Automated Product Identification in Videos Using Dual-Modality Embeddings
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Solution Overview
Problem
Existing solutions for automated product identification in video content, such as computer vision and language processing, face challenges in accurately identifying products due to false positives, false negatives, and difficulty in understanding intentionality, especially in user-generated videos with multiple objects or similar products.
Innovation Solution
A method that determines first embeddings from video content and second embeddings from text content associated with the video, producing a product candidate index through nearest neighbor comparison to identify the product featured in the video, without manual user labeling or annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated product identification is performed using computer vision and language processing, then productivity is improved, but measurement precision deteriorates due to false positives and false negatives
Solution Approach 1:
The patent combines computer vision embeddings from video frames with text embeddings from video descriptions and transcripts. By merging these two independent identification pathways, the system cross-validates product candidates, reducing false positives and false negatives while maintaining automated processing speed.
Solution Approach 2:
The system implements feedback by comparing product candidates from visual analysis against those from text analysis. The nearest neighbor search algorithm provides feedback mechanisms that refine identification accuracy by evaluating consistency between multiple detection modalities, allowing the system to correct erroneous identifications.
2Quantity of substance
If multiple objects are detected in video frames, then completeness of detection is improved, but measurement precision deteriorates due to difficulty in identifying the intended product
Solution Approach 1:
Text content from video descriptions and transcripts serves as an intermediary that disambiguates which detected object is the intended product. The system uses text embeddings to filter and prioritize product candidates among multiple detected objects, resolving intent without requiring manual annotation.
3Quantity of substance
If similar products are present in the video, then completeness of product detection is improved, but measurement precision deteriorates due to difficulty in distinguishing between them
Solution Approach 1:
The system merges visual features from computer vision with semantic information from text processing. This combination provides distinguishing characteristics that enable the nearest neighbor search to differentiate between similar products, maintaining high precision even when multiple similar products are present.
4Ease of operation
If manual user labeling or annotation is avoided, then ease of operation is improved, but measurement precision deteriorates due to lack of ground truth data
Solution Approach 1:
The system performs self-service by automatically generating product identifications through the integration of computer vision and text processing. The dual-modality approach creates self-validation mechanisms that compensate for the absence of manual labeling, maintaining accuracy through cross-referencing multiple independent detection pathways.
Data Source
AI summary
Automated product identification within hosted and streamed videos is performed based on video content of a video received at an online video platform and text content associated with the video. First embeddings representative of one or more first candidate products are determined based on video content of the video, such as one or more frames selected from within the video. Second embeddings representative of one or more second candidate products are determined based on text content associated with the video, such as a title, description, or transcript of the video. A product candidate index is produced based on the second embeddings. A product identification representative of a product featured in the video is determined based on a comparison of the first embeddings against entries of the product candidate index, such as including by a nearest neighbor search responsive to the comparison. An indication of the product identification is then output at the online video platform.