Vision-Language Classifier Text Embedding Selection for Low-Sample Accuracy
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Solution Overview
Problem
Existing vision-language models struggle with optimizing classification tasks requiring complex understanding of visual and textual data, particularly in scenarios with limited training samples.
Innovation Solution
A method to optimize vision-language classifiers by tuning a visual-language model using pre-trained encoders for image and textual embeddings, iteratively adjusting textual sentences to minimize classification loss, and determining an optimal bias for improved classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing vision-language models are used for classification tasks, then the models can process visual and textual data, but the classification accuracy is insufficient in scenarios with limited training samples
Solution Approach 1:
The patent applies preliminary action by using pre-trained encoders (image encoder and textual encoder) that have been trained on large datasets beforehand. These pre-trained encoders provide optimized embeddings that can be directly applied to classification tasks with limited training samples, eliminating the need for extensive retraining and enabling high accuracy with minimal data.
2Reliability
If the vision-language model uses standard training procedures, then the model can be trained, but the training process is complex and requires extensive computational resources
Solution Approach 1:
The patent extracts and utilizes pre-trained encoders from existing vision-language models, separating the embedding generation function from the classification function. This allows the classification task to be performed independently using only the pre-trained encoders and minimal training data, significantly simplifying the training process while maintaining high classification performance.
3Measurement precision
If the model processes all textual sentences from the superset, then comprehensive coverage is achieved, but the computational overhead and training time increase significantly
Solution Approach 1:
The patent applies partial action by selecting only a subset of textual sentences from the superset that are most relevant to the classification task. Instead of processing all possible sentences, the method identifies and uses the most informative sentences, reducing computational overhead and training time while maintaining or improving classification accuracy.
Data Source
AI summary
Herein, a technology that facilitates the optimization of vision-language (VL) based classifiers with text embeddings is discussed. The technology includes tuning the VL-based classifier employing a pre-trained image encoder of a visual-language model (VLM) for imaging embedding of pre-classified images and a pre-trained textual encoder of the VLM for textual embedding of a set of differing textual sentences. The technology further includes determining an optimized set of differing textual sentences of a superset of textual sentences. The optimized set of differing textual sentences has a minimal classification loss of the VL-based classifier when classifying the pre-classified images.