Kolmogorov-Arnold Networks: KAN You Make It Work?
Although deep learning is a very new branch of...
Facial identification and verification for consumer and security applications.
Activity recognition and threat detection across camera views.
Spatial computing, gesture recognition, and gaze estimation for headsets.
Millions of identities and clothing options to train best-in-class models.
Simulate driver and occupant behavior captured with multi-modal cameras.
Simulate edge cases and rare events to ensure the robust performance of autonomous vehicles.
Together, we’re building the future of computer vision & machine learning
Facial identification and verification for consumer and security applications.
Activity recognition and threat detection across camera views.
Spatial computing, gesture recognition, and gaze estimation for headsets.
Millions of identities and clothing options to train best-in-class models.
Simulate driver and occupant behavior captured with multi-modal cameras.
Simulate edge cases and rare events to ensure the robust performance of autonomous vehicles.
Together, we’re building the future of computer vision & machine learning
Facial identification and verification for consumer and security applications.
Activity recognition and threat detection across camera views.
Spatial computing, gesture recognition, and gaze estimation for headsets.
Millions of identities and clothing options to train best-in-class models.
Simulate driver and occupant behavior captured with multi-modal cameras.
Simulate edge cases and rare events to ensure the robust performance of autonomous vehicles.
Together, we’re building the future of computer vision & machine learning
Facial identification and verification for consumer and security applications.
Activity recognition and threat detection across camera views.
Spatial computing, gesture recognition, and gaze estimation for headsets.
Millions of identities and clothing options to train best-in-class models.
Simulate driver and occupant behavior captured with multi-modal cameras.
Simulate edge cases and rare events to ensure the robust performance of autonomous vehicles.
Together, we’re building the future of computer vision & machine learning
Synthetic data for computer vision to enable more capable and ethical AI.
Robust pedestrian monitoring is critical for ensuring the safety and performance of driver assistance systems and autonomous vehicles. Predictive pedestrian detection is a form of automotive AI that can help computer vision systems interpret, prepare for and respond to any number of scenarios, with or without driver oversight.
Obtaining training data is difficult given safety risks and the potential for large numbers of edge cases. The most obvious example is a child running in front of a vehicle, a scenario for which it is unethical and impractical to create the necessary training data. Synthetic data enables computer vision engineers to create millions of simulations of such situations, informing the development of critical safety systems.