A novel CNN model for fine-grained classification with large spatial variants
文献类型：期刊论文
| 作者 | Wang,Junpeng1,2 ; Lu,Yanfeng1
|
| 刊名 | Journal of Physics: Conference Series
 |
| 出版日期 | 2020-05-01 |
| 卷号 | 1544期号:1 |
| ISSN号 | 1742-6588 |
| DOI | 10.1088/1742-6596/1544/1/012138 |
| 英文摘要 | Abstract Convolutional Neural Networks (CNN) have achieved great performance in many visual tasks. However, CNN models are sensitive to samples with large spatial variants, especially severe in fine-grained classification task. In this paper, we propose a novel CNN model called ST-BCNN to solve these problems. ST-BCNN contains two functional CNN modules: Spatial Transform Network (STN) and Bilinear CNN(BCNN). Firstly, STN module is used to select key region in input samples and get it spatially modified. Since the adoption of STN will cause an information loss phenomenon called boundary loss, we design a brand-new IOU loss method to solve it. We make a theoretical analysis of the IOU loss method. Secondly, to discover discriminative features for fine-grained classification task, BCNN module is applied. BCNN interacts CNN features from different channels to produce more discriminative bilinear features than fully connected features of CNN. ST-BCNN works by reducing irrelevant spatial states and producing fine-grained features. We evaluate our model on 3 public fine-grained classification datasets with large spatial variants: CUB200-2011, Fish100 and UAV43. Experiments show that the IOU loss method can reduce boundary loss and make STN module output spatial transformed image appropriately. Our proposed ST-BCNN model outperforms other advanced CNN models on all three datasets. |
| 语种 | 英语 |
| WOS记录号 | IOP:1742-6588-1544-1-012138 |
| 出版者 | IOP Publishing |
| 源URL | [http://ir.ia.ac.cn/handle/173211/39830]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 |
| 作者单位 | 1.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China |
| 推荐引用方式 GB/T 7714 | Wang,Junpeng,Lu,Yanfeng. A novel CNN model for fine-grained classification with large spatial variants[J]. Journal of Physics: Conference Series,2020,1544(1). |
| APA | Wang,Junpeng,&Lu,Yanfeng.(2020).A novel CNN model for fine-grained classification with large spatial variants.Journal of Physics: Conference Series,1544(1). |
| MLA | Wang,Junpeng,et al."A novel CNN model for fine-grained classification with large spatial variants".Journal of Physics: Conference Series 1544.1(2020). |
入库方式： OAI收割
来源：自动化研究所
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