{"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/multi-label-image-recognition-with-graph","title":"Multi-Label Image Recognition with Graph Convolutional Networks","arxiv_id":"1904.03582","date":"2019-04-07","proceeding":"CVPR 2019 6","authors":["Zhao-Min Chen","Xiu-Shen Wei","Peng Wang","Yanwen Guo"],"abstract":"The task of multi-label image recognition is to predict a set of object\nlabels that present in an image. As objects normally co-occur in an image, it\nis desirable to model the label dependencies to improve the recognition\nperformance. To capture and explore such important dependencies, we propose a\nmulti-label classification model based on Graph Convolutional Network (GCN).\nThe model builds a directed graph over the object labels, where each node\n(label) is represented by word embeddings of a label, and GCN is learned to map\nthis label graph into a set of inter-dependent object classifiers. These\nclassifiers are applied to the image descriptors extracted by another sub-net,\nenabling the whole network to be end-to-end trainable. Furthermore, we propose\na novel re-weighted scheme to create an effective label correlation matrix to\nguide information propagation among the nodes in GCN. Experiments on two\nmulti-label image recognition datasets show that our approach obviously\noutperforms other existing state-of-the-art methods. In addition, visualization\nanalyses reveal that the classifiers learned by our model maintain meaningful\nsemantic topology.","url_abs":"http://arxiv.org/abs/1904.03582v1","url_pdf":"http://arxiv.org/pdf/1904.03582v1.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":"multi-label-image-recognition-with-graph","repo_url":"https://github.com/Megvii-Nanjing/ML_GCN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"multi-label-image-recognition-with-graph","repo_url":"https://github.com/megvii-research/ml-gcn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"long-tail-learning","task_name":"Long-tail Learning"},{"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-recognition","task_name":"Multi-Label Image Recognition"},{"task_slug":"object","task_name":"Object"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[{"method_slug":"gcn","method_name":"GCN"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/long-tail-learning-on-coco-mlt","task":"Long-tail Learning","dataset":"COCO-MLT","model":"ML-GCN(ResNet-50)","rank_in_archive_order":12,"of":13,"metrics":{"Average mAP":"44.24"},"uses_additional_data":false},{"leaderboard":"/sota/long-tail-learning-on-voc-mlt","task":"Long-tail Learning","dataset":"VOC-MLT","model":"ML-GCN(ResNet-50)","rank_in_archive_order":13,"of":13,"metrics":{"Average mAP":"68.92"},"uses_additional_data":false},{"leaderboard":"/sota/multi-label-classification-on-pascal-voc-2007","task":"Multi-Label Classification","dataset":"PASCAL VOC 2007","model":"ML-GCN (pretrain from ImageNet)","rank_in_archive_order":13,"of":17,"metrics":{"mAP":"94.0"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.03582","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}