Live Comment Generation via Video-Text Matching
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Solution Overview
Problem
Traditional video comments lack time information, making them unable to be embedded in videos and displayed in real-time, resulting in less engagement and attraction for users compared to live comments.
Innovation Solution
A machine learning model is proposed to generate live comments by matching candidate texts with video segments, using a multi-modality cross-matching layer to fuse feature information from various modalities and determine suitable comments for display during video playback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional video comments are used, then comments can be provided for videos, but they lack time information and cannot be displayed in real-time, reducing user engagement
Solution Approach 1:
The patent segments video content into multiple video segments with associated time information. Each video segment is matched with corresponding candidate texts to generate live comments that are synchronized with specific time points in the video playback, thereby restoring time information to comments and enabling real-time display.
Solution Approach 2:
The patent performs preliminary matching between video segments and candidate texts before video playback. The machine learning model pre-processes and matches candidate texts with corresponding video segments, storing the results for efficient retrieval and display during real-time playback, thus preparing time-synchronized comments in advance.
2Measurement precision
If a machine learning model with multi-modality cross-matching is used, then relevant live comments can be generated, but the system complexity increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component that bridges video segments and candidate texts. This model performs multi-modality cross-matching by extracting features from both video segments and candidate texts, calculating similarity scores, and ranking matches to generate accurate live comments despite the increased system complexity.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based matching methods with a machine learning-based semantic matching system. The model uses neural networks to perform feature extraction and similarity calculation, substituting complex mechanical processing with intelligent algorithms that achieve higher matching accuracy.
3Measurement precision
If candidate texts are matched with video segments in real-time, then relevant comments can be generated, but processing time increases
Solution Approach 1:
The patent performs the computationally intensive matching process in advance before video playback. The machine learning model pre-matches video segments with candidate texts, calculates similarity scores, and stores the results. During playback, pre-generated live comments are simply retrieved and displayed based on current playback time, dramatically reducing processing time while maintaining high comment relevance.
Solution Approach 2:
The patent updates live comments periodically based on video playback progress rather than continuously processing in real-time. The system retrieves pre-matched comments at regular intervals corresponding to video segment boundaries, reducing processing frequency while ensuring comments remain relevant to the current playback state.
Data Source
AI summary
The present disclosure provides a technical solution of live comments generating, which may acquire candidate texts highly similar with segments of video as live comments of corresponding segments by matching the candidate texts with the segments, and further generate new live comments based on video segments and existed live comments to enrich the live comments information of related video.