Video Topic Chaptering With Cross-Modal Attention Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video segmentation systems inaccurately generate topic chapters for digital videos due to the lack of integration of contextual information from both textual and visual cues.
Innovation Solution
A video segmentation system that utilizes a multi-modal segmentation model combining a text encoder, frame encoder, and cross-modal attention model to generate text-aware visual representations, determining topic-boundary labels by integrating textual and visual features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing video segmentation systems are used, then topic chapters can be generated, but the accuracy is insufficient due to lack of integration of contextual information
Solution Approach 1:
The patent merges multiple information sources including visual features from video frames, textual features from transcripts, and audio features into a unified multi-modal representation. This combination allows the system to leverage complementary information from different modalities, thereby improving topic segmentation accuracy while reducing information loss that would occur with single-modality approaches
Solution Approach 2:
The system creates a composite representation by integrating features from multiple modalities (visual, textual, audio) into a unified feature space. This composite approach allows the model to capture complex contextual relationships that cannot be detected by individual modalities alone, directly addressing the accuracy limitation of existing systems
2Measurement precision
If multi-modal segmentation model is used, then accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex multi-modal processing task into distinct modular components: a visual feature extractor for video frames, a textual feature extractor for transcripts, an audio feature extractor, and a fusion model. Each component independently processes its designated modality, which simplifies the overall system architecture while maintaining the benefits of multi-modal integration and improving computational efficiency
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for segmenting digital videos into topic chapters. In particular, in some embodiments, the disclosed systems generate, utilizing a text encoder, a text representation for a transcript sentence of a video transcript. In addition, in some embodiments, the disclosed systems generate, utilizing a frame encoder, a set of frame representations for a set of video frames associated with the transcript sentence. Moreover, in some embodiments, the disclosed systems generate, utilizing a cross-modal attention model, a text-aware visual representation from the text representation and the set of frame representations. Furthermore, in some embodiments, the disclosed systems determine a topic-boundary label for the transcript sentence from the text representation and the text-aware visual representation.