README.md

---
license: cc-by-nc-4.0
task_categories:
- text-to-image
- image-to-text
- text-retrieval
modalities:
- image
- text
language:
- zh
- en
- ja
- ru
tags:
- realistic
- industry
- mobile user interface
- image-text matching
- image-text retrieval
- noisy correspondence learning
- NCL benchmark
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, Zhangxuan Gu, Zhenzhe Ying, Lei Qi, Changhua Meng and Yinghuan Shi*
 
Quick links: [[arXiv (coming soon)]() | [Published paper (coming soon)]() | [Poster (coming soon)]() | [Zhihu (coming soon)]() | [Code download]() | [Dataset download](https://drive.google.com/file/d/1MsR9GmRDUj4NoeL4xL8TXpes51JnpsrZ/view?usp=drive_link)]

## Data Collection
We develop a new dataset named **Noise of Web (NoW)** for NCL. It contains **100K** cross-modal 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 $\times$ 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="/figures/now-1.jpg"> 
 
</div>

## Data Structure

```

|-- h5100k_precomp
|   |-- dev_caps_bpe.txt
|   |-- dev_caps_bert.txt
|   |-- dev_ids.txt
|   |-- dev_ims.npy
|   |-- test_caps_bpe.txt
|   |-- test_caps_bert.txt
|   |-- test_ids.txt
|   |-- test_ims.npy
|   |-- train_caps_bpe.txt
|   |-- train_caps_bert.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 use both [Tokenizers](https://github.com/huggingface/tokenizers) with [BPE](https://huggingface.co/docs/tokenizers/api/models#tokenizers.models.BPE) to produce \*_caps_bpe.txt and [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. **Our vocabulary size of BPE tokenizer is 10,000 and that of BertTokenizer is 32702**. \*_ids.txt records the serial number of the data in the original 500k dataset. In the future, we may process and make the original dataset public.