핵심 개념
Harnessing self-questioning in vision-language models enhances understanding and alignment.
초록
The paper introduces SQ-LLaVA, a framework that utilizes visual self-questioning to improve vision-language understanding. By training the model to ask high-quality questions based on image context, SQ-LLaVA achieves advanced levels of generalized visual understanding. The model outperforms traditional visual instruction tuning methods by leveraging overlooked contextual information within images. Through experiments, SQ-LLaVA demonstrates improved performance in various vision-language tasks, showcasing the effectiveness of self-questioning techniques in enhancing comprehension of visual content.
통계
Existing works consider more visual instruction data for fine-tuning models for question-answering tasks.
LLaVA has achieved better performance on GQA and VizWiz tasks compared to previous methods.
SQ-LLaVA shows consistent performance improvement compared to traditional tuning methods.
인용구
"Existing works usually consider more visual instruction data covering a broader range of vision tasks to fine-tune the model for question-answering."
"SQ-LLaVA exhibits proficiency in generating flexible and meaningful image-related questions while analyzing the visual clue and prior language knowledge."
"Our proposed method leads to better performance in several areas, including traditional Visual Question Answering tasks."