  Recent vision-language (VL) studies have shown remarkable progress by
learning generic representations from massive image-text pairs with transformer
models and then fine-tuning on downstream VL tasks. While existing research has
been focused on achieving high accuracy with large pre-trained models, building
a lightweight model is of great value in practice but is less explored. In this
paper, we propose a smaller and faster VL model, MiniVLM, which can be
finetuned with good performance on various downstream tasks like its larger
counterpart. MiniVLM consists of two modules, a vision feature extractor and a
transformer-based vision-language fusion module. We design a Two-stage
Efficient feature Extractor (TEE), inspired by the one-stage EfficientDet
network, to significantly reduce the time cost of visual feature extraction by
$95\%$, compared to a baseline model. We adopt the MiniLM structure to reduce
the computation cost of the transformer module after comparing different
compact BERT models. In addition, we improve the MiniVLM pre-training by adding
$7M$ Open Images data, which are pseudo-labeled by a state-of-the-art
captioning model. We also pre-train with high-quality image tags obtained from
a strong tagging model to enhance cross-modality alignment. The large models
are used offline without adding any overhead in fine-tuning and inference. With
the above design choices, our MiniVLM reduces the model size by $73\%$ and the
inference time cost by $94\%$ while being able to retain $94-97\%$ of the
accuracy on multiple VL tasks. We hope that MiniVLM helps ease the use of the
state-of-the-art VL research for on-the-edge applications.
