MiniVLM: A Smaller and Faster Vision-Language Model

• URL: http://arxiv.org/abs/2012.06946v1
• Date: Sun, 13 Dec 2020 03:02:06 GMT
• Title: MiniVLM: A Smaller and Faster Vision-Language Model
• Authors: Jianfeng Wang and Xiaowei Hu and Pengchuan Zhang and Xiujun Li and Lijuan Wang and Lei Zhang and Jianfeng Gao and Zicheng Liu
• Abstract summary: MiniVLM consists of two modules, a vision feature extractor and a vision-language fusion module. MiniVLM reduces the model size by $73%$ and the inference time cost by $94%$.
• Score: 76.35880443015493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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