Dynamic Multi-view Combination for Image Classification
| Autor: | Wanguo Wang, Fengyuan Liu, Chenghua Li, Zhenyu Guo |
|---|---|
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Journal of Physics: Conference Series. 1631:012125 |
| ISSN: | 1742-6596 1742-6588 |
| DOI: | 10.1088/1742-6596/1631/1/012125 |
| Popis: | Multi-view learning is widely used in image classification tasks to better explore the discriminative information of different views. However, existing multi-view methods commonly rely on some pre-defined assumptions or fail to fully take advantage of the combination commonality between individual images. This paper presents an efficient dynamic multi-view combination approach to dynamically combine the discriminative power of different views. Specially, we firstly utilize a group of pre-trained CNNs to extract different views of an image. Secondly, we apply a dynamic gating module to the image, which will generate a weight vector of these views to model the image-level information for the multi-view learning. Finally, the weight vector and the views are combined for the classification. Experimental results and analysis on CIFAR-10 and ImageNet show the effectiveness of the proposed dynamic multi-view combination method (DMVC) for visual classification. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|