Multimodal Video Segment Scoring for Accurate Highlight Recognition
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Solution Overview
Problem
Existing video processing technologies struggle to accurately extract highlight segments, often misidentifying segments that are significantly different from the video content, leading to poor user experience.
Innovation Solution
A method that divides videos into segments and extracts multi-modal features using audio, image, and structured features, which are then fused and scored to identify highlight segments accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single-modal video processing is used, then the processing speed is fast, but the accuracy of highlight segment recognition is low
Solution Approach 1:
The video processing system is segmented into multiple independent feature extraction modules (audio, video, text) that process different modalities separately, then combine results. This allows comprehensive multi-modal analysis without overwhelming system complexity, resolving the contradiction between recognition accuracy and system complexity.
Solution Approach 2:
The patent combines multiple feature types (audio features, video features, text features) into a composite multi-modal feature representation. This composite approach enables more accurate highlight segment recognition by leveraging complementary information from different modalities, while the modular architecture keeps processing complexity manageable.
2Measurement precision
If comprehensive multi-modal feature extraction is performed, then the recognition accuracy improves, but the computational cost increases
Solution Approach 1:
The computational workload is segmented across three specialized feature extraction models (audio, video, text) that operate in parallel. This distribution allows efficient utilization of computational resources while maintaining comprehensive multi-modal analysis for accurate video segment scoring.
Solution Approach 2:
The system performs preliminary feature extraction on audio, video, and text content separately before combining them for final highlight segment identification. This preliminary processing organizes data in advance, reducing the computational burden during the final scoring and recognition phase.
3Measurement precision
If the entire video is processed to identify highlights, then the completeness of highlight detection is high, but the processing time is long
Solution Approach 1:
The video is automatically segmented into multiple segments, and the system processes each segment independently using multi-modal features. This segmentation enables comprehensive highlight detection across the entire video while reducing overall processing time through parallel processing of divided segments.
Solution Approach 2:
The system extracts key features (audio, video, text) that are most relevant to highlight identification, focusing computational effort on the most informative aspects of each video segment rather than processing all possible video data equally, thus achieving complete highlight detection with reduced processing time.
Data Source
AI summary
A video processing method and apparatus is provided, the video processing method includes: dividing a received initial video into at least one video segment; obtaining, based on a feature extraction model, a first modal feature, a second modal feature, and a third modal feature that correspond to each video segment in the at least one video segment; and inputting, into a recognition model, the first modal feature, the second modal feature, and the third modal feature that correspond to each video segment, to obtain a video score corresponding to each video segment, and determining a target video segment in the initial video based on the video score.