output
20032026
most citedStrong Photoluminescence Enhancement of MoS2 through Defect Engineering and Oxygen Bonding

1.2k citations

Showing 2020 · cs.CVShow all

8 papers · 2 filters

cs.CV2020

Superpixel Segmentation Based on Spatially Constrained Subspace Clustering

Hua Li, Yuheng Jia, Runmin Cong +3

Superpixel segmentation aims at dividing the input image into some representative regions containing pixels with similar and consistent intrinsic properties, without any prior know…

cs.CV202021 cited

BGGAN: Bokeh-Glass Generative Adversarial Network for Rendering Realistic Bokeh

Ming Qian, Congyu Qiao, Jiamin Lin +4

A photo captured with bokeh effect often means objects in focus are sharp while the out-of-focus areas are all blurred. DSLR can easily render this kind of effect naturally. Howeve…

cs.CV2020113 cited

AdaFuse: Adaptive Multiview Fusion for Accurate Human Pose Estimation in the Wild

Zhe Zhang, Chunyu Wang, Weichao Qiu +2

Occlusion is probably the biggest challenge for human pose estimation in the wild. Typical solutions often rely on intrusive sensors such as IMUs to detect occluded joints. To make…

cs.CV2020

A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition

Yang Li, Boxun Fu, Fu Li +2

The existed methods for electroencephalograph (EEG) emotion recognition always train the models based on all the EEG samples indistinguishably. However, some of the source (trainin…

cs.CV20208 cited

Deep Image Clustering with Category-Style Representation

Junjie Zhao, Donghuan Lu, Kai Ma +2

Deep clustering which adopts deep neural networks to obtain optimal representations for clustering has been widely studied recently. In this paper, we propose a novel deep image cl…

cs.CV20206 cited

Marginal loss and exclusion loss for partially supervised multi-organ segmentation

Gonglei Shi, Li Xiao, Yang Chen +1

Annotating multiple organs in medical images is both costly and time-consuming; therefore, existing multi-organ datasets with labels are often low in sample size and mostly partial…