Adaptive Thresholding for Video Retrieval Accuracy
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Solution Overview
Problem
Existing video analysis systems face challenges in accurately retrieving relevant videos due to discrepancies in the accuracy of machine-learned video retrieval models, particularly when dealing with queries of varying content and complexity.
Innovation Solution
The implementation of a machine-learned adaptive thresholding model that dynamically adjusts the filtering threshold for video retrieval based on the content and complexity of user queries, thereby improving the accuracy of relevance analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a static threshold value is used for video retrieval filtering, then the system operation is simple, but the retrieval accuracy deteriorates when dealing with queries of varying content and complexity
Solution Approach 1:
The patent applies dynamics by transitioning from a static threshold to a dynamic threshold that adapts based on query characteristics. The system analyzes query content and complexity to determine appropriate threshold values, allowing the filtering criterion to change according to the specific retrieval task rather than remaining fixed.
Solution Approach 2:
The patent implements parameter changes by modifying the threshold parameter based on query attributes. Different threshold values are selected according to the content type and complexity level of the query, enabling the system to optimize retrieval accuracy for diverse query scenarios through parameter adjustment.
2Adaptability or versatility
If a machine-learned video retrieval model is applied, then the system can handle varied query content, but discrepancies in accuracy occur due to model limitations
Solution Approach 1:
The patent introduces an intermediary component that acts as a bridge between the machine-learned retrieval model and the final results. This intermediary layer applies additional filtering using dynamically determined thresholds to correct and refine the model's output, thereby improving reliability while maintaining the model's versatility.
Solution Approach 2:
The system implements feedback by using query characteristics to inform threshold selection. The analysis of query content and complexity feeds back into the threshold determination process, creating a closed-loop system that continuously adapts to maintain consistent accuracy across different query types.
3Measurement precision
If the video retrieval model is updated to account for query content and complexity, then the retrieval accuracy improves, but the device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex retrieval process into distinct stages: query analysis, threshold determination based on query characteristics, model-based retrieval, and final filtering. This segmentation allows each component to be optimized independently while maintaining overall accuracy.
Solution Approach 2:
The system performs preliminary action by analyzing query content and determining appropriate thresholds before executing the main retrieval operation. This preparatory step ensures that the retrieval model is configured optimally for the specific query type, improving accuracy without requiring complex real-time adjustments during retrieval.
Data Source
AI summary
A video analysis system trains and uses a machine-learned adaptive thresholding model configured to receive a query (e.g., text, image, video), and generate a predicted threshold value for a corresponding video retrieval model that indicates what value the filtering threshold should be for the query. The video analysis system filters video segments for the query that are associated with relevance scores above the predicted threshold generated by the adaptive thresholding model. In one instance, the adaptive thresholding model is configured as a machine-learned model, including neural networks, embedding models, transformer-based architectures, and the like that are capable of generating a predicted threshold value given a query that includes, for example, text, images, videos, audio, or any other appropriate data modality.