Data Augmentation for Vision Language Model Question Alignment
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Solution Overview
Problem
Vision Language Models (VLMs) may output incorrect answers even when they understand the input image, due to challenges in aligning focus or attention with specific questions, beyond general image captioning.
Innovation Solution
A method for data augmentation that involves obtaining candidate descriptions of an image related to a question, determining a target description based on effectiveness metrics, and constructing training samples for a machine learning model comprising the image, question, and target description.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Vision Language Models are trained with general image-caption pairs, then they can understand visual content, but they output incorrect answers when specific question alignment is needed
Solution Approach 1:
The patent segments the training data into multiple types including image-caption pairs, image-question-answer triples, and text-only question-answer triples. This segmentation allows the model to learn different aspects of visual understanding and question alignment separately, resolving the contradiction between general comprehension and specific question alignment capability
Solution Approach 2:
The patent adds a new dimension to the training data by introducing text-only question-answer triples that are aligned with images. This additional dimension enables the model to learn question semantics and alignment strategies independently of visual features, improving answer accuracy for specific questions while maintaining general visual understanding
2Reliability
If more training data is collected to improve model performance, then answer accuracy improves, but data collection and processing time increases
Solution Approach 1:
The patent creates synthetic training data by copying and adapting existing data structures. Image-question-answer triples are constructed by combining existing image-caption pairs with generated or extracted questions and answers. Text-only question-answer triples are generated by extracting question-answer pairs from existing datasets without requiring additional image collection, thus improving model performance without proportional increases in data collection time
Solution Approach 2:
The patent performs preliminary data processing by pre-processing images into standardized formats and pre-extracting question-answer pairs from available datasets before model training. This preliminary action reduces the time required for data preparation during the training process, allowing more time to be allocated to collecting additional diverse training samples
Data Source
AI summary
Embodiments of the present disclosure provide a solution for data augmentation. A method includes: obtaining one or more candidate descriptions of an image with respect to a question associated with the image; determining a target description from the one or more candidate descriptions based on respective effectiveness metrics of the one or more candidate descriptions, an effectiveness metric of a candidate description indicating whether the candidate description is useful in answering the question; and constructing a training sample for a machine learning model, the training sample comprising the image, the question and the target description.