted substructure, combining the outputs of each of the constructed neural networks for each of the plurality of edges into a single vector describing a driving scene of a vehicle, and classifying the single vector into a set of one or more dangerous situations involving the vehicle.
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公开(公告)号：US20180307967A1
公开(公告)日：2018-10-25
申请号：US15785796
申请日：2017-10-17
Applicant: NEC Laboratories America, Inc.
Inventor： Hans Peter Graf , Eric Cosatto , Iain Melvin
CPC classification number: G06N3/04 , G01S7/417 , G01S13/931 , G06N3/08
Abstract: A computer-implemented method executed by a processor for training a neural network to recognize driving scenes from sensor data received from vehicle radar is presented. The computer-implemented method includes extracting substructures from the sensor data received from the vehicle radar to define a graph having a plurality of nodes and a plurality of edges, constructing a neural network for each extracted substructure, combining the outputs of each of the constructed neural networks for each of the plurality of edges into a single vector describing a driving scene of a vehicle, and classifying the single vector into a set of one or more dangerous situations involving the vehicle.
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公开(公告)号：US20180081053A1
公开(公告)日：2018-03-22
申请号：US15689656
申请日：2017-08-29
Applicant: NEC Laboratories America, Inc.
Inventor： Iain Melvin , Eric Cosatto , Igor Durdanovic , Hans Peter Graf
CPC classification number: G01S13/931 , B60G2400/823 , B60Q9/008 , B60R1/00 , B60R2300/301 , B60R2300/8093 , B60W30/09 , B60W2420/42 , B60W2420/52 , G01S7/20 , G01S7/2955 , G01S7/417 , G01S13/867 , G01S17/936 , G01S2013/936 , G01S2013/9367 , G01S2013/9375 , G06K9/00805 , G06K9/46 , G06K9/6215 , G06K9/6232 , G06N3/0454 , G06N3/08 , G06N3/084
Abstract: A computer-implemented method and system are provided. The system includes an image capture device configured to capture image data relative to an ambient environment of a user. The system further includes a processor configured to detect and localize objects, in a real-world map space, from the image data using a trainable object localization Convolutional Neural Network (CNN). The CNN is trained to detect and localize the objects from image and radar pairs that include the image data and radar data for different scenes of a natural environment. The processor is further configured to perform a user-perceptible action responsive to a detection and a localization of an object in an intended path of the user.
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公开(公告)号：US20240394524A1
公开(公告)日：2024-11-28
申请号：US18670275
申请日：2024-05-21
Applicant: NEC Laboratories America, Inc.
Inventor： Iain Melvin
Abstract: Methods and systems for configuring a machine learning model include selecting a head from a set of stored heads, responsive to an input, to implement a layer in a transformer machine learning model. The selected head is copied from persistent storage to active memory. The layer in the transformer machine learning model is executed on the input using the selected head to generate an output. An action is performed responsive to the output.
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