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Welcome to the NEC Labs America blog, where we cover important topics related to our research in a more informal medium than a research paper.

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Check out our #computervision papers at #CVPR2022

The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses.NEC Laboratories Media Analytics department is excited to be presenting at CVPR 2022, being held in New Orleans, LA from Sunday, June 19 to Friday, June 24, 2022.Learning To Learn Across Diverse Data Biases in Deep Face Recognition- Authors: Chang Liu, Xiang Yu, Yi-Hsuan Tsai, Ramin Moslemi, Masoud …

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NEC provides AI-based traffic monitoring system with fiber-optic sensing technology for NEXCO CENTRAL

Tokyo, May 24, 2022 - NEC Corporation has deployed an AI-based traffic monitoring system to Central Nippon Expressway Company Limited (NEXCO CENTRAL). The system uses fiber-optic sensing and AI technologies to visualize traffic conditions, such as the location, speed, and direction of travel, from vibrations produced by vehicle movement.The system includes sensing devices attached to one end of an optical fiber and an analytical AI engine, developed by NEC Laboratories America, which makes it p…

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Privacy-Aware AI Models

We developed two types of technologies for privacy-aware AI models: differential privacy and differentially-private federated learning. These technologies prevent attackers from using model outputs to decipher data, either in stand-alone training or in combined training across multiple stakeholders. Furthermore, our privacy-aware AI models achieve high privacy and accuracy with less data required compared to competitors. For example, Google needs much larger data than us, so it cannot achieve h…

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Universal Representations for Responsible and Robust AI

Responsible AI systems for surveillance, healthcare and biometrics applications that focus on people need to satisfy legal and social requirements. Moreover, robust AI engines need to achieve accuracy in new and unexpected situations for applications such as mobility, insurance, construction and factories. To enable both responsible and robust AI systems while reducing the cost of data collection and annotation, we present a URLearning framework to learn universal representations for visual ana…

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Understanding Road Scenes in Videos

We review our recent work on 3D scene understanding, which appeared in CVPR 2020 as "Understanding Road Layout in Videos as a Whole" by Buyu Liu, Bingbing Zhuang, Samuel Schulter, Pan Ji and Manmohan Chandraker.SummaryGiven a video of a complex scene, we represent various elements of the scene in a way that is both interpretable and accurate. This allows explainable decision-making for downstream application and intuitive visualization for human-machine interaction. For a road scene, …

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AI Solutions With a Guarantee of Privacy

We review our recent work on differential privacy, which appeared in CVPR 2020 as "Private-kNN: Practical Differential Privacy for Computer Vision" by Xiang Yu Manmohan ChandrakerSummaryWith increasing ethical and legal concerns about privacy for deep models in visual recognition, differential privacy has emerged as a mechanism to disguise membership of sensitive data in training datasets. Previous methods, like private aggregation of teacher ensembles (PATE), leverage a large ensembl…

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Media Analytics Research at CVPR 2020

CVPR is a highly rated conference for computer vision, a virtual version of which was held during June 2020. The MA team from NEC Labs America presented four papers at the conference. These papers covered a range of topics, including outdoor and indoor 3D scene understanding, learning universal representations for face recognition in the wild and practical methods for differential privacy that achieve high levels of theoretical guarantee and accuracy even with limited data.Private-kNN: Making D…

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Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction

In this post, we review our recent CVPR 2021 paper (oral) "Divide and Conquer for Lane Aware Diverse Trajectory Prediction" by Sriram Narayanan, Ramin Moslemi, Francesco Pittaluga and Buyu Liu.SummaryTrajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute actions. Our work addresses two key challenges in trajectory prediction: (i) learning multimodal outputs and (ii) improving predictions by imposing constraints using driving knowledge. Recent metho…

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Camera Motion Estimation by Learning Uncertainty-Aware Fusion

We review our recent work on camera motion estimation, which has been accepted by CVPR 2021 as an oral paper entitled "Fusing the Old with the New: Learning Relative Camera Pose with Geometry-Guided Uncertainty" by Bingbing Zhuang and Manmohan Chandraker.SummaryHumans and autonomous agents always move in a 3D environment rather than on a 2D image plane, making 3D motion perception a fundamental capability for any intelligent system. As such, estimating relative camera pose or motion f…

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Cross-Domain Similarity Learning for Face Recognition in Unseen Domains

In this post, we review our recent CVPR 2021 paper, Cross-Domain Similarity Learning for Face Recognition in Unseen Domains, by Masoud FarakiXiang YuYumin SuhManmohan Chandraker.SummaryFacial recognition (FR)-based systems continue to be the preferred approach for many applications such as biometric technologies and emotion analysis. That is mainly because FR is the most natural way to meet these types of demands with no physical interaction with the end-user. Furthermore, it is fast and easy t…

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