Semantic and Correlation Disentangled Graph Convolutions for Multilabel Image Recognition
文献类型：期刊论文
| 作者 | Cai, Shaofei5; Li, Liang5; Han, Xinzhe4,5; Huang, Shan3; Tian, Qi2; Huang, Qingming1,4,5 |
| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| 出版日期 | 2023-11-30 |
| 页码 | 13 |
| 关键词 | Attention mechanism feature disentangling graph convolutional network (GCN) multilabel recognition |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2023.3333542 |
| 英文摘要 | Multilabel image recognition (MLR) aims to annotate an image with comprehensive labels and suffers from object occlusion or small object sizes within images. Although the existing works attempt to capture and exploit label correlations to tackle these issues, they predominantly rely on global statistical label correlations as prior knowledge for guiding label prediction, neglecting the unique label correlations present within each image. To overcome this limitation, we propose a semantic and correlation disentangled graph convolution (SCD-GC) method, which builds the image-specific graph and employs graph propagation to reason the labels effectively. Specifically, we introduce a semantic disentangling module to extract categorywise semantic features as graph nodes and develop a correlation disentangling module to extract image-specific label correlations as graph edges. Performing graph convolutions on this image-specific graph allows for better mining of difficult labels with weak visual representations. Visualization experiments reveal that our approach successfully disentangles the dominant label correlations existing within the input image. Through extensive experimentation, we demonstrate that our method achieves superior results on the challenging Microsoft COCO (MS-COCO), PASCAL visual object classes (PASCAL-VOC), NUS web image dataset (NUS-WIDE), and Visual Genome 500 (VG-500) datasets. Code is available at GitHub: https://github.com/caigitrepo/SCDGC. |
| 资助项目 | National Key Research and Development Program of China |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001121688300001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/38485]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Li, Liang |
| 作者单位 | 1.Peng Cheng Lab, Shenzhen 518066, Peoples R China 2.Huawei Technol, Cloud BU, Shenzhen 100190, Peoples R China 3.Tencent, Beijing 100085, Peoples R China 4.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100190, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cai, Shaofei,Li, Liang,Han, Xinzhe,et al. Semantic and Correlation Disentangled Graph Convolutions for Multilabel Image Recognition[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2023:13. |
| APA | Cai, Shaofei,Li, Liang,Han, Xinzhe,Huang, Shan,Tian, Qi,&Huang, Qingming.(2023).Semantic and Correlation Disentangled Graph Convolutions for Multilabel Image Recognition.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,13. |
| MLA | Cai, Shaofei,et al."Semantic and Correlation Disentangled Graph Convolutions for Multilabel Image Recognition".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023):13. |
入库方式： OAI收割
来源：计算技术研究所
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