Unsupervised Learning of Discriminative Attributes and Visual Representations
文献类型：会议论文
| 作者 | Chen Huang; Chen Change Loy; Xiaoou Tang |
| 出版日期 | 2016 |
| 会议名称 | CVPR2016 |
| 会议地点 | 美国 |
| 英文摘要 | Attributes offer useful mid-level features to interpret vi- sual data. While most attribute learning methods are super- vised by costly human-generated labels, we introduce a sim- ple yet powerful unsupervised approach to learn and predict visual attributes directly from data. Given a large unlabeled image collection as input, we train deep Convolutional Neu- ral Networks (CNNs) to output a set of discriminative, bi- nary attributes often with semantic meanings. Specifically, we first train a CNN coupled with unsupervised discrimi- native clustering, and then use the cluster membership as a soft supervision to discover shared attributes from the clus- ters while maximizing their separability. The learned at- tributes are shown to be capable of encoding rich imagery properties from both natural images and contour patches. The visual representations learned in this way are also transferrable to other tasks such as object detection. We show other convincing results on the related tasks of image retrieval and classification, and contour detection. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/10019]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2016 |
| 推荐引用方式 GB/T 7714 | Chen Huang,Chen Change Loy,Xiaoou Tang. Unsupervised Learning of Discriminative Attributes and Visual Representations[C]. 见:CVPR2016. 美国. |
入库方式： OAI收割
来源：深圳先进技术研究院
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