Multimodal Video Segmentation Using Visual and Language Features
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Solution Overview
Problem
Conventional video summarization systems struggle with segmenting long livestream videos due to unclear visual changes, requiring manual segmentation which is time-consuming.
Innovation Solution
A system and method for video segmentation and summarization that uses a combination of visual features and language features, employing a text encoder with a transformer network and an image encoder with a convolutional neural network to segment videos unsupervisedly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional video summarization systems use visual data for segmentation, then scene changes can be detected, but visual changes are unclear in livestream videos making segmentation ineffective
Solution Approach 1:
The patent combines visual features and language features into a unified segmentation framework. The segmentation module integrates visual segmentations from the visual feature module and language segmentations from the language feature module to produce final video segmentations. This merging of multiple feature types compensates for the weakness of visual-only methods in livestream videos where visual changes are unclear.
2Measurement precision
If manual segmentation is used to overcome unclear visual changes, then segmentation accuracy can be improved, but the process becomes time consuming
Solution Approach 1:
The system performs automatic segmentation by processing visual features and language features through trained modules without requiring manual annotation. The segmentation module automatically integrates results from both feature types to produce segmentations, eliminating the need for time-consuming manual segmentation while maintaining accuracy.
Solution Approach 2:
The visual feature module and language feature module are trained in advance using training videos and their corresponding transcripts. This preliminary training enables the modules to automatically extract meaningful features and perform segmentation without manual intervention during actual video processing, significantly reducing segmentation time.
3Measurement precision
If a combination of visual and language features is used, then segmentation quality improves, but system complexity increases
Solution Approach 1:
The system is divided into distinct functional modules: a visual feature module for processing visual data, a language feature module for processing transcript data, and a segmentation module for integrating results. This modular segmentation allows each component to specialize in specific tasks while working together to achieve high segmentation quality without overwhelming complexity.
4Productivity
If automated segmentation systems are developed to reduce manual work, then productivity increases, but handling videos with unclear visual changes remains difficult
Solution Approach 1:
The system changes the parameters used for segmentation by incorporating language features (from video transcripts) alongside visual features. For livestream videos where visual changes are unclear, the language features provide alternative cues for identifying segmentation points, maintaining reliability while enabling automated high-speed processing.
Data Source
AI summary
Systems and methods for video segmentation and summarization are described. Embodiments of the present disclosure receive a video and a transcript of the video; generate visual features representing frames of the video using an image encoder; generate language features representing the transcript using a text encoder, wherein the image encoder and the text encoder are trained based on a correlation between training visual features and training language features; and segment the video into a plurality of video segments based on the visual features and the language features.