Multimodal Video Highlight Extraction With Explainable Frame Scoring
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Solution Overview
Problem
Conventional video editing workflows are tedious and challenging for users, particularly in identifying and trimming moments of interest, due to the multimodal nature of videos, subjective interpretation of what is interesting, lack of extensibility to new modalities, and lack of explainability in existing machine learning models.
Innovation Solution
Utilizing independent and orthogonal machine learning models to extract different types of features across various modalities, assigning importance scores to each frame, and combining these scores to identify and visualize moments of interest, with customizable parameters and explainable outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional video editing workflows are used to identify and trim moments of interest, then users can manually review and select interesting content, but the process becomes tedious and challenging due to the multimodal nature of videos
Solution Approach 1:
The system automatically analyzes videos by extracting features from multiple modalities (visual, audio, textual) and generates importance scores for each frame without requiring manual user intervention. The automated moment detection and trimming process eliminates the need for users to manually review entire videos, directly addressing the contradiction between ease of operation and time loss.
Solution Approach 2:
Manual mechanical video review and selection processes are replaced with automated machine learning-based feature extraction and scoring systems. The system substitutes human manual inspection with computational analysis that processes multiple video modalities simultaneously, reducing time requirements while maintaining identification accuracy.
2Extent of automation
If existing machine learning models are used to detect moments of interest, then automation can be achieved, but the models lack explainability making it difficult to understand why certain moments are selected
Solution Approach 1:
The system segments the video analysis process into distinct modular components: visual feature extraction, audio feature extraction, textual feature extraction, and importance scoring. Each segment operates independently and contributes to the final automated decision, making the automation process transparent and explainable through modular architecture.
Solution Approach 2:
The system introduces intermediate representation layers that translate complex multimodal data into interpretable importance scores for each frame. These intermediate scores serve as mediators between the raw multimodal inputs and the final automated moment selection, providing explainability by showing which frames were deemed most important and why.
3Device complexity
If a fixed machine learning model architecture is used, then implementation is simplified, but the system lacks extensibility to accommodate new modalities
Solution Approach 1:
The system implements a universal feature extraction framework that can accommodate multiple modalities (visual, audio, textual) through a common architectural structure. The standardized interface for feature extraction and importance scoring allows new modalities to be integrated without redesigning the core system, maintaining simplicity while achieving extensibility.
Solution Approach 2:
The system employs a dynamic and flexible architecture where the set of active modalities and their corresponding feature extractors can be configured and adjusted. This dynamic configuration capability allows the system to adapt to new modalities and data types while maintaining the same core processing framework, balancing implementation simplicity with future extensibility.
4Measurement precision
If multiple machine learning models are used to extract features from different modalities, then comprehensive moment detection is achieved, but the computational complexity and processing time increase
Solution Approach 1:
The system applies partial action by processing videos through a prioritized sequence of feature extraction operations. It extracts features from the most relevant modalities first and can dynamically adjust the depth and scope of analysis based on video characteristics and user needs, achieving comprehensive detection when necessary while reducing computational effort when sufficient precision can be obtained with lighter processing.
Data Source
AI summary
Embodiments of the present invention provide systems, methods, and computer storage media for extracting moments of interest (e.g., video frames, video segments) from a video. In an example embodiment, independent and/or orthogonal machine learning models are used to extract different types of features considering different modalities, and each frame in the video is assigned an importance score for each model. The importance scores for each model are combined into an aggregated importance score for each frame in the video. Depending on the embodiment, the aggregated importance scores are used to visualize the score per frame, identify moments of interest, automatically crop down the video into a highlight reel, browse or visualize the moments of interest within the video, and/or search across multiple videos.