Automated VQA Training Data Generation via Semantic Analysis
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Solution Overview
Problem
Conventional Visual Question Answering (VQA) systems rely on costly and time-consuming manual labeling for generating training data, resulting in limited data and low accuracy.
Innovation Solution
An automated method for generating training data by determining a second question and answer based on semantic analysis of existing data, using semantic relationships to extend the dataset efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to generate training data, then the quality and accuracy of labels can be ensured, but the cost is high and the speed is slow
Solution Approach 1:
The system performs preliminary automated question generation and answer extraction from existing training data before manual labeling. This preliminary action creates a draft dataset that can be quickly reviewed and corrected manually, significantly reducing the time required for manual labeling while maintaining quality through human-in-the-loop verification.
Solution Approach 2:
The system copies existing training data (images, questions, answers) and generates new training samples by creating semantically related questions and answers based on the original data. This copying approach with semantic transformation allows rapid expansion of the dataset without requiring new manual annotations from scratch.
2Measurement precision
If manual labeling is used to generate training data, then the data quality can be maintained, but the cost increases significantly
Solution Approach 1:
The system enables self-service by automatically generating questions and answers from existing training data using semantic analysis and question generation models. This self-service capability reduces dependency on expensive manual annotators while maintaining data quality through automated consistency checks and semantic validation.
Solution Approach 2:
The system changes the parameters of existing training data by generating new questions and answers with different semantic relationships (e.g., paraphrases, related concepts, alternative formulations) while keeping the same underlying image and answer. This parameter transformation expands data quantity without proportional increases in labeling cost.
3Reliability
If more training data is collected through manual labeling, then the VQA system accuracy improves, but the time required increases
Solution Approach 1:
The system maintains continuous useful action by automatically and continuously generating new training data from existing data without interruption. This continuous automated generation process eliminates the batch-by-batch manual labeling workflow, providing a steady stream of training data that improves VQA accuracy over time without corresponding increases in collection time.
Solution Approach 2:
The system performs preliminary generation of candidate questions and answers using semantic analysis before final selection and validation. This preliminary action creates a pool of potential training samples that can be quickly filtered and validated, enabling rapid expansion of training data volume without linear increases in processing time.
Data Source
AI summary
Embodiments of the present disclosure relate to a method, an apparatus, a device and a computer readable medium for generating VQA training data. The method comprises: obtaining a first group of training data of the VQA system, the first group of training data including a first question for an image in the VQA system and a first answer corresponding to the first question; determining a second question associated with the first question in term of semantic; and determining a second answer corresponding to the second question based on the first question and the first answer, to obtain a second group of training data for the image in the VQA system, the second group of training data including the second question and the second answer.