NVIDIA Corporation (20240221166). POINT-LEVEL SUPERVISION FOR VIDEO INSTANCE SEGMENTATION simplified abstract

POINT-LEVEL SUPERVISION FOR VIDEO INSTANCE SEGMENTATION

Organization Name

NVIDIA Corporation

Inventor(s)

Zhiding Yu of Cupertino CA (US)

Shuaiyi Huang of Greenbelt MD (US)

De-An Huang of Cupertino CA (US)

Shiyi Lan of Sunnyvale CA (US)

Subhashree Radhakrishnan of Milpitas CA (US)

Jose M. Alvarez Lopez of Mountain View CA (US)

Anima Anandkumar of Pasadena CA (US)

POINT-LEVEL SUPERVISION FOR VIDEO INSTANCE SEGMENTATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240221166 titled 'POINT-LEVEL SUPERVISION FOR VIDEO INSTANCE SEGMENTATION

Abstract: Video instance segmentation is a computer vision task that aims to detect, segment, and track objects continuously in videos. It can be used in numerous real-world applications, such as video editing, three-dimensional (3D) reconstruction, 3D navigation (e.g. for autonomous driving and/or robotics), and view point estimation. However, current machine learning-based processes employed for video instance segmentation are lacking, particularly because the densely annotated videos needed for supervised training of high-quality models are not readily available and are not easily generated. To address the issues in the prior art, the present disclosure provides point-level supervision for video instance segmentation in a manner that allows the resulting machine learning model to handle any object category.

Key Features and Innovation:

• Video instance segmentation task
• Detection, segmentation, and tracking of objects in videos
• Real-world applications in video editing, 3D reconstruction, 3D navigation, and view point estimation
• Addressing the lack of densely annotated videos for supervised training
• Point-level supervision for handling any object category

Potential Applications: - Video editing software - Autonomous driving systems - Robotics applications - Augmented reality development

Problems Solved: - Lack of high-quality models due to the unavailability of densely annotated videos - Difficulty in training machine learning models for video instance segmentation - Inability to handle various object categories effectively

Benefits: - Improved accuracy in detecting and tracking objects in videos - Enhanced performance in video editing and 3D reconstruction tasks - Versatile application across different industries

Commercial Applications: Title: Innovative Video Instance Segmentation Technology for Enhanced Object Detection and Tracking This technology can be utilized in video editing software, autonomous driving systems, robotics applications, and augmented reality development. The market implications include improved efficiency, accuracy, and performance in various industries.

Prior Art: Prior research in video instance segmentation includes methods that rely on supervised learning with densely annotated videos. However, the present disclosure introduces a novel approach with point-level supervision to address the limitations of current techniques.

Frequently Updated Research: Stay updated on advancements in machine learning algorithms for video instance segmentation, as well as developments in object detection and tracking technologies.

Questions about Video Instance Segmentation: 1. How does point-level supervision improve video instance segmentation models? Point-level supervision allows the machine learning model to handle any object category effectively by providing detailed guidance at specific points in the video frames.

2. What are the challenges associated with generating densely annotated videos for supervised training in video instance segmentation? Generating densely annotated videos can be time-consuming and labor-intensive, leading to a lack of high-quality training data for machine learning models in this task.

Original Abstract Submitted

video instance segmentation is a computer vision task that aims to detect, segment, and track objects continuously in videos. it can be used in numerous real-world applications, such as video editing, three-dimensional (3d) reconstruction, 3d navigation (e.g. for autonomous driving and/or robotics), and view point estimation. however, current machine learning-based processes employed for video instance segmentation are lacking, particularly because the densely annotated videos needed for supervised training of high-quality models are not readily available and are not easily generated. to address the issues in the prior art, the present disclosure provides point-level supervision for video instance segmentation in a manner that allows the resulting machine learning model to handle any object category.