Multimodal Video Summarization With Alignment-Guided Self-Attention
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Solution Overview
Problem
Conventional video summarization systems struggle with aligning and fusing video and text modalities effectively, requiring large amounts of annotated data and inefficiently consuming computing resources, especially in the context of livestream videos with static backgrounds and redundant information.
Innovation Solution
A multimodal video summarization system using an alignment-guided self-attention module and dual contrastive learning to align video and text modalities, reducing the need for extensive training data and improving computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional video summarization systems are used to align and fuse video and text modalities, then the system can produce video summaries, but the system requires large amounts of annotated data and consumes excessive computing resources
Solution Approach 1:
The patent introduces an alignment-guided self-attention module as an intermediary mechanism that learns temporal correspondences between video and text modalities. This module acts as a mediator that aligns features from different modalities without requiring extensive annotated data, using self-attention mechanisms to identify and fuse relevant temporal segments automatically
Solution Approach 2:
The system employs self-supervised learning through contrastive losses that enable the model to learn from unannotated data. The alignment-guided self-attention mechanism serves itself by automatically identifying temporal alignments and generating training signals without human annotation, reducing dependency on large annotated datasets
2Reliability
If conventional video summarization systems are used to align and fuse video and text modalities, then the system can produce video summaries, but the system consumes excessive computing resources
Solution Approach 1:
The patent extracts and focuses computational resources only on temporally aligned segments between video and text modalities using the alignment-guided self-attention module. Instead of processing entire video sequences and text transcripts uniformly, the system identifies and processes only the relevant temporal correspondences, significantly reducing computational overhead while maintaining alignment accuracy
Solution Approach 2:
The system segments the video and text inputs into temporally aligned segments using self-attention mechanisms. By dividing the processing into discrete temporal segments rather than processing the entire input uniformly, the system reduces computational complexity and resource consumption while preserving the alignment relationships between modalities
3Ease of manufacture
If livestream videos are stored without significant editing, then the videos can be captured and stored easily, but the videos become long and contain irrelevant or redundant information
Solution Approach 1:
The patent applies partial action by selecting and summarizing only the most relevant segments of livestream videos using the alignment-guided self-attention module. Instead of processing or retaining the entire lengthy video, the system identifies and processes only the temporally aligned key segments that contain meaningful information, filtering out redundant content while maintaining ease of video capture
Data Source
AI summary
A method includes receiving a video input and a text transcription of the video input. The video input includes a plurality of frames and the text transcription includes a plurality of sentences. The method further includes determining, by a multimodal summarization model, a subset of key frames of the plurality of frames and a subset of key sentences of the plurality of sentences. The method further includes providing a summary of the video input and a summary of the text transcription based on the subset of key frames and the subset of key sentences.