Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions

Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions

Liunian Harold Li, Haoxuan You, Zhecan Wang, Alireza Zareian, Shih-Fu Chang, and Kai-Wei Chang, in NAACL, 2021.

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Abstract

Pre-trained contextual vision-and-language (V&L) models have brought impressive performance improvement on various benchmarks. However, the paired text-image data required for pre-training are hard to collect and scale up. We investigate if a strong V&L representation model can be learned without text-image pairs. We propose Weakly-supervised VisualBERT with the key idea of conducting "mask-and-predict" pre-training on language-only and image-only corpora. Additionally, we introduce the object tags detected by an object recognition model as anchor points to bridge two modalities. Evaluation on four V&L benchmarks shows that Weakly-supervised VisualBERT achieves similar performance with a model pre-trained with paired data. Besides, pre-training on more image-only data further improves a model that already has access to aligned data, suggesting the possibility of utilizing billions of raw images available to enhance V&L models.

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Bib Entry

@inproceedings{li2021unsupervised,
  author = {Li, Liunian Harold and You, Haoxuan and Wang, Zhecan and Zareian, Alireza and Chang, Shih-Fu and Chang, Kai-Wei},
  title = {Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions},
  booktitle = {NAACL},
  presentation_id = {https://underline.io/events/122/sessions/4269/lecture/19725-unsupervised-vision-and-language-pre-training-without-parallel-images-and-captions},
  year = {2021}
}

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Related Publications

1. Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning, EMNLP, 2021
2. What Does BERT with Vision Look At?, ACL (short), 2020
3. VisualBERT: A Simple and Performant Baseline for Vision and Language, Arxiv, 2019