57 citations · 64 across the 10 of their papers we have counts for
12 papers
RL-CoSeg : A Novel Image Co-Segmentation Algorithm with Deep Reinforcement Learning
Xin Duan, Xiabi Liu, Xiaopeng Gong +1
This paper proposes an automatic image co-segmentation algorithm based on deep reinforcement learning (RL). Existing co-segmentation tasks mainly rely on deep learning methods, and…
Mining the Weights Knowledge for Optimizing Neural Network Structures
Mengqiao Han, Xiabi Liu, Zhaoyang Hai +1
Knowledge embedded in the weights of the artificial neural network can be used to improve the network structure, such as in network compression. However, the knowledge is set up by…
A Novel Structured Natural Gradient Descent for Deep Learning
Weihua Liu, Xiabi Liu
Natural gradient descent (NGD) provided deep insights and powerful tools to deep neural networks. However the computation of Fisher information matrix becomes more and more difficu…
A Dense Siamese U-Net trained with Edge Enhanced 3D IOU Loss for Image Co-segmentation
Xi Liu, Xiabi Liu, Huiyu Li +1
Image co-segmentation has attracted a lot of attentions in computer vision community. In this paper, we propose a new approach to image co-segmentation through introducing the dens…
Explore the Knowledge contained in Network Weights to Obtain Sparse Neural Networks
Mengqiao Han, Xiabi Liu, Zhaoyang Hai +1
Sparse neural networks are important for achieving better generalization and enhancing computation efficiency. This paper proposes a novel learning approach to obtain sparse fully…
Improving Image co-segmentation via Deep Metric Learning
Zhengwen Li, Xiabi Liu
Deep Metric Learning (DML) is helpful in computer vision tasks. In this paper, we firstly introduce DML into image co-segmentation. We propose a novel Triplet loss for Image Segmen…