Name
Abstract | ||
|---|---|---|
Pre-trained contextual vision-and-language (V&L) models have achieved impressive performance on various benchmarks. However, existing models require a large amount of parallel image-caption data for pre-training. Such data are costly to collect and require cumbersome curation. Inspired by unsupervised machine translation, we investigate if a strong V&L representation model can be learned through unsupervised pre-training without image-caption corpora. In particular, we propose to conduct “mask-and-predict” pre-training on text-only and image-only corpora and introduce the object tags detected by an object recognition model as anchor points to bridge two modalities. We find that such a simple approach achieves performance close to a model pre-trained with aligned data, on four English V&L benchmarks. Our work challenges the widely held notion that aligned data is necessary for V&L pre-training, while significantly reducing the amount of supervision needed for V&L models. |
Year | Venue | DocType |
|---|---|---|
2021 | NAACL-HLT | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liunian Harold Li | 1 | 0 | 1.01 |
| Haoxuan You | 2 | 37 | 4.87 |
| Zhecan Wang | 3 | 20 | 2.74 |
| Zareian, Alireza | 4 | 7 | 4.20 |
| Shih-Fu Chang | 5 | 13015 | 1101.53 |
| Kai-Wei Chang | 6 | 4735 | 276.81 |