ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision

Wonjae Kim, Bokyung Son, Ildoo Kim
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:5583-5594, 2021.

Abstract

Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks. Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision (e.g., object detection) and the convolutional architecture (e.g., ResNet). Although disregarded in the literature, we find it problematic in terms of both (1) efficiency/speed, that simply extracting input features requires much more computation than the multimodal interaction steps; and (2) expressive power, as it is upper bounded to the expressive power of the visual embedder and its predefined visual vocabulary. In this paper, we present a minimal VLP model, Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically simplified to just the same convolution-free manner that we process textual inputs. We show that ViLT is up to tens of times faster than previous VLP models, yet with competitive or better downstream task performance. Our code and pre-trained weights are available at https://github.com/dandelin/vilt.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-kim21k, title = {ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision}, author = {Kim, Wonjae and Son, Bokyung and Kim, Ildoo}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {5583--5594}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/kim21k/kim21k.pdf}, url = {https://proceedings.mlr.press/v139/kim21k.html}, abstract = {Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks. Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision (e.g., object detection) and the convolutional architecture (e.g., ResNet). Although disregarded in the literature, we find it problematic in terms of both (1) efficiency/speed, that simply extracting input features requires much more computation than the multimodal interaction steps; and (2) expressive power, as it is upper bounded to the expressive power of the visual embedder and its predefined visual vocabulary. In this paper, we present a minimal VLP model, Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically simplified to just the same convolution-free manner that we process textual inputs. We show that ViLT is up to tens of times faster than previous VLP models, yet with competitive or better downstream task performance. Our code and pre-trained weights are available at https://github.com/dandelin/vilt.} }
Endnote
%0 Conference Paper %T ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision %A Wonjae Kim %A Bokyung Son %A Ildoo Kim %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-kim21k %I PMLR %P 5583--5594 %U https://proceedings.mlr.press/v139/kim21k.html %V 139 %X Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks. Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision (e.g., object detection) and the convolutional architecture (e.g., ResNet). Although disregarded in the literature, we find it problematic in terms of both (1) efficiency/speed, that simply extracting input features requires much more computation than the multimodal interaction steps; and (2) expressive power, as it is upper bounded to the expressive power of the visual embedder and its predefined visual vocabulary. In this paper, we present a minimal VLP model, Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically simplified to just the same convolution-free manner that we process textual inputs. We show that ViLT is up to tens of times faster than previous VLP models, yet with competitive or better downstream task performance. Our code and pre-trained weights are available at https://github.com/dandelin/vilt.
APA
Kim, W., Son, B. & Kim, I.. (2021). ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:5583-5594 Available from https://proceedings.mlr.press/v139/kim21k.html.

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