Contextual Video Summarization Using Query-Based Partial Attention
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Solution Overview
Problem
Existing video management systems struggle to efficiently summarize lengthy videos into shorter versions while preserving contextual meaning and relevance to user queries, leading to increased resource utilization and difficulty in navigating large video databases.
Innovation Solution
A system that identifies relevant frames and captions in a video based on a user query, stitches them together, generates an abstractive text summary, and synchronizes it with audio to create a summarized video, automating the process to maintain contextual integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional video management systems are used to store and retrieve videos, then video database capacity increases, but video navigation efficiency decreases and resource utilization increases
Solution Approach 1:
The system segments lengthy videos into discrete frames and generates captions for each frame. This segmentation enables selective processing and retrieval of only relevant video portions based on user queries, rather than navigating entire videos. The video is divided into frame-caption units that can be independently searched and assembled.
Solution Approach 2:
The system introduces captions as an intermediary layer between the video content and user queries. These captions serve as searchable text representations that bridge the gap between visual content and user intent, enabling efficient query-based retrieval without requiring direct video scanning.
2Length of moving object
If traditional video summarization methods are used, then video length is reduced, but contextual meaning is lost
Solution Approach 1:
The system performs preliminary actions by generating captions for all video frames in advance and storing them with their corresponding frames. This pre-processing enables the system to quickly assemble contextually accurate summaries by selecting and stitching relevant pre-captioned frames, rather than attempting to summarize after the fact.
Solution Approach 2:
Instead of processing entire videos, the system applies partial action by selectively processing only the frames and captions that are relevant to the user's query. The attention mechanism identifies and processes a subset of frames that contain the necessary contextual information, reducing processing while maintaining meaning.
3Reliability
If manual video summarization is performed, then contextual accuracy is maintained, but automation level decreases and resource utilization increases
Solution Approach 1:
The system employs an attention mechanism that provides feedback loops between user queries, frame relevance scoring, and frame selection. The attention mechanism continuously adjusts which frames to select based on how well they match the query intent, enabling automated yet contextually accurate summarization through iterative refinement.
Solution Approach 2:
The system performs self-service by automatically generating summaries without human intervention. The attention mechanism autonomously identifies relevant frames, stitches them together, and generates captions based on user queries, eliminating the need for manual video analysis while maintaining contextual accuracy.
Data Source
AI summary
An embodiment for query-based synthesis of a contextual video frame summary through partial attention is provided. The embodiment may include receiving a video from a repository and a query from a user. The embodiment may also include obtaining one or more captions for the video. The embodiment may further include identifying one or more relevant frames in the video and one or more relevant captions. The embodiment may also include in response to determining the one or more relevant frames and the one or more relevant captions are capable of being stitched together, stitching the one or more relevant frames and the one or more relevant captions together. The embodiment may further include generating an abstractive text summary of the stitched one or more relevant captions. The embodiment may also include converting the abstractive text summary into audio. The embodiment may further include generating a new summarized video.