Two-Stage Recall Model Training for Accurate Multimedia Retrieval
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Solution Overview
Problem
Existing multimedia recall systems suffer from low accuracy, leading to multiple user interactions and increased time consumption in querying multimedia content.
Innovation Solution
A method for recall model training that involves pre-training a recall model using first text pairs, where each pair consists of a question text generated from multimedia description information and an answer text being a resource identifier. This is followed by fine-tune training using second text pairs that include related multimedia resource identifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional multimedia recall systems are used, then the system structure is simple, but the recall accuracy is low
Solution Approach 1:
The training process is segmented into two distinct stages: pre-training using first text pairs (question-text and answer-text) and fine-tune training using second text pairs (question-text and related multimedia resource identifier). This segmentation allows the model to first learn general multimedia-recall patterns and then specialize in accurate related multimedia prediction, resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The pre-training stage performs preliminary learning using first text pairs before the fine-tune training stage. This preliminary action establishes the model's foundational ability to understand multimedia relationships, enabling subsequent fine-tune training to achieve high recall accuracy without requiring the entire training process to be as complex.
2Loss of time
If recall accuracy is improved through better training, then user query time is reduced, but training time and computational resources increase
Solution Approach 1:
Training is segmented into pre-training and fine-tune training phases. The pre-training phase uses first text pairs to establish basic recall capabilities, while the fine-tune phase uses second text pairs to optimize accuracy. This segmentation allows the model to achieve high recall accuracy efficiently, reducing user query time without requiring excessively long training duration.
Solution Approach 2:
The pre-training stage performs a partial training action using first text pairs, which is sufficient to establish basic functionality. The subsequent fine-tune training then performs a targeted action using second text pairs to optimize performance. This partial action approach allows the model to achieve high accuracy without requiring excessive training time.
3Measurement precision
If the recall model uses detailed description information, then recall accuracy improves, but data processing complexity increases
Solution Approach 1:
The method extracts key information from detailed multimedia description information to create first text pairs and second text pairs. By taking out only the essential features needed for accurate recall and representing them in text format, the system achieves high recall accuracy while simplifying the data processing complexity compared to processing all detailed multimedia information.
Solution Approach 2:
Text pairs serve as an intermediary representation between detailed multimedia description information and the recall model. This intermediary format allows the model to process and learn from detailed information in a simplified manner, achieving high accuracy without directly processing complex multimedia data structures.
Data Source
AI summary
A method for recall model training includes: obtaining first text pairs with a first text pair including a first question text, generated based on description information of multimedia and using a resource identifier of the multimedia as a question target, and a first answer text, being a resource identifier targeted by a question of the first question text; pre-training a recall model; obtaining second text pairs with a second text pair including a second question text that uses a resource identifier of related multimedia as a question target, and a second answer text being a resource identifier of the related multimedia targeted by a question of the second question text, and the related multimedia involved in the second text pair corresponding to the multimedia involved in the first text pair; and performing fine tune training on the pre-trained recall model based on second question texts and second answer texts.