{"url":"/dataset/noise-of-web-now","name":"Noise of Web","full_name":"NoW","description_markdown":"Noise of Web (NoW) is a challenging noisy correspondence learning (NCL) benchmark for robust image-text matching/retrieval models. It contains **100K image-text pairs** consisting of **website pages** 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%**.","description_withheld":null,"homepage":"https://huggingface.co/datasets/NJUyued/NoW","introduced_date":"2024-08-02","introduced_date_note":null,"introduced_by":null,"license":{"name":"cc-by-nc-4.0","url":"https://spdx.org/licenses/CC-BY-NC-4.0"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Cross-modal retrieval with noisy correspondence","url":"/task/cross-modal-retrieval-with-noisy","datasets_with_task":"/datasets/task/cross-modal-retrieval-with-noisy"},{"name":"Image-text Retrieval","url":"/task/image-text-retrieval","datasets_with_task":"/datasets/task/image-text-retrieval"}],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"Chinese","url":"/datasets/language/chinese"},{"name":"Japanese","url":"/datasets/language/japanese"},{"name":"Russian","url":"/datasets/language/russian"}],"variants":["Noise of Web"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}