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1from zeronlg import ZeroNLG
2
3# Automatically download the model from Huggingface Hub
4# Note: this model is especially pre-trained for visual captioning
5model = ZeroNLG('zeronlg-4langs-vc')
6
7# `images` can be a remote image url, a local image/video file, etc
8# `lang` should be one of English ('en'), Chinese ('zh'), German ('de'), and French ('fr')
9url = 'https://img2.baidu.com/it/u=1856500011,1563285204&fm=253&fmt=auto&app=138&f=JPEG?w=667&h=500'
10caption = model.forward(images=url, lang='en', num_beams=3, task='caption')
11# caption = "dogs play in the snow"
12
13caption = model.forward(images=url, lang='zh', num_beams=3, task='caption')
14# caption = "狗 在 雪 地 里 玩 耍"
15
16# Althernatively, you can call the specific forward function
17caption = model.forward_caption(images=url, lang='en', num_beams=3)| Dataset | Language | Type | BLEU@1 | BLEU@2 | BLEU@3 | BLEU@4 | METEOR | ROUGE-L | CIDEr-D | SPICE |
|---|---|---|---|---|---|---|---|---|---|---|
| Flickr30K | English | Image | 46.4 | 27.2 | 15.5 | 8.9 | 13.0 | 31.3 | 21.0 | 7.6 |
| Flickr30K | Chinese | Image | 45.3 | 25.5 | 14.6 | 8.4 | - | 31.8 | 18.0 | - |
| Flickr30K | German | Image | 41.9 | 21.1 | 11.2 | 5.7 | - | 21.2 | 17.1 | - |
| Flickr30K | French | Image | 19.8 | 9.5 | 5.0 | 2.8 | - | 18.6 | 24.8 | - |
| COCO | English | Image | 47.5 | 29.0 | 16.8 | 9.6 | 14.4 | 34.9 | 29.9 | 8.7 |
| MSR-VTT | English | Video | 52.2 | 31.9 | 16.6 | 8.7 | 15.0 | 35.4 | 9.9 | - |
| VATEX | English | Video | 42.2 | 24.6 | 12.5 | 6.3 | 11.7 | 29.3 | 9.1 | - |
| VATEX | Chinese | Video | 41.9 | 24.3 | 13.7 | 7.1 | - | 29.6 | 9.8 | - |
Flickr30K-Chinese is known as Flickr30K-CN.Flickr30K-German and Flickr30K-French are introduced in Multi30K.| Dataset | Language | Type | I2T R@1 | I2T R@5 | I2T R@10 | I2T Mean | T2I R@1 | T2I R@5 | T2I R@10 | T2I Mean | Avg. |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Flickr30K | English | Image | 75.2 | 93.9 | 97.1 | 88.7 | 57.1 | 82.2 | 89.1 | 76.1 | 82.4 |
| Flickr30K | Chinese | Image | 75.0 | 93.0 | 96.7 | 88.2 | 53.8 | 79.8 | 87.1 | 73.6 | 80.9 |
| Flickr30K | German | Image | 70.9 | 91.1 | 95.7 | 85.9 | 47.5 | 74.1 | 83.1 | 68.2 | 77.1 |
| Flickr30K | French | Image | 55.8 | 83.4 | 91.5 | 76.9 | 56.6 | 81.2 | 88.4 | 75.4 | 76.2 |
| COCO 5K | English | Image | 45.0 | 71.1 | 80.3 | 65.5 | 28.2 | 53.3 | 64.5 | 48.7 | 57.1 |
| COCO 1K | English | Image | 66.0 | 89.1 | 94.6 | 83.2 | 47.5 | 77.5 | 87.9 | 71.0 | 77.1 |
| MSR-VTT | English | Video | 32.0 | 55.5 | 65.8 | 51.1 | 17.9 | 36.4 | 45.5 | 33.3 | 42.2 |
| VATEX | English | Video | 26.9 | 52.8 | 64.2 | 48.0 | 19.2 | 41.2 | 52.7 | 37.7 | 42.8 |
| VATEX | Chinese | Video | 40.6 | 70.9 | 82.7 | 64.7 | 28.8 | 58.0 | 70.1 | 52.3 | 58.5 |
I2T: image-to-text retrieval, image as the query, search similar textsT2I: text-to-image retrieval, text as the query, search similar imagesR@K: Recall rate at top-K candidatesAvg.: Average of R@{1,5,10} on both directionsCOCO-1K, which splits the original testing set into 5 folds and report performance averaged over 5 folds.1@article{Yang2023ZeroNLG,
2 title={ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation},
3 author={Yang, Bang and Liu, Fenglin and Zou, Yuexian and Wu, Xian and Wang, Yaowei and Clifton, David A.},
4 journal={arXiv preprint arXiv:2303.06458}
5 year={2023}
6}