  Recent advances in vision-language models have shown notable generalization
in broad tasks through visual instruction tuning. However, bridging the gap
between the pre-trained vision encoder and the large language models (LLMs)
becomes the whole network's bottleneck. To improve cross-modality alignment,
existing works usually consider more visual instruction data covering a broader
range of vision tasks to fine-tune the model for question-answering, which,
however, is costly to obtain and has not thoroughly explored the rich
contextual information contained in images. This paper first attempts to
harness the overlooked context within visual instruction data, training the
model to self-supervised "learning" how to ask high-quality questions. In this
way, we introduce a novel framework named SQ-LLaVA: Self-Questioning for Large
Vision-Language Assistant. SQ-LLaVA exhibits proficiency in generating flexible
and meaningful image-related questions while analyzing the visual clue and
prior language knowledge, signifying an advanced level of generalized visual
understanding. Moreover, fine-tuning SQ-LLaVA on higher-quality instruction
data shows a performance improvement compared with traditional
visual-instruction tuning methods. This improvement highlights the efficacy of
self-questioning techniques in achieving a deeper and more nuanced
comprehension of visual content across various contexts.
