-
公开(公告)号：US11087174B2
公开(公告)日：2021-08-10
申请号：US16580497
申请日：2019-09-24
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Kai Li , Bing Bai , Hans Peter Graf
Abstract: A method is provided for visual inspection. The method includes learning, by a processor, group disentangled visual feature embedding vectors of input images. The input images include defective objects and defect-free objects. The method further includes generating, by the processor using a weight generation network, classification weights from visual features and semantic descriptions. Both the visual features and the semantic descriptions are for predicting defective and defect-free labels. The method also includes calculating, by the processor, a cosine similarity score between the classification weights and the group disentangled visual feature embedding vectors. The method additionally includes episodically training, by the processor, the weight generation network on the input images to update parameters of the weight generation network. The method further includes generating, by the processor using the trained weight generation network, a prediction of a test image as including any of defective objects and defect-free objects.
-
公开(公告)号：US20210174784A1
公开(公告)日：2021-06-10
申请号：US17114946
申请日：2020-12-08
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Christopher Malon , Hans Peter Graf
Abstract: Methods and systems for disentangled data generation include accessing a dataset including pairs, each formed from a given input text structure and a given style label for the input text structures. An encoder is trained to disentangle a sequential text input into disentangled representations, including a content embedding and a style embedding, based on a subset of the dataset, using an objective function that includes a regularization term that minimizes mutual information between the content embedding and the style embedding. A generator is trained to generate a text output that includes content from the style embedding, expressed in a style other than that represented by the style embedding of the text input.
-
公开(公告)号：US20210174213A1
公开(公告)日：2021-06-10
申请号：US17115464
申请日：2020-12-08
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Christopher Malon , Pengyu Cheng
Abstract: A computer-implemented method is provided for disentangled data generation. The method includes accessing, by a bidirectional Long Short-Term Memory (LSTM) with a multi-head attention mechanism, a dataset including a plurality of pairs each formed from a given one of a plurality of input text structures and given one of a plurality of style labels for the plurality of input text structures. The method further includes training the bidirectional LSTM as an encoder to disentangle a sequential text input into disentangled representations comprising a content embedding and a style embedding based on a subset of the dataset. The method also includes training a unidirectional LSTM as a decoder to generate a next text structure prediction for the sequential text input based on previously generated text structure information and a current word, from a disentangled representation with the content embedding and the style embedding.
-
74.发明授权公开(公告)号：US10885627B2
公开(公告)日：2021-01-05
申请号：US16371552
申请日：2019-04-01
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Farley Lai , Eric Cosatto , Hans Peter Graf
Abstract: Methods and systems for detecting and correcting anomalous inputs include training a neural network to embed high-dimensional input data into a low-dimensional space with an embedding that preserves neighbor relationships. Input data items are embedded into the low-dimensional space to form respective low-dimensional codes. An anomaly is determined among the high-dimensional input data based on the low-dimensional codes. The anomaly is corrected.
-
75.发明申请公开(公告)号：US20200097771A1
公开(公告)日：2020-03-26
申请号：US16580497
申请日：2019-09-24
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Kai Li , Bing Bai , Hans Peter Graf
Abstract: A method is provided for visual inspection. The method includes learning, by a processor, group disentangled visual feature embedding vectors of input images. The input images include defective objects and defect-free objects. The method further includes generating, by the processor using a weight generation network, classification weights from visual features and semantic descriptions. Both the visual features and the semantic descriptions are for predicting defective and defect-free labels. The method also includes calculating, by the processor, a cosine similarity score between the classification weights and the group disentangled visual feature embedding vectors. The method additionally includes episodically training, by the processor, the weight generation network on the input images to update parameters of the weight generation network. The method further includes generating, by the processor using the trained weight generation network, a prediction of a test image as including any of defective objects and defect-free objects.
-
Patent Agency Ranking
76.发明申请公开(公告)号：US20190304079A1
公开(公告)日：2019-10-03
申请号：US16371552
申请日：2019-04-01
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Farley Lai , Eric Cosatto , Hans Peter Graf
Abstract: Methods and systems for detecting and correcting anomalous inputs include training a neural network to embed high-dimensional input data into a low-dimensional space with an embedding that preserves neighbor relationships. Input data items are embedded into the low-dimensional space to form respective low-dimensional codes. An anomaly is determined among the high-dimensional input data based on the low-dimensional codes. The anomaly is corrected.
-
77.发明授权公开(公告)号：US10402653B2
公开(公告)日：2019-09-03
申请号：US15380014
申请日：2016-12-15
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Dongjin Song , Eric Cosatto
Abstract: A computer-implemented method and system are provided for video-based anomaly detection. The method includes forming, by a processor, a Deep High-Order Convolutional Neural Network (DHOCNN)-based model having a one-class Support Vector Machine (SVM) as a loss layer of the DHOCNN-based model. An objective of the SVM is configured to perform the video-based anomaly detection. The method further includes generating, by the processor, one or more predictions of an impending anomaly based on the high-order deep learning based model applied to an input image. The method also includes initiating, by the processor, an action to a hardware device to mitigate expected harm to at least one item selected from the group consisting of the hardware device, another hardware device related to the hardware device, and a person related to the hardware device.
-
公开(公告)号：US10366292B2
公开(公告)日：2019-07-30
申请号：US15794758
申请日：2017-10-26
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Yunchen Pu
IPC: G06K9/00 , G06K9/46 , G06N3/04 , G06K9/66 , H04N5/278 , G06K9/62 , H04N21/218 , H04N21/234 , H04N21/488 , G06K9/72 , H04N7/18
Abstract: A system is provided for video captioning. The system includes a processor. The processor is configured to apply a three-dimensional Convolutional Neural Network (C3D) to image frames of a video sequence to obtain, for the video sequence, (i) intermediate feature representations across L convolutional layers and (ii) top-layer features. The processor is further configured to produce a first word of an output caption for the video sequence by applying the top-layer features to a Long Short Term Memory (LSTM). The processor is further configured to produce subsequent words of the output caption by (i) dynamically performing spatiotemporal attention and layer attention using the intermediate feature representations to form a context vector, and (ii) applying the LSTM to the context vector, a previous word of the output caption, and a hidden state of the LSTM. The system further includes a display device for displaying the output caption to a user.
-
公开(公告)号：US10304008B2
公开(公告)日：2019-05-28
申请号：US15063236
申请日：2016-03-07
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Dongjin Song
Abstract: Systems and methods are disclosed for operating a machine, by receiving training data from one or more sensors; training a machine learning module with the training data by: partitioning a data matrix into smaller submatrices to process in parallel and optimized for each processing node; for each submatrix, performing a greedy search for rank-one solutions; using alternating direction method of multipliers (ADMM) to ensure consistency over different data blocks; and controlling one or more actuators using live data and the learned module during operation.
-
80.发明申请公开(公告)号：US20190122111A1
公开(公告)日：2019-04-25
申请号：US16168244
申请日：2018-10-23
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Bing Bai , Alexandru Niculescu-Mizil , Igor Durdanovic , Hans Peter Graf
Abstract: Systems and methods for predicting new relationships in the knowledge graph, including embedding a partial triplet including a head entity description and a relationship or a tail entity description to produce a separate vector for each of the head, relationship, and tail. The vectors for the head entity, relationship, and tail entity can be combined into a first matrix, and adaptive kernels generated from the entity descriptions can be applied to the matrix through convolutions to produce a second matrix having a different dimension from the first matrix. An activation function can be applied to the second matrix to obtain non-negative feature maps, and max-pooling can be used over the feature maps to get subsamples. A fixed length vector, Z, flattens the subsampling feature maps into a feature vector, and a linear mapping method is used to map the feature vectors into a prediction score.
-
-
-
-
-
-
-
-
-
Search Results
109
records
(took 0.025 seconds)
