A Deformable Convolutional Neural Network with Oriented Response for Fine-Grained Visual Classification
文献类型：会议论文
| 作者 | Ruan, Shangxian3; Yang, Jiating2; Chen, Jianbo1 |
| 出版日期 | 2021-02-26 |
| 会议日期 | 2021-02-26 |
| 会议地点 | Virtual, Online, China |
| 关键词 | Fine-grained visual classification Deformable convolution Oriented response Weakly supervised |
| DOI | 10.1145/3457682.3457702 |
| 页码 | 133-140 |
| 英文摘要 | Fine-grained visual classification (FGVC) aims to classify images belonging to the same basic category in a more detailed sub-category. It is a challenging research topic in the field of computer vision and pattern recognition in recent years. The existing FGVC method conduct the task by considering the part detection of the object in the image and its variants, which rarely pays attention to the difference in expression of many changes such as object size, posture, and perspective. As a result, these methods generally face two major difficulties: 1) How to effectively pay attention to the latent semantic region, and reduce the interference caused by many changes in pose and perspective; 2) How to extract rich feature information for non-rigid and weak structure objects. In order to solve these two problems, this paper proposes a deformable convolutional neural network with oriented response for FGVC. The proposed method can be divided into three main steps: firstly, the local region of latent semantic information is localized based on a lightweight CAM network; then, the deformable convolutional ResNet-50 network and the rotation-invariant coding oriented response network are designed, which input the original image and local region into the feature network to learn the discriminant features of rotation invariance; finally, the learned features are embed into a joint loss to optimize the entire network end-to-end. Experiments are carried out on three challenging FGVC datasets, including CUB-200-2011, FGVC_Aircraft and Aircraft_2 datasets. The results show that the accuracy of the proposed method on all datasets is better than the comparison method, which can effectively improve the accuracy of weakly supervised FGVC. © 2021 ACM. |
| 产权排序 | 2 |
| 会议录 | 2021 13th International Conference on Machine Learning and Computing, ICMLC 2021
 |
| 会议录出版者 | Association for Computing Machinery |
| 语种 | 英语 |
| ISBN号 | 9781450389310 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/94955]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.University of Chinese Academy of Sciences, Beijing, China 2.Xian Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xian, China; 3.Amazingx Academy, Foshan, China; |
| 推荐引用方式 GB/T 7714 | Ruan, Shangxian,Yang, Jiating,Chen, Jianbo. A Deformable Convolutional Neural Network with Oriented Response for Fine-Grained Visual Classification[C]. 见:. Virtual, Online, China. 2021-02-26. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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