57 citations · 69 across the 16 of their papers we have counts for
7 papers · 1 filter
LCCo: Lending CLIP to Co-Segmentation
Xin Duan, Yan Yang, Liyuan Pan +1
This paper studies co-segmenting the common semantic object in a set of images. Existing works either rely on carefully engineered networks to mine the implicit semantic informatio…
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…
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…
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…
PiaNet: A pyramid input augmented convolutional neural network for GGO detection in 3D lung CT scans
Weihua Liu, Xiabi Liua, Xiongbiao Luo +4
This paper proposes a new convolutional neural network with multiscale processing for detecting ground-glass opacity (GGO) nodules in 3D computed tomography (CT) images, which is r…
Contraction Mapping of Feature Norms for Classifier Learning on the Data with Different Quality
Weihua Liu, Xiabi Liu, Murong Wang +1
The popular softmax loss and its recent extensions have achieved great success in the deep learning-based image classification. However, the data for training image classifiers usu…
Automatic Image Co-Segmentation: A Survey
Xiabi Liu, Xin Duan
Image co-segmentation is important for its advantage of alleviating the ill-pose nature of image segmentation through exploring the correlation between related images. Many automat…