Localized Video Annotation via Multi-Modal Semantic Segmentation
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Solution Overview
Problem
Existing video distribution services lack the ability to provide localized contextual information for video segments, leading to unsuitable advertisements being displayed based on the viewer's emotional state, as global metadata does not account for scene-level context.
Innovation Solution
A system that segments videos into meaningful units, processes multiple input modalities such as video frames, audio signals, and textual information, and applies AI models to classify and annotate segments with semantic contextual information, allowing for personalized viewing experiences and contextually relevant advertisement placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If global metadata is used to annotate video content, then comprehensive video information is provided, but temporal information and scene-level context are lost
Solution Approach 1:
The video is divided into discrete segments (scenes, shots, or temporal units) and each segment is annotated independently with semantic labels. This segmentation approach preserves temporal information by creating granular annotations for each video segment rather than applying a single global annotation to the entire video, thus resolving the contradiction between information completeness and annotation complexity.
2Ease of operation
If advertisements are placed in video segments without considering contextual information, then advertisement insertion is simple, but advertisement suitability for viewer emotional state deteriorates
Solution Approach 1:
Semantic annotations are generated for video segments in advance, capturing contextual information about each segment before advertisement insertion. This preliminary annotation process enables the system to later match advertisements with appropriate contextual segments, improving advertisement suitability while maintaining operational simplicity through automated matching algorithms.
3Measurement precision
If multiple input modalities are processed for segment classification, then classification accuracy improves, but processing complexity increases
Solution Approach 1:
Multiple input modalities (visual features, audio features, textual information) are merged and processed together through a unified classification framework. This merging approach enables the system to leverage complementary information from different modalities to improve classification accuracy while managing processing complexity through integrated multi-modal processing architectures.
Data Source
AI summary
Embodiments described herein provide a system for localized contextual video annotation. During operation, the system can segment a video into a plurality of segments based on a segmentation unit and parse a respective segment for generating multiple input modalities for the segment. A respective input modality can indicate a form of content in the segment. The system can then classify the segment into a set of semantic classes based on the input modalities and determine an annotation for the segment based on the set of semantic classes.