Video Summarization Using Contextualized Frame Representations
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Solution Overview
Problem
Conventional video summarization systems face limitations due to small training datasets, leading to overfitting and inaccurate predictions, particularly in classifying video frames as summary or non-summary moments independently, resulting in repetitive categorization.
Innovation Solution
A video processing system generates continuous feature representations conditioned on previous frames, incorporating a language signal from transcripts to improve accuracy, using an autoregressive process and transformer models for contextual understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If binary classifier models are used to classify each video frame independently, then the classification process is simple and fast, but the accuracy deteriorates due to overfitting on small datasets and repetitive categorization of summary moments
Solution Approach 1:
The system uses an autoregressive transformer model that incorporates feedback from previously classified frames when classifying the current frame. The model attends to the sequence of video frames and previous classification results, allowing it to learn contextual dependencies and avoid repetitive categorization while maintaining efficient processing through parallel computation capabilities of the transformer architecture.
Solution Approach 2:
The patent introduces an intermediate autoregressive transformer model that acts as a mediator between the input video frames and the final binary classification output. This intermediate model generates continuous feature representations that capture temporal dependencies and contextual information, which then inform the final summary moment classification decisions, thereby improving accuracy without sacrificing processing efficiency.
2Loss of time
If small training datasets are used for model training, then the training process is fast and resource-efficient, but the model reliability deteriorates due to overfitting and inability to generalize to new videos
Solution Approach 1:
The system changes the parameter dimensionality by using an autoregressive transformer model that generates continuous feature representations in a high-dimensional space, capturing more nuanced patterns from limited training data. This parameter transformation allows the model to learn more robust features that generalize better to unseen videos while still training efficiently on small datasets.
Solution Approach 2:
The patent transitions from traditional frame-by-frame binary classification to an autoregressive approach that operates in the temporal dimension, considering the sequence and context of multiple frames. This dimensional shift allows the model to leverage temporal dependencies and contextual information, improving reliability and generalization ability without requiring extensive training data or prolonged training time.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for video summarization include obtaining a video, generating a sequence of contextualized visual representations corresponding to portions of the video, generating a sequence of summary visual representations corresponding to a subset of the portions of the video based on the sequence of contextualized visual representations, and generating a summary video including the subset of the portions of the video based on the sequence of summary visual representations.