Name
Abstract | ||
|---|---|---|
In this work, we investigate the ability of Convolutional Neural Networks (CNN) to infer the presence of components that comprise an image. In recent years, CNNs have achieved powerful results in classification, detection, and segmentation. However, these models learn from instance-level supervision of the detected object. In this paper, we determine if CNNs can detect objects using image-level weakly supervised labels without localization. To demonstrate that a CNN can infer awareness of objects, we evaluate a CNN's classification ability with a database constructed of Chinese characters with only character-level labeled components. We show that the CNN is able to achieve a high accuracy in identifying the presence of these components without specific knowledge of the component. Furthermore, we verify that the CNN is deducing the knowledge of the target component by comparing the results to an experiment with the component removed. This research is important for applications with large amounts of data without robust annotation such as Chinese character recognition. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICDAR.2017.72 | 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) |
Keywords | Field | DocType |
Convolutional Neural Network,Chinese Character,Weakly Labeled Data | Computer vision,Object detection,Chinese characters,Annotation,Task analysis,Pattern recognition,Character recognition,Convolutional neural network,Segmentation,Computer science,Image segmentation,Artificial intelligence | Conference |
Volume | ISSN | ISBN |
01 | 1520-5363 | 978-1-5386-3587-2 |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brian Kenji Iwana | 1 | 0 | 0.34 |
| Letao Zhou | 2 | 0 | 0.34 |
| Kumiko Tanaka-Ishii | 3 | 261 | 36.69 |
| Seiichi Uchida | 4 | 790 | 105.59 |