SQ-LLaVA: Self-Questioning for Large Vision-Language Assistant
Guohao Sun Can Qin Jiamian Wang Zeyuan Chen Ran Xu Zhiqiang Tao
Recent advancements in the vision-language model have shown notable
generalization in vision-language tasks after visual instruction tuning.
However, bridging the gap between the pre-trained vision encoder and the large
language models 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 are costly to obtain. However, the image
contains rich contextual information that has been largely under-explored. This
paper first attempts to harness this 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 consistent
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.