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公开(公告)号：US12205356B2
公开(公告)日：2025-01-21
申请号：US18188766
申请日：2023-03-23
Applicant: NEC Laboratories America, Inc.
Inventor： Samuel Schulter , Sparsh Garg , Manmohan Chandraker
IPC: G06V10/776 , G06T7/00 , G06T7/11 , G06V10/74 , G06V10/774 , G06V20/70 , H04N17/00
Abstract: Methods and systems for detecting faults include capturing an image of a scene using a camera. The image is embedded using a segmentation model that includes an image branch having an image embedding layer that embeds images into a joint latent space and a text branch having a text embedding layer that embeds text into the joint latent space. Semantic information is generated for a region of the image corresponding to a predetermined static object using the embedded image. A fault of the camera is identified based on a discrepancy between the semantic information and semantic information of the predetermined static image. The fault of the camera is corrected.
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公开(公告)号：US20240379234A1
公开(公告)日：2024-11-14
申请号：US18659506
申请日：2024-05-09
Applicant: NEC Laboratories America, Inc.
Abstract: Methods and systems for visual question answering include decomposing an initial question to generate a sub-question. The initial question and an image are applied to a visual question answering model to generate an answer and a confidence score. It is determined that the confidence score is below a threshold value. The sub-question is applied to the visual question answering model, responsive to the determination that the confidence score is below a threshold value, to generate a final answer.
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公开(公告)号：US12045992B2
公开(公告)日：2024-07-23
申请号：US17520207
申请日：2021-11-05
Applicant: NEC Laboratories America, Inc.
Inventor： Yi-Hsuan Tsai , Masoud Faraki , Yumin Suh , Sparsh Garg , Manmohan Chandraker , Dongwan Kim
IPC: G06K9/00 , G06F18/214 , G06F18/2415 , G06F18/2431 , G06T7/11
CPC classification number: G06T7/11 , G06F18/2148 , G06F18/2415 , G06F18/2431 , G06T2207/20081 , G06T2207/20084
Abstract: Methods and systems for training a model include combining data from multiple datasets, the datasets having different respective label spaces. Relationships between labels in the different label spaces are identified. A unified neural network model is trained, using the combined data and the identified relationships to generate a unified model, with a class relational binary cross-entropy loss.
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公开(公告)号：US12005892B2
公开(公告)日：2024-06-11
申请号：US17090399
申请日：2020-11-05
Applicant: NEC Laboratories America, Inc.
Inventor： Sriram Nochur Narayanan , Manmohan Chandraker
CPC classification number: B60W30/095 , G05D1/0088 , G06F30/27 , B60W40/09 , B60W2556/10 , G01C21/3815 , G06V20/588
Abstract: A method for simultaneous multi-agent recurrent trajectory prediction is presented. The method includes reconstructing a topological layout of a scene from a dataset including real-world data, generating a road graph of the scene, the road graph capturing a hierarchical structure of interconnected lanes, incorporating vehicles from the scene on the generated road graph by utilizing tracklet information available in the dataset, assigning the vehicles to their closest lane identifications, and identifying diverse plausible behaviors for every vehicle in the scene. The method further includes sampling one behavior from the diverse plausible behaviors to select an associated velocity profile sampled from the real-world data of the dataset that resembles the sampled one behavior and feeding the road graph and the sampled velocity profile with a desired destination to a dynamics simulator to generate a plurality of simulated diverse trajectories output on a visualization device.
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公开(公告)号：US20240152767A1
公开(公告)日：2024-05-09
申请号：US18497079
申请日：2023-10-30
Applicant: NEC Laboratories America, Inc.
Inventor： Vijay Kumar Baikampady Gopalkrishna , Samuel Schulter , Xiang Yu , Zaid Khan , Manmohan Chandraker
Abstract: Systems and methods for training a visual question answer model include training a teacher model by performing image conditional visual question generation on a visual language model (VLM) and a targeted visual question answer dataset using images to generate question and answer pairs. Unlabeled images are pseudolabeled using the teacher model to decode synthetic question and answer pairs for the unlabeled images. The synthetic question and answer pairs for the unlabeled images are merged with real data from the targeted visual question answer dataset to generate a self-augmented training set. A student model is trained using the VLM and the self-augmented training set to return visual answers to text queries.
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Patent Agency Ranking
公开(公告)号：US11977602B2
公开(公告)日：2024-05-07
申请号：US17521252
申请日：2021-11-08
Applicant: NEC Laboratories America, Inc.
Inventor： Xiang Yu , Yi-Hsuan Tsai , Masoud Faraki , Ramin Moslemi , Manmohan Chandraker , Chang Liu
IPC: G06K9/00 , G06F18/21 , G06F18/214 , G06N20/00 , G06V40/16
CPC classification number: G06F18/214 , G06F18/217 , G06N20/00 , G06V40/172
Abstract: A method for training a model for face recognition is provided. The method forward trains a training batch of samples to form a face recognition model w(t), and calculates sample weights for the batch. The method obtains a training batch gradient with respect to model weights thereof and updates, using the gradient, the model w(t) to a face recognition model what(t). The method forwards a validation batch of samples to the face recognition model what(t). The method obtains a validation batch gradient, and updates, using the validation batch gradient and what(t), a sample-level importance weight of samples in the training batch to obtain an updated sample-level importance weight. The method obtains a training batch upgraded gradient based on the updated sample-level importance weight of the training batch samples, and updates, using the upgraded gradient, the model w(t) to a trained model w(t+1) corresponding to a next iteration.
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公开(公告)号：US20230073055A1
公开(公告)日：2023-03-09
申请号：US17903383
申请日：2022-09-06
Applicant: NEC Laboratories America, Inc.
Inventor： Yi-Hsuan Tsai , Sparsh Garg , Manmohan Chandraker , Samuel Shulter , Vijay Kumar Baikampady Gopalkrishna
IPC: G06T7/11
Abstract: A computer-implemented method for rut detection is provided. The method includes detecting, by a rut detection system, areas in a road-scene image that include ruts with pixel-wise probability values, wherein a higher value indicates a better chance of being a rut. The method further includes performing at least one of rut repair and vehicle rut avoidance responsive to the pixel-wise probability values. The detecting step includes performing neural network-based, pixel-wise semantic segmentation with context information on the road-scene image to distinguish rut pixels from non-rut pixels on a road depicted in the road-scene image.
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公开(公告)号：US11600113B2
公开(公告)日：2023-03-07
申请号：US17091066
申请日：2020-11-06
Applicant: NEC Laboratories America, Inc.
Inventor： Xiang Yu , Manmohan Chandraker , Kihyuk Sohn , Aruni RoyChowdhury
Abstract: A computer-implemented method for implementing face recognition includes obtaining a face recognition model trained on labeled face data, separating, using a mixture of probability distributions, a plurality of unlabeled faces corresponding to unlabeled face data into a set of one or more overlapping unlabeled faces that include overlapping identities to those in the labeled face data and a set of one or more disjoint unlabeled faces that include disjoint identities to those in the labeled face data, clustering the one or more disjoint unlabeled faces using a graph convolutional network to generate one or more cluster assignments, generating a clustering uncertainty associated with the one or more cluster assignments, and retraining the face recognition model on the labeled face data and the unlabeled face data to improve face recognition performance by incorporating the clustering uncertainty.
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公开(公告)号：US11580780B2
公开(公告)日：2023-02-14
申请号：US17091011
申请日：2020-11-06
Applicant: NEC Laboratories America, Inc.
Inventor： Xiang Yu , Manmohan Chandraker , Kihyuk Sohn , Yichun Shi
Abstract: A computer-implemented method for implementing face recognition includes receiving training data including a plurality of augmented images each corresponding to a respective one of a plurality of input images augmented by one of a plurality of variations, splitting a feature embedding generated from the training data into a plurality of sub-embeddings each associated with one of the plurality of variations, associating each of the plurality of sub-embeddings with respective ones of a plurality of confidence values, and applying a plurality of losses including a confidence-aware identification loss and a variation-decorrelation loss to the plurality of sub-embeddings and the plurality of confidence values to improve face recognition performance by learning the plurality of sub-embeddings.
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公开(公告)号：US20220147767A1
公开(公告)日：2022-05-12
申请号：US17521252
申请日：2021-11-08
Applicant: NEC Laboratories America, Inc.
Inventor： Xiang Yu , Yi-Hsuan Tsai , Masoud Faraki , Ramin Moslemi , Manmohan Chandraker , Chang Liu
Abstract: A method for training a model for face recognition is provided. The method forward trains a training batch of samples to form a face recognition model w(t), and calculates sample weights for the batch. The method obtains a training batch gradient with respect to model weights thereof and updates, using the gradient, the model w(t) to a face recognition model what(t). The method forwards a validation batch of samples to the face recognition model what(t). The method obtains a validation batch gradient, and updates, using the validation batch gradient and what(t), a sample-level importance weight of samples in the training batch to obtain an updated sample-level importance weight. The method obtains a training batch upgraded gradient based on the updated sample-level importance weight of the training batch samples, and updates, using the upgraded gradient, the model w(t) to a trained model w(t+1) corresponding to a next iteration.
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