Video Moment Extraction Using Multimodal Importance Scoring
Find Innovative SolutionsGenerate Solutions
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 interest, lack of extensibility to new modalities, and unexplainable black-box machine learning models.
Innovation Solution
Utilizing independent and orthogonal machine learning models to extract features across different modalities, assign importance scores to video frames, and combine them into aggregated scores to identify and customize moments of interest, providing explainability and extensibility.
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 segments the video analysis task into multiple independent modality analyses (visual, audio, textual, etc.), where each modality is processed by dedicated machine learning models. This segmentation allows parallel processing of different video components, reducing overall analysis time while maintaining comprehensive coverage of the multimodal content.
Solution Approach 2:
The manual mechanical process of reviewing and selecting video moments is replaced with automated machine learning models that analyze video frames, audio tracks, and other modalities to automatically identify moments of interest. This substitution eliminates tedious manual review while accurately capturing the essence of interesting content across multiple modalities.
2Productivity
If machine learning models are used to automatically identify moments of interest, then efficiency is improved, but the models become black-box systems that lack explainability
Solution Approach 1:
The system introduces an intermediary explanation layer between the machine learning models and the final output. This intermediary component generates human-readable explanations that bridge the black-box model decisions with understandable results, allowing users to comprehend why specific moments were selected without sacrificing the automated efficiency of the underlying models.
3Reliability
If fixed machine learning models are used for video analysis, then the system has a确定的 workflow, but it lacks extensibility to accommodate new modalities
Solution Approach 1:
The system employs a dynamic architecture where the set of active machine learning models and analyzed modalities can be flexibly configured based on specific video content and user needs. This dynamic design allows the system to adaptively incorporate new modalities (such as new sensor types or data formats) without disrupting the core analysis workflow, maintaining reliability while enabling versatility.
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.