实验室博士生黄梦珂在图像显著性检测上的工作、博士生李恭杨在基于注视点分割上的工作以及博士生任静茹在协同显著性检测上的工作近期被国际期刊Neurocomputing（SCI二区，IF=4.072）陆续录用和发表。三篇论文分别针对图像显著性检测提出了一种端到端的多级特征整合和多尺度特征融合的深度卷积神经网络[1]；提出了一种基于人类视觉系统的神经网络，将基于注视点的分割转移到基于点击的交互式分割[2]；以及提出了一种基于多层卷积特征与图像间传播的协同显著性检测模型[3]。

[1]M. Huang, Z. Liu*, L. Ye, X. Zhou, and Y. Wang, “Saliency detection via multi-level integration and multi-scale fusion neural networks,” Neurocomputing, vol. 364, pp. 310-321, Oct. 2019.
Link：https://www.sciencedirect.com/science/article/pii/S092523121931029X

[2] G. Li, Z. Liu*, R. Shi, and W. Wei, “Constrained fixation point based segmentation via deep neural network,” Neurocomputing, doi: 10.1016/j.neucom.2019.08.051, Aug. 2019.
Link：http://www.sciencedirect.com/science/article/pii/S0925231219311890

[3] J. Ren, Z. Liu*, X. Zhou, C. Bai, and G. Sun, “Co-saliency detection via integration of multi-layer convolutional features and inter-image propagation,” Neurocomputing,doi: 10.1016/j.neucom.2019.09.010,Sept. 2019.
Link：https://www.sciencedirect.com/science/article/pii/S0925231219312706