Feature-level fusion of convolutional neural networks for visual object classification
2016pp. 2173–2176
Citations Over Time
Abstract
Deep learning architectures have shown great success in various computer vision applications. In this study, we investigate some of the very popular convolutional neural network (CNN) architectures, namely GoogleNet, AlexNet, VGG19 and ResNet. Furthermore, we show possible early feature fusion strategies for visual object classification tasks. Concatanation of features, average pooling and maximum pooling are among the investigated fusion strategies. We obtain state-of-the-art results on well-known image classification datasets of Caltech-101, Caltech-256 and Pascal VOC 2007.
Related Papers
- → Object detection through region proposal based techniques(2021)13 cited
- → Object detection using improved YOLOv3-tiny based on pyramid pooling(2021)1 cited
- → Improving object detection via improving accuracy of object localization(2016)
- → Improved Regional Proposal Generation and Proposal Selection Method for Weakly Supervision Object detection(2023)