Nvidia corporation (20240096115). LANDMARK DETECTION WITH AN ITERATIVE NEURAL NETWORK simplified abstract

LANDMARK DETECTION WITH AN ITERATIVE NEURAL NETWORK

Organization Name

nvidia corporation

Inventor(s)

Pavlo Molchanov of Mountain View CA (US)

Jan Kautz of Lexington MA (US)

Arash Vahdat of San Mateo CA (US)

Hongxu Yin of San Jose CA (US)

Paul Micaelli of Edinburgh (GB)

LANDMARK DETECTION WITH AN ITERATIVE NEURAL NETWORK - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240096115 titled 'LANDMARK DETECTION WITH AN ITERATIVE NEURAL NETWORK

Simplified Explanation

The abstract of the patent application describes a method for landmark detection using an iterative neural network to reduce jitter in landmark detection results for video.

• The patent application proposes landmark detection using an iterative neural network.
• The method reduces jitter in landmark detection results for video by reusing previous hidden states from previous frames.

Potential Applications

The technology can be applied in various computer vision tasks such as emotion recognition, face identity verification, hand tracking, gesture recognition, and eye gaze tracking.

Problems Solved

1. Current landmark detection methods rely on cascaded computation through cascaded networks or an ensemble of multiple models, leading to increased training memory cost and jitter in landmark detection results for video. 2. The iterations required by current methods do not have an obvious stopping criteria, leading to inefficiencies in landmark detection.

Benefits

1. Improved landmark detection accuracy using an iterative neural network. 2. Reduction in jitter in landmark detection results for video by reusing previous hidden states from previous frames.

Potential Commercial Applications

Enhancing facial recognition systems, improving hand tracking in virtual reality applications, enhancing gesture recognition in human-computer interaction systems.

Possible Prior Art

One possible prior art could be the use of cascaded networks for landmark detection in computer vision tasks.

How does the iterative neural network improve landmark detection accuracy compared to current methods?

The iterative neural network in the patent application refines landmark detection by iteratively producing corrected landmarks that match the input more finely. This iterative process allows for more accurate landmark detection compared to the linear iterations in current methods.

What specific techniques are used to reduce jitter in landmark detection results for video?

The patent application mentions the reuse of previous hidden states from previous frames to reduce jitter in landmark detection results for video. This technique helps maintain consistency in landmark detection across frames in a video sequence.

Original Abstract Submitted

landmark detection refers to the detection of landmarks within an image or a video, and is used in many computer vision tasks such emotion recognition, face identity verification, hand tracking, gesture recognition, and eye gaze tracking. current landmark detection methods rely on a cascaded computation through cascaded networks or an ensemble of multiple models, which starts with an initial guess of the landmarks and iteratively produces corrected landmarks which match the input more finely. however, the iterations required by current methods typically increase the training memory cost linearly, and do not have an obvious stopping criteria. moreover, these methods tend to exhibit jitter in landmark detection results for video. the present disclosure improves current landmark detection methods by providing landmark detection using an iterative neural network. furthermore, when detecting landmarks in video, the present disclosure provides for a reduction in jitter due to reuse of previous hidden states from previous frames.