Personalized Video Summarization via Attention Coding
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Solution Overview
Problem
Existing video summarization methods are inefficient and costly, as they rely on manual browsing and preset rules, failing to cater to individual user preferences, resulting in low video viewing rates.
Innovation Solution
A video summarization method that uses self-attention calculation models to generate user-specific attention coding parameters based on behavior data, identifying interest frames from clips to create personalized video summaries that align with user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual browsing and preset rules are used for video summarization, then the implementation is simple, but the efficiency is low and cost is high
Solution Approach 1:
The system automatically generates video summaries by utilizing user behavior data and self-attention calculation models to identify interest frames, eliminating the need for manual browsing and rule-based processing. The model autonomously learns user preferences and generates personalized summaries without human intervention.
Solution Approach 2:
The patent replaces manual mechanical operations (browsing, frame selection) with an automated deep learning system using self-attention calculation models. The mechanical process of manual video analysis is substituted with computational algorithms that automatically process video frames and user behavior data.
2Ease of operation
If preset rules based on common preferences are used, then the implementation is straightforward, but the content does not match individual user interests
Solution Approach 1:
The system generates different video summaries tailored to each user's specific preferences by processing individual user behavior data through the self-attention model. Each user receives a customized summary with locally optimized content selection based on their unique interests, rather than a uniform summary for all users.
Solution Approach 2:
The video summarization system dynamically adapts to each user's changing preferences by continuously learning from user behavior data. The content selection and frame weighting are dynamically adjusted based on real-time user interactions, making the summary flexible and responsive to individual user needs.
3Reliability
If manual extraction of key frames is performed, then the process is controllable, but the cost is high and efficiency is low
Solution Approach 1:
The system performs preliminary encoding of user behavior data into attention coding parameters before the actual video summarization process. This pre-processing of user preferences enables the model to quickly identify relevant frames during summarization, reducing the time required for manual-like frame selection while maintaining controllability.
Solution Approach 2:
The patent uses self-attention calculation models to create a computational copy of the manual frame selection process. Instead of actual manual browsing, the system replicates the decision-making process through neural network computations that mimic human preference-based selection, achieving both speed and reliability.
Data Source
AI summary
A video summarization method that includes: obtaining an attention coding parameter of a user based on behavior data of the user; determining, for each clip included in a target video, whether the clip is a clip of interest to the user, based on the attention coding parameter of the user; based on determining that at least one clip included in the target video is a clip of interest, identifying, for each clip of interest included in the target video, at least one interest frame from the clip of interest; and obtaining a video summary of the target video by combining the interest frame from each clip of interest.