{"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/cnn-rnn-a-unified-framework-for-multi-label","title":"CNN-RNN: A Unified Framework for Multi-label Image Classification","arxiv_id":"1604.04573","date":"2016-04-15","proceeding":"CVPR 2016 6","authors":["Jiang Wang","Yi Yang","Junhua Mao","Zhiheng Huang","Chang Huang","Wei Xu"],"abstract":"While deep convolutional neural networks (CNNs) have shown a great success in\nsingle-label image classification, it is important to note that real world\nimages generally contain multiple labels, which could correspond to different\nobjects, scenes, actions and attributes in an image. Traditional approaches to\nmulti-label image classification learn independent classifiers for each\ncategory and employ ranking or thresholding on the classification results.\nThese techniques, although working well, fail to explicitly exploit the label\ndependencies in an image. In this paper, we utilize recurrent neural networks\n(RNNs) to address this problem. Combined with CNNs, the proposed CNN-RNN\nframework learns a joint image-label embedding to characterize the semantic\nlabel dependency as well as the image-label relevance, and it can be trained\nend-to-end from scratch to integrate both information in a unified framework.\nExperimental results on public benchmark datasets demonstrate that the proposed\narchitecture achieves better performance than the state-of-the-art multi-label\nclassification model","url_abs":"http://arxiv.org/abs/1604.04573v1","url_pdf":"http://arxiv.org/pdf/1604.04573v1.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":"cnn-rnn-a-unified-framework-for-multi-label","repo_url":"https://github.com/Lin-Zhipeng/CNN-RNN-A-Unified-Framework-for-Multi-label-Image-Classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"multi-label-classification-2","task_name":"MUlTI-LABEL-ClASSIFICATION"},{"task_slug":"multi-label-classification","task_name":"Multi-Label Classification"},{"task_slug":"multi-label-image-classification","task_name":"Multi-Label Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1604.04573","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}