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Facial Recognition For Masked Individuals

Patent Number: 11,710,346
Xiang Yu; Ting Wang; Yi-Hsuan Tsai; Masoud Faraki; Ming-Fang Huang; Jian Fang; Yue Tian; Francesco Pittaluga; Gaurav Sharma; Yuheng Chen; Manmohan Chandraker
July 25, 2023

Joint Rolling Shutter Image Stitching and Rectification

Patent Number: 11,694,311
Bingbing Zhuang; Quoc-Huy Tran
July 04, 2023

Self-Supervised Cross-Video Temporal Difference Learning For Unsupervised Domain Adaptation

Patent Number: 11,676,370
Jinwoo Choi; Gaurav Sharma
June 13, 2023

Human Detection In Scenes

Patent Number: 11,610,420
Buyu Liu; Kihyuk Sohn; Jong-Chyi Su; Manmohan Chandraker; Yi-Hsuan Tsai
March 21, 2023

Domain Adaptation For Structured Output Via Disentangled Representations

Patent Number: 11,604,943
Kihyuk Sohn; Samuel Schulter; Manmohan Chandraker; Yi-Hsuan Tsai
March 14, 2023

Segmentation To Determine Lane Markings And Road Signs

Patent Number: 11,604,945
Yi-Hsuan Tsai; Manmohan Chandraker; Kihyuk Sohn; Buyu Liu; Jong-Chyi Su
March 14, 2023

Deep Face Recognition Based On Clustering Over Unlabeled Face Data

Patent Number: 11,600,113
Xiang Yu; Manmohan Chandraker; Aruni RoyChowdhury; Kihyuk Sohn
March 7, 2023

Joint Rolling Shutter Correction And Image Deblurring

Patent Number: 11,599,974
Quoc-Huy Tran; Manmohan Chandraker; Bingbing Zhuang; Pan Ji
March 7, 2023

Obstacle Detection In Road Scenes

Patent Number: 11,594,041
Kihyuk Sohn; Buyu Liu; Yi-Hsuan Tsai; Manmohan Chandraker; Jong-Chyi Su
February 28, 2023

Construction Zone Segmentation

Patent Number: 11,580,334
Manmohan Chandraker; Buyu Liu; Jong-Chyi Su; Kihyuk Sohn; Yi-Hsuan Tsai
February 14, 2023

Universal Feature Representation Learning For Face Recognition

Patent Number: 11,580,780
Yichun Shi; Xiang Yu; Manmohan Chandraker; Kihyuk Sohn
February 14, 2023

Learning To Simulate

Patent Number: 11,518,382
Nataniel Ruiz; Samuel Schulter; Manmohan Chandraker
December 6, 2022

Privacy-Preserving Visual Recognition Via Adversarial Learning

Patent Number: 11,520,923
Yi-Hsuan Tsai; Manmohan Chandraker; Kihyuk Sohn
December 6, 2022

Scene Attribute Annotation of Complex Road Typographies

Patent Number: 11,468,591
Samuel Schulter
October 11, 2022

Shuffle, Attend, and Adapt: Video Domain Adaptation by Clip Order Prediction and Clip Attention Alignment

Patent Number: 11,468,680
Gaurav Sharma; Samuel Schulter; Jinwoo Choi
October 11, 2022

Pseudo RGB-D for Self-Improving Monocular Slam and Depth Prediction

Patent Number: 11,468,585
Manmohan Chandraker; Quoc-Huy Tran; Pan Ji; Lokender Tiwari
October 11, 2022

Multi-Task Perception Network with Applications to Scene Understanding and Advanced Driver-Assistance System

Patent Number: 11,462,112
Buyu Liu; Paul Vernaza; Samuel Schulter; Manmohan Chandraker; Yi-Hsuan Tsai; Quoc-Huy Tran; Pan Ji
October 4, 2022

Parametric Top-View Representation of Complex Road Scenes

Patent Number: 11,455,813
Samuel Schulter; Buyu Liu; Manmohan Chandraker; Bingbing Zhuang
September 27, 2022

Parametric Top-View Representation Of Scenes

Patent Number: 11,373,067
Buyu Liu; Ziyan Wang; Samuel Schulter; Manmohan Chandraker
June 28, 2022

Self-Supervised Visual Odometry Framework Using Long-Term Modeling And Incremental Learning

Patent Number: 11,321,853
Pan Ji; Manmohan Chandraker; Yuliang Zou; Quoc-Huy Tran
May 3, 2022

Action Recognition System For Action Recognition In Unlabeled Videos With Domain Adversarial Learning And Knowledge Distillation

Patent Number: 11,314,993
Manmohan Chandraker; Kihyuk Sohn; Xiang Yu
April 26, 2022

Unsupervised Domain Adaptation for Video Classification

Patent Number: 11,301,716
Manmohan Chandraker; Jinwoo Choi; Gaurav SharApril 12, 2022

Face Spoofing Detection Using A Physical-Cue-Guided Multi-Source Multi-Channel Framework

Patent Number: 11,250,282
Junru Wu; Xiang Yu; Buyu Liu; Manmohan Chandraker
February 15, 2022

Human Action Recognition In Drone Videos

Patent Number: 11,250,573
Gaurav Sharma; Manmohan Chandraker; Jinwoo Choi
February 15, 2022

Image/Video Deblurring Using Convolutional Neural Networks With Applications To Sfm/Slam With Blurred Images/Videos

Patent Number: 11,222,409
Quoc-Huy Tran; Pan Ji; Manmohan Chandraker; Bingbing Zhuang
January 11, 2022

Object Detection With Training From Multiple Datasets

Patent Number: 11,222,238
Samuel Schulter; Yi-Hsuan Tsai; Xiangyun Zhao; Manmohan Chandraker; Gaurav SharJanuary 11, 2022

Attention And Warping Based Domain Adaptation For Videos

Patent Number: 11,222,210
Gaurav Sharma; Manmohan Chandraker
January 11, 2022

Traffic Prediction With Reparameterized Pushforward Policy For Autonomous Vehicles

Patent Number: 11,189,171
Nicholas Rhinehart; Paul Vernaza
November 30, 2021

Rolling Shutter Rectification In Images/Videos Using Convolutional Neural Networks With Applications To Sfm/Slam With Rolling Shutter Images/Videos

Patent Number: 11,132,586
Manmohan Chandraker; Quoc-Huy Tran; Pan Ji; Bingbing Zhuang
September 28, 2021

Recognizing Fine-Grained Objects In Surveillance Camera Images

Patent Number: 11,087,142
Shuyang Dai; Kihyuk Sohn; Yi-Hsuan Tsai; Manmohan Chandraker
August 10, 2021

Viewpoint Invariant Object Recognition By Synthesization And Domain Adaptation

Patent Number: 11,055,989
Luan Tran; Kihyuk Sohn; Manmohan Chandraker; Xiang Yu
July 6, 2021

Balancing Diversity And Precision Of Generative Models With Complementary Density Estimators

Patent Number: 11,049,265
Anqi Liu; Kihyuk Sohn; Nicholas Rhinehart; Paul Vernaza
June 29, 2021

Pose-Variant 3D Facial Attribute Generation

Patent Number: 10,991,145
Feng-Ju Chang; Xiang Yu; Manmohan Chandraker
April 27, 2021

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The Industrial Lab Advantage: Delivering on the Expectations of AI | Future of Work News

November 10, 2023

Our Chris White outlines how Invisible AI transforms our lives and its potential to bring about transformative societal changes, including safer space travel, reliable cell networks, smarter cities, productive factories, and efficient homes. Invisible AI will intelligently anticipate needs, automate tasks, and enhance the human experience.

Adaptation Speed Analysis for Fairness-Aware Causal Models

October 25, 2023

For example, in machine translation tasks, to achieve bidirectional translation between two languages, the source corpus is often used as the target corpus, which involves the training of two models with opposite directions. The question of which one can adapt most quickly to a domain shift is of significant importance in many fields. Specifically, consider an original distribution p that changes due to an unknown intervention, resulting in a modified distribution p*. In aligning p with p*, several factors can affect the adaptation rate, including the causal dependencies between variables in p. In real-life scenarios, however, we have to consider the fairness of the training process, and it is particularly crucial to involve a sensitive variable (bias) present between a cause and an effect variable. To explore this scenario, we examine a simple structural causal model (SCM) with a cause-bias-effect structure, where variable A acts as a sensitive variable between cause (X) and effect (Y). The two models respectively exhibit consistent and contrary cause-effect directions in the cause-bias-effect SCM. After conducting unknown interventions on variables within the SCM, we can simulate some kinds of domain shifts for analysis. We then compare the adaptation speeds of two models across four shift scenarios. Additionally, we prove the connection between the adaptation speeds of the two models across all interventions.

Calibrate Graph Neural Networks under Out-of-Distribution Nodes via Deep Q-learning

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October 25, 2023

Graph neural networks (GNNs) have achieved great success in dealing with graph-structured data that are prevalent in the real world. The core of graph neural networks is the message passing mechanism that aims to generate the embeddings of nodes by aggregating the neighboring node information. However, recent work suggests that GNNs also suffer the trustworthiness issues. Our empirical study shows that the calibration error of the in-distribution (ID) nodes would be exacerbated if a graph is mixed with out-of-distribution (OOD) nodes, and we assume that the noisy information from OOD nodes is the root for the worsened calibration error. Both previous study and our empirical study suggest that adjusting the weights of edges could be a promising way to reduce the adverse impact from the OOD nodes. However, how to precisely select the desired edges and modify the corresponding weights is not trivial, since the distribution of OOD nodes is unknown to us. To tackle this problem, we propose a Graph Edge Re-weighting via Deep Q-learning (GERDQ) framework to calibrate the graph neural networks. Our framework aims to explore the potential influence of the change of the edge weights on target ID nodes by sampling and traversing the edges in the graph, and we formulate this process as a Markov Decision Process (MDP). Many existing GNNs could be seamlessly incorporated into our framework. Experimental results show that when wrapped with our method, the existing GNN models can yield lower calibration error under OOD nodes as well as comparable accuracy compared to the original ones and other strong baselines. The source code is available at:https://github.com/DamoSWL/Calibration-GNN-OOD.

About Us

NEC Laboratories America, Inc. (NEC Labs) is the US-based center for NEC Corporation’s global network of corporate research laboratories. Our diverse research groups collaborate with industry, academia and governments to provide disruptive solutions to complex problems. A leader in the integration of IT and network technologies with more than 100 years of expertise, NEC provides a combination of products and solutions that cross-utilize the company’s experience and global resources to meet the complex and ever-changing needs of its customers.

Media Analytics | Patents

Patents

Facial Recognition For Masked Individuals

Joint Rolling Shutter Image Stitching and Rectification

Self-Supervised Cross-Video Temporal Difference Learning For Unsupervised Domain Adaptation

Human Detection In Scenes

Domain Adaptation For Structured Output Via Disentangled Representations

Segmentation To Determine Lane Markings And Road Signs

Deep Face Recognition Based On Clustering Over Unlabeled Face Data

Joint Rolling Shutter Correction And Image Deblurring

Obstacle Detection In Road Scenes

Construction Zone Segmentation

Universal Feature Representation Learning For Face Recognition

Learning To Simulate

Privacy-Preserving Visual Recognition Via Adversarial Learning

Scene Attribute Annotation of Complex Road Typographies

Shuffle, Attend, and Adapt: Video Domain Adaptation by Clip Order Prediction and Clip Attention Alignment

Pseudo RGB-D for Self-Improving Monocular Slam and Depth Prediction

Multi-Task Perception Network with Applications to Scene Understanding and Advanced Driver-Assistance System

Parametric Top-View Representation of Complex Road Scenes

Parametric Top-View Representation Of Scenes

Self-Supervised Visual Odometry Framework Using Long-Term Modeling And Incremental Learning

Action Recognition System For Action Recognition In Unlabeled Videos With Domain Adversarial Learning And Knowledge Distillation

Unsupervised Domain Adaptation for Video Classification

Face Spoofing Detection Using A Physical-Cue-Guided Multi-Source Multi-Channel Framework

Human Action Recognition In Drone Videos

Image/Video Deblurring Using Convolutional Neural Networks With Applications To Sfm/Slam With Blurred Images/Videos

Object Detection With Training From Multiple Datasets

Attention And Warping Based Domain Adaptation For Videos

Traffic Prediction With Reparameterized Pushforward Policy For Autonomous Vehicles

Rolling Shutter Rectification In Images/Videos Using Convolutional Neural Networks With Applications To Sfm/Slam With Rolling Shutter Images/Videos

Recognizing Fine-Grained Objects In Surveillance Camera Images

Viewpoint Invariant Object Recognition By Synthesization And Domain Adaptation

Balancing Diversity And Precision Of Generative Models With Complementary Density Estimators

Pose-Variant 3D Facial Attribute Generation

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