Area of research
Media Technology · Computer Vision and Pattern Recognition
Research interest
Research topics from publications: A Remote-Sensing Scene-Image Classification Method Based on Deep Multiple-Instance Learning with a Residual Dense Attention ConvNet; Deep Tensor Capsule Network; Deep Multi-Instance Learning with Induced Self-Attention for Medical Image Classification; A lightweight and stochastic depth residual attention network for remote sensing scene classification; A multi-scale dense residual correlation network for remote sensing scene classification. Representative work: The spatial distribution of remote-sensing scene images is highly complex in character, so how to extract local key semantic information and discriminative features is the key to making it possible to classify accurately. However, most of the existing convolutional neural network (CNN) models tend to have global feature representations and lose the shallow features. In addition, when the network is too deep, gradient disappearance and overfitting tend to occur. To solve these problems, a lightweight, multi-instance CNN model for remote sensing scene classification is proposed in this paper: MILRDA. In the instance extraction and classifier part, more discriminative features are extracted by Capsule network is a promising model in computer vision. It has achieved excellent results on simple datasets such as MNIST, but the performance deteriorates as data becomes complicated. In order to address this issue, we propose a deep capsule network in this paper. To deepen the capsule network, we present a new tensor capsule based routing algorithm and the corresponding convolution operation. Compared to vector capsules, tensor capsules can capture more instance-level information. Together, the relevant convolution operation is beneficial for reducing the amount of parameters in the routing process. Furthermore, we propose a dropout mechanism for vectors and tensors in order to alleviate
A multi-scale dense residual correlation network for remote sensing scene classification
A lightweight and stochastic depth residual attention network for remote sensing scene classification
A Remote-Sensing Scene-Image Classification Method Based on Deep Multiple-Instance Learning with a Residual Dense Attention ConvNet
Deep Tensor Capsule Network
Deep Multi-Instance Learning with Induced Self-Attention for Medical Image Classification