{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/webvision-database-visual-learning-and","title":"WebVision Database: Visual Learning and Understanding from Web Data","arxiv_id":"1708.02862","date":"2017-08-09","proceeding":null,"authors":["Wen Li","Li-Min Wang","Wei Li","Eirikur Agustsson","Luc van Gool"],"abstract":"In this paper, we present a study on learning visual recognition models from\nlarge scale noisy web data. We build a new database called WebVision, which\ncontains more than $2.4$ million web images crawled from the Internet by using\nqueries generated from the 1,000 semantic concepts of the benchmark ILSVRC 2012\ndataset. Meta information along with those web images (e.g., title,\ndescription, tags, etc.) are also crawled. A validation set and test set\ncontaining human annotated images are also provided to facilitate algorithmic\ndevelopment. Based on our new database, we obtain a few interesting\nobservations: 1) the noisy web images are sufficient for training a good deep\nCNN model for visual recognition; 2) the model learnt from our WebVision\ndatabase exhibits comparable or even better generalization ability than the one\ntrained from the ILSVRC 2012 dataset when being transferred to new datasets and\ntasks; 3) a domain adaptation issue (a.k.a., dataset bias) is observed, which\nmeans the dataset can be used as the largest benchmark dataset for visual\ndomain adaptation. Our new WebVision database and relevant studies in this work\nwould benefit the advance of learning state-of-the-art visual models with\nminimum supervision based on web data.","url_abs":"http://arxiv.org/abs/1708.02862v1","url_pdf":"http://arxiv.org/pdf/1708.02862v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"}],"methods":[],"datasets_introduced":[{"slug":"webvision-database","name":"WebVision","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.02862","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}