Datasets:

NJUyued
/

NoW

@@ -4,19 +4,19 @@ task_categories:
 - text-to-image
 - image-to-text
 - text-retrieval
-modalities:
-- text
-- image
 language:
 - zh
 - en
 - ja
 - ru
-format:
 - npy
 - txt
 - json
-tags:
 - realistic
 - industry
 - mobile user interface
@@ -35,7 +35,7 @@ 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%**.

 - text-to-image
 - image-to-text
 - text-retrieval
 language:
 - zh
 - en
 - ja
 - ru
+tags:
+- image
+- text
 - npy
 - txt
 - json
 - realistic
 - industry
 - mobile user interface
 > ***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://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** 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%**.