Datasets:
Size:
100K<n<1M
ArXiv:
Tags:
image-text retrieval
noisy correspondence learning
NCL-specific benchmark
realistic
industry
mobile user interface
License:
File size: 5,375 Bytes
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---
license: cc-by-nc-4.0
task_categories:
- text-to-image
- image-to-text
- text-retrieval
language:
- zh
- en
- ja
- ru
tags:
- image-text retrieval
- noisy correspondence learning
- NCL-specific benchmark
- realistic
- industry
- mobile user interface
- image-text matching
- image
- text
- npy
- txt
- json
size_categories:
- 100K<n<1M
---
# PC2-NoiseofWeb
This repo releases data introduced in our paper
> ***PC2: Pseudo-Classification Based Pseudo-Captioning for Noisy Correspondence Learning in Cross-Modal Retrieval***
> **Authors**: ***[Yue Duan](https://njuyued.github.io/)**, Zhangxuan Gu, Zhenzhe Ying, Lei Qi, Changhua Meng and Yinghuan Shi*
Quick links: [[Code](https://github.com/alipay/PC2-NoiseofWeb) | [[PDF](https://arxiv.org/pdf/2408.01349)/[Abs](https://arxiv.org/abs/2408.01349)-arXiv | [Published paper (coming soon)]() | [Poster (coming soon)]() | [Zhihu](https://zhuanlan.zhihu.com/p/711149124) | [Dataset download](https://huggingface.co/datasets/NJUyued/NoW/resolve/main/NoW.zip?download=true)]
## Data Collection
We develop a new dataset named **Noise of Web (NoW)** for NCL. It contains **100K image-text pairs** consisting of **website images** and **multilingual website meta-descriptions** (**98,000 pairs for training, 1,000 for validation, and 1,000 for testing**). NoW has two main characteristics: *without human annotations and the noisy pairs are naturally captured*. The source image data of NoW is obtained by taking screenshots when accessing web pages on mobile user interface (MUI) with 720 X 1280 resolution, and we parse the meta-description field in the HTML source code as the captions. In [NCR](https://github.com/XLearning-SCU/2021-NeurIPS-NCR) (predecessor of NCL), each image in all datasets were preprocessed using Faster-RCNN detector provided by [Bottom-up Attention Model](https://github.com/peteanderson80/bottom-up-attention) to generate 36 region proposals, and each proposal was encoded as a 2048-dimensional feature. Thus, following NCR, we release our the features instead of raw images for fair comparison. However, we can not just use detection methods like Faster-RCNN to extract image features since it is trained on real-world animals and objects on MS-COCO. To tackle this, we adapt [APT](https://openaccess.thecvf.com/content/CVPR2023/papers/Gu_Mobile_User_Interface_Element_Detection_via_Adaptively_Prompt_Tuning_CVPR_2023_paper.pdf) as the detection model since it is trained on MUI data. Then, we capture the 768-dimensional features of top 36 objects for one image. Due to the automated and non-human curated data collection process, the noise in NoW is highly authentic and intrinsic. **The estimated noise ratio of this dataset is nearly 70%**.
<div align=center>
<img width="750px" src="NoW.jpg">
</div>
## Data Structure
```
|-- h5100k_precomp
| |-- dev_caps_bpe.txt
| |-- dev_caps_bert.txt
| |-- dev_caps_jieba.txt
| |-- dev_ids.txt
| |-- dev_ims.npy
| |-- test_caps_bpe.txt
| |-- test_caps_bert.txt
| |-- test_caps_jieba.txt
| |-- test_ids.txt
| |-- test_ims.npy
| |-- train_caps_bpe.txt
| |-- train_caps_bert.txt
| |-- train_caps_jieba.txt
| |-- train_ids.txt
| |-- train_ims.npy
|-- vocab
| |-- now100k_precomp_vocab_bert.json
| |-- now100k_precomp_vocab_bpe.json
| |-- now100k_precomp_vocab_jieba.json
```
Please note that since our raw data contains some sensitive business data, we only provide the **encoded image features** (\*_ims.npy) and the **token ids of the text tokenized**. For tokenizer, we provide [Tokenizers](https://github.com/huggingface/tokenizers) with [BPE](https://huggingface.co/docs/tokenizers/api/models#tokenizers.models.BPE) to produce \*_caps_bpe.txt, [BertTokenizer](https://huggingface.co/transformers/v3.0.2/model_doc/bert.html#berttokenizer) with [bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) pre-trained model to produce \*_caps_bert.txt, and [Jieba](https://github.com/fxsjy/jieba) to produce \*_caps_jieba.txt. **Our vocabulary size of BPETokenizer is 10,000, while BertTokenizer and JiebaTokenizer have a vocabulary size of 32,702 and 56,271 respectively.** (recorded in now100k_precomp_vocab\_\*.txt). \*_ids.txt records the data indexs in the original 500k dataset. In the future, we may process and make the original dataset public.
## Usage
```
# data_path: your dataset name and path
# data_split: {train,dev,test}
# tokenizer: {bpe,bert,jieba}
# vocabulary size of {bpe,bert,jieba} is {10000,32702,56271}
# captions
with open(os.path.join(data_path, "{}_caps_{}.txt".format(data_split, tokenizer))) as f:
for line in f:
captions.append(line.strip())
captions_token = []
for index in range(len(captions)):
caption = captions[index]
tokens = caption.split(',')
caption = []
caption.append(vocab("<start>"))
caption.extend([int(token) for token in tokens if token])
caption.append(vocab("<end>"))
captions_token.append(caption)
# images
images = np.load(os.path.join(data_path, "%s_ims.npy" % data_split))
return captions_token, images
```
Additionally, you can search for code snippets containing the string `now100k_precomp` in `co_train.py`, `data.py`, `evaluation.py`, and `run.py` in [PC2's repo](https://github.com/alipay/PC2-NoiseofWeb) and refer to them to process the NoW dataset for use in your own code.
|