{"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/a-large-scale-attribute-dataset-for-zero-shot","title":"A Large-scale Attribute Dataset for Zero-shot Learning","arxiv_id":"1804.04314","date":"2018-04-12","proceeding":null,"authors":["Bo Zhao","Yanwei Fu","Rui Liang","Jia-Hong Wu","Yonggang Wang","Yizhou Wang"],"abstract":"Zero-Shot Learning (ZSL) has attracted huge research attention over the past\nfew years; it aims to learn the new concepts that have never been seen before.\nIn classical ZSL algorithms, attributes are introduced as the intermediate\nsemantic representation to realize the knowledge transfer from seen classes to\nunseen classes. Previous ZSL algorithms are tested on several benchmark\ndatasets annotated with attributes. However, these datasets are defective in\nterms of the image distribution and attribute diversity. In addition, we argue\nthat the \"co-occurrence bias problem\" of existing datasets, which is caused by\nthe biased co-occurrence of objects, significantly hinders models from\ncorrectly learning the concept. To overcome these problems, we propose a\nLarge-scale Attribute Dataset (LAD). Our dataset has 78,017 images of 5\nsuper-classes, 230 classes. The image number of LAD is larger than the sum of\nthe four most popular attribute datasets. 359 attributes of visual, semantic\nand subjective properties are defined and annotated in instance-level. We\nanalyze our dataset by conducting both supervised learning and zero-shot\nlearning tasks. Seven state-of-the-art ZSL algorithms are tested on this new\ndataset. The experimental results reveal the challenge of implementing\nzero-shot learning on our dataset.","url_abs":"http://arxiv.org/abs/1804.04314v2","url_pdf":"http://arxiv.org/pdf/1804.04314v2.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":[{"paper_slug":"a-large-scale-attribute-dataset-for-zero-shot","repo_url":"https://github.com/PatrickZH/A-Large-scale-Attribute-Dataset-for-Zero-shot-Learning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"zero-shot-learning","task_name":"Zero-Shot Learning"}],"methods":[],"datasets_introduced":[{"slug":"lad","name":"LAD","full_name":"Large-scale Attribute Dataset"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.04314","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}