Image-Text Matching Training With Contrastive Positive-Negative Samples
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Solution Overview
Problem
The challenge in training image-text matching models is the scarcity of training samples due to the manual generation of text descriptions, leading to poor training effectiveness.
Innovation Solution
The method involves acquiring positive and negative samples through contrastive learning, where positive samples consist of text describing image content and negative samples consist of text inconsistent with image content, allowing for increased sample generation and improved training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual text descriptions are used to obtain training samples, then the accuracy of text-image matching is improved, but the quantity of training samples decreases
Solution Approach 1:
The patent uses pre-trained language models to automatically generate text descriptions for images, creating copies of training data without manual intervention. This allows大量 training samples to be generated efficiently while maintaining reasonable quality through the use of advanced language generation capabilities
Solution Approach 2:
The system employs self-supervised learning where the model learns from the data itself without requiring external manual annotation. The pre-trained language model generates its own training data from image inputs, enabling the system to serve itself in creating training samples
2Productivity
If the number of training samples is increased through automatic generation, then the training effect is improved, but the quality control of text descriptions becomes more difficult
Solution Approach 1:
The patent applies pre-training of language models on large corpora before using them for image captioning. This preliminary action ensures that the model has already learned high-quality language patterns and descriptions, which then transfer to the image-text matching task, maintaining description quality while enabling automatic generation
Solution Approach 2:
The system uses contrastive learning with positive and negative samples where the model receives feedback on its predictions. The loss function computes gradients based on the difference between predicted and actual labels, allowing the model to iteratively improve its text generation quality while maintaining high productivity
Data Source
AI summary
The present disclosure provides a training method and apparatus for an image-text matching model, a device and a storage medium. The method includes: acquiring a positive sample and a negative sample; where the positive sample includes text and an image, the text in the positive sample is used to describe content of the image in the positive sample; the negative sample includes text and an image, the text in the negative sample describes content that is inconsistent with content of the image in the negative sample; training the image-text matching model by using the acquired positive sample and the acquired negative sample based on a manner of contrastive learning; where the image-text matching model is used to predict, for an input image and input text, whether the input text is used to describe content of the input image.