18529173. Hierarchical Video Encoders simplified abstract (GOOGLE LLC)

Hierarchical Video Encoders

Organization Name

GOOGLE LLC

Inventor(s)

Vihan Jain of San Francisco CA (US)

Joonseok Lee of Fremont CA (US)

Ming Zhao of Sunnyvale CA (US)

Sheide Chammas of San Francisco CA (US)

Hexiang Hu of Los Angeles CA (US)

Bowen Zhang of Los Angeles CA (US)

Fei Sha of Los Angeles CA (US)

Tze Way Eugene Ie of Los Altos CA (US)

Hierarchical Video Encoders - A simplified explanation of the abstract

This abstract first appeared for US patent application 18529173 titled 'Hierarchical Video Encoders

Simplified Explanation

The computer-implemented method described in the abstract involves generating video representations using a hierarchical video encoder. Here is a simplified explanation of the abstract:

• Obtaining a video with multiple frames
• Processing each frame using a machine-learned encoder to generate frame representations
• Determining segment representations based on frame representations
• Processing segment representations with a machine-learned encoder to generate contextualized segment representations
• Generating a video representation based on contextualized segment representations

Potential Applications

This technology could be applied in video editing software, video streaming platforms, surveillance systems, and virtual reality applications.

Problems Solved

This technology solves the problem of efficiently generating video representations by utilizing machine learning models to process frames and segments.

Benefits

The benefits of this technology include improved video representation quality, faster processing speeds, and enhanced contextual understanding of video segments.

Potential Commercial Applications

The potential commercial applications of this technology include video production tools, video analytics software, and content recommendation systems.

Possible Prior Art

One possible prior art for this technology could be existing video encoding and processing techniques used in the field of computer vision and machine learning.

Unanswered Questions

How does this technology handle different types of video content, such as action scenes versus dialogue scenes?

The abstract does not provide specific details on how the technology adapts to different types of video content to generate accurate representations.

What are the limitations of this hierarchical video encoder in terms of scalability and complexity?

The abstract does not address potential limitations of the technology in handling large-scale video datasets or complex video structures.

Original Abstract Submitted

A computer-implemented method for generating video representations utilizing a hierarchical video encoder includes obtaining a video, wherein the video includes a plurality of frames, processing each of the plurality of frames with a machine-learned frame-level encoder model to respectively generate a plurality of frame representations for the plurality of frames, the plurality of frame representations respective to the plurality of frames determining a plurality of segment representations representative of a plurality of video segments including one or more of the plurality of frames, the plurality of segment representations based at least in part on the plurality of frame representations, processing the plurality of segment representations with a machine-learned segment-level encoder model to generate a plurality of contextualized segment representations, determining a video representation based at least in part on the plurality of contextualized segment representations, and providing the video representation as an output.