Video Topic Segmentation Using Sliding Windows and Cross-Modal Attention
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Solution Overview
Problem
Existing video segmentation systems struggle with inaccuracies in segmenting long-form videos, often missing contextual information by focusing solely on visual features and failing to integrate textual cues effectively.
Innovation Solution
A video segmentation system that utilizes a sliding window to divide digital videos and transcripts into overlapping segments, employing cross-modal attention models trained with intra-modal and cross-modal contrastive losses to accurately segment long-form videos by integrating textual and visual features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing video segmentation systems focus solely on visual features, then the system complexity remains low, but the segmentation accuracy deteriorates due to missing contextual information
Solution Approach 1:
The patent combines visual features and textual features into a unified segmentation framework. The cross-modal attention mechanism integrates information from both video frames and transcript text, allowing the system to leverage complementary information from multiple modalities to improve segmentation accuracy while managing complexity through structured fusion.
Solution Approach 2:
The patent introduces a cross-modal attention mechanism as an intermediary component that bridges visual and textual modalities. This attention mechanism selectively weights and integrates information from both sources, enabling accurate segmentation without directly combining all possible features, thus managing system complexity.
2Loss of information
If the system processes long-form videos with subtle visual changes, then the contextual information capture improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent divides long-form videos into smaller segments and processes them individually through the sliding window mechanism. This segmentation allows the system to manage computational complexity by handling manageable portions of video at a time while maintaining contextual awareness through overlapping windows that capture subtle transitions.
Solution Approach 2:
The patent adds a temporal dimension to the analysis by using sliding windows that move through the video sequence. This allows the system to detect subtle visual changes over time by comparing adjacent segments, effectively capturing contextual information that would be missed in static frame analysis.
3Loss of information
If the sliding window divides videos into overlapping segments, then the contextual information is preserved, but the processing time increases
Solution Approach 1:
The patent performs preliminary processing of video segments in parallel, preparing feature representations before the final segmentation decision is made. This allows overlapping segments to be processed efficiently by reusing computations from previously processed regions, reducing redundant processing time while maintaining contextual information.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for segmenting digital videos into topic chapters utilizing a sliding window and video segmentation models. Specifically, the disclosed systems utilize a sliding window to divide a digital video into overlapping segments, each segment including a subset of sentences of a transcript of the video and corresponding video frames for a given time window of the digital video. Further, the disclosed systems generate, for each overlapping segment, topic-boundary label predictions for the subset of sentences. Specifically, the disclosed systems generate text representations for the sentences using a text encoder and frame representations for the corresponding video frames using a frame encoder. Moreover, the disclosed systems generate the topic-boundary label predictions based on the text representations and the frame representations. Additionally, the disclosed systems generate a topic-boundary label for each sentence of the transcript based on the topic-boundary label predictions.