{"id":802141,"date":"2021-12-06T14:54:08","date_gmt":"2021-12-06T22:54:08","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=802141"},"modified":"2021-12-06T14:54:08","modified_gmt":"2021-12-06T22:54:08","slug":"minivlm-a-smaller-and-faster-vision-language-model","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/minivlm-a-smaller-and-faster-vision-language-model\/","title":{"rendered":"MiniVLM: A Smaller and Faster Vision-Language Model"},"content":{"rendered":"

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\u00a095<\/span>%<\/span><\/span><\/span><\/span>, 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\u00a07<\/span>M<\/span><\/span><\/span><\/span>\u00a0Open 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\u00a073<\/span>%<\/span><\/span><\/span><\/span>\u00a0and the inference time cost by\u00a094<\/span>%<\/span><\/span><\/span><\/span>\u00a0while being able to retain\u00a094<\/span>\u2212<\/span>97<\/span>%<\/span><\/span><\/span><\/span>\u00a0of 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.<\/p>\n","protected":false},"excerpt":{"rendered":"

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 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