Name
Abstract | ||
|---|---|---|
Zero-shot classification and segmentation aims to recognize and segment objects of unseen classes. The attribute information, such as color, shape, part and material, is usually used for zero-shot classification. Moreover, we observe that this kind of attribute information could also be helpful in the segmentation task. On this basis, we propose an Attribute-Segmentation-Attribute (ASA) framework to address the zero-shot classification and segmentation problem. In the framework, a multi-task model is pre-trained to capture category and attribute features simultaneously. Then, a two-branch fully convolutional structure is built on the pre-trained model and fine-tuned for segmentation task. Finally, the extracted class-unseen object is recognized with the segmentation-assisted attribute prediction and a class-attribute matrix. Experimental results on the public bench-mark datasets indicate that the proposed ASA framework out-performs the state-of-the-art methods for both classification and segmentation tasks. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICMEW.2018.8551489 | 2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) |
Keywords | Field | DocType |
zero-shot classification and segmentation,attribute driven,ASA framework | Computer vision,Pattern recognition,Segmentation,Computer science,Matrix (mathematics),Artificial intelligence | Conference |
ISSN | ISBN | Citations |
2330-7927 | 978-1-5386-4196-5 | 0 |
PageRank | References | Authors |
0.34 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shu Yang | 1 | 63 | 11.18 |
| Yemin Shi | 2 | 37 | 9.48 |
| Yaowei Wang | 3 | 134 | 29.62 |
| Jing Wang | 4 | 507 | 93.00 |
| Zesong Fei | 5 | 699 | 86.33 |