  Large-scale pretraining and instruction tuning have been successful for
training general-purpose language models with broad competencies. However,
extending to general-purpose vision-language models is challenging due to the
distributional diversity in visual inputs. A recent line of work explores
vision-language instruction tuning, taking inspiration from the Query
Transformer (QFormer) approach proposed in BLIP-2 models for bridging frozen
modalities. However, these approaches rely heavily on large-scale multi-modal
pretraining for representation learning before eventual finetuning, incurring a
huge computational overhead, poor scaling, and limited accessibility. To that
end, we propose a more efficient method for QFormer-based vision-language
alignment and demonstrate the effectiveness of our strategy compared to
existing baselines in improving the efficiency of vision-language pretraining.
