代表性学术论文及被引用情况

项目代表性学术论文及被引用情况
本项目精选出5篇代表性论学术论文，列示如下：
[1]Nihong Chen, Peng Cai,Tiangang Zhou, Benjamin Thompson, Fang Fang.
Perceptual Learning Modifies the Functional Specializations of Visual Cortical Areas. Proceedings of the National Academy of Sciences, 2016, 113(20): 5724-5729. (Google Scholar他引2次) (课题一)
[2]Zhiwu Huang, Ruiping Wang, Shiguang Shan, Xilin Chen. Projection Metric
Learning on Grassmann Manifold with Application to Video based Face Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 140-149, 2015. (Google Scholar他引22次).(课题二)
[3]Xinmei Tian, Zhe Dong, Kuiyuan Yang, Tao Mei. Query-dependent aesthetic
model with deep learning for photo quality assessment. IEEE Transactions on Multimedia, 17(11): 2035-2048, 2015.(Google Scholar他引1次).(课题三)
[4]Xianming Liu, Xiaolin Wu, Jiantao Zhou, Debin Zhao. Data-driven sparsity-based
restoration of jpeg-compressed images in dual transform-pixel domain.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5171-5178, 2015. (Google Scholar他引8次).(课题四)
[5]Jian Zhang, Ruiqin Xiong, Chen Zhao, Yongbing Zhang, Siwei Ma, Wen Gao.
CONCOLOR: Constrained Non-Convex Low-Rank Model for Image Deblocking.
IEEE Transactions on Image Processing, 25(3): 1246-1259. (Google Scholar他引2次).(课题五)
附：论文全文及典型引用和评价
课题1典型引文
1)加州大学洛杉矶分校教授 Zili Liu在其发表于 Brain Simulation 2016上
的关于视觉运动感知的文章中（见附件，典型引文1）指出：“Effects of V1 and V5 TMS on the performance of visual tasks have been reported using both online and offline stimulation protocols, .g. Refs. [21–28].”
第一篇论文的作者承认本论文中使用的TMS实验方法的正确性以及有效性，并在他们的工作中借鉴了本论文中使用的方法。

(其中，“[23]”为我们的代表性论文[1])
2)布朗大学教授Takeo Watanabe在其发表于 PNAS 2016上的关于 V3A 区视觉
功能替代的文章中（见附件，典型引文2）指出“The recent study by Chen et al. in PNAS (2) advances the understanding by showing that transfer of perceptual learning of 100% coherent motion to noisy motion is associated with dramatic changes in involved neural sites.”
以及“Although initial studies of perceptual learning showed changes in local circuits involved in the trained feature, recent studies including the one by Chen et al. (2) have indicated that perceptual learning is also associated with a larger-scale changes.”（参考译文：作者Sasaki Y 和 Watanabe T. 认为在本论文之前，视觉任务中成人大脑的可塑性的神经机制尚未被完全理解。

而本论文的研究很大程度上更新了人们对该领域的认知和理解，是具有创新性和突破性的工作。

）(其中，“（2）”为我们的代表性论文[1])
课题2典型引文
1)美国马里兰大学教授Rama Chellappa(曾任IEEE T-PAMI主编，ACM/IEEE/
IAPR Fellow，IEEE CVPR 2017大会主席)在其发表于IEEE WACV2016上的视频人脸识别的文章中（见附件，典型引文1），将我们PML/DARG两个工作列入当前最好的代表性方法（“Wealso compared with other state-of-the-art methods in [17]and [33].”）进行了对比(其中，“[17]”为我们的代表性论文[2]，“[33]”为本项目资助发表的另一篇相关论文)。

2)意大利佛罗伦萨大学的StefanoBerretti教授在其发表于Pattern
Recognition 2016上关于人脸形状分析的文章中（见附件，典型引文2）指出“More recently, Huang et al. [13] proposed learning projection distance on Grassmann manifold for face recognition from image sets.
In this work, an improved recognition is obtained by representing every image set using a Gaussian distribution over the manifold.”（参考译文：Huang等人在[13]中提出在Grassmann流形上进行投影距离学习并用于图像集合人脸识别。

该工作通过将图像集合表示为流形上的高斯分布，取得了改进的识别性能。

）(其中，“[13]”为我们的代表性论文[2])
3)中科院自动化所谭铁牛院士在其发表于AAAI2016上关于图像集合分类的文
章中（见附件，典型引文3），将我们PML/DARG/SGM三个工作列为当前最好的代表性方法（“we compare the proposed method withstate-of-the-art image-set classification methods, including,regularized nearest points (RNP) (Yang et al. 2013),mean sequence sparse
representation-based classification(MSSRC) (Ortiz, Wright, and Shah 2013), single Gaussianmodel (SGM) (Huang et al. 2015a), projection metric learning(PML) (Huang et al. 2015b), discriminant analysis onRiemannian Manifold (DARG) (2015), and Deep ReconstructionModel (DRM) (2015).”）进行了对比(其中，“Huang et al. 2015b”为我们的代表性论文[2]，“Huang et al. 2015a”和“(DARG) (2015)”为本项目资助发表的另两篇相关论文)。

课题3典型引文
1)马来西亚多媒体大学的研究人员在其发表于Multimedia Tools And
Applications的图像美学质量评价综述的文章中（见附件，典型引文1）指出我们所提出的方法显著且一致优于传统方法，取得了目前最高的精确度。

原文是“Motivated by thefact that photographers employ different rules for capturing different images, [35] movedaway from the universal model paradigm and proposed a query-dependent aesthetics modelwith deep learning for aesthetics assessment. Extensive experiments demonstrate that theirnovel query-dependent approach significantly and consistently outperforms the conventionaluniversal scheme. Their model achieved an accuracy of 80.38 % on AVA dataset,which is considered the highest accuracy among the state-of-the-arts techniques.”(其中，“[35]”为我们的代表性论文[3])。

课题4典型引文
1)美国伊利诺依大学（香槟分校）Beckman研究院图像实验室主任Thomas S.
Huang教授（美国IEEE、美国光学学会、国际光学学会（SPIE）和国际模式识别学会Fellow）在其发表于IEEE CVPR2016 上的压缩图象恢复文章中（见附件，典型引文1），将我们的代表性论文高度评价（“Our work is inspired by the prior wisdom in [24]. ”），指出“ Our major innovation is to explicitly combine both the prior knowledge in the JPEG compression scheme and the successful practice of dual-domain sparse coding [24]”（参考译文：我们的主要创新来自于JPEG图像压缩先验，以及在双域稀疏编码上取得的成功实践
[24]）；并对我们代表性论文工作做出介绍“We first review the sparsity-based
dual-domain restoration model established in [24]. ”，并进行对比“We include the following two relevant, state-of-the-art methods for comparison: Sparsity-based Dual-Domain Method (S-D2) [24]，could be viewed as the “shallow” counterpart of D3. Ithas outperformed most traditional methods [24], suchas BM3D [9] and DicTV [7]”（参考译文：我们对如下两个相关的、目前最好的方法进行了对比：基于稀疏的双域方法(S-D2) [24]，可以看作是一个“浅”的D3；效果超过传统的方法，如BM3D [9]和DicTV [7]）(其中，“(S-D2) [24]”为我们的代表性论文[3])。

2)加拿大麦克马斯特大学，Xiaolin Wu 教授在其在线发表关于压缩图象恢复的
文章中（见附件，典型引文2），将我们的代表性论文中的工作列为当前最好的代表性方法并进行了对比，“we compare the results with the state-of-the-art
denoising and JPEG artifact removal techniques. The comparison group is composed of the following methods: one JPEG deblocking method: the ACR algorithm [22]; two denoising methods: the BM3D algorithm [5] and WNNM algorithm [8]; and three JPEG soft-decoding methods: the TV algorithm [2], DicTV [4] algorithm and DTPD algorithm [12].”（参考译文：我们和目前最好的去噪方法及JPEG压缩恢复方法进行对比：一种JPEG去块效应方法，ACR 算法[22]；两种去噪方法：BM3D[5]和WNNM方法[8]；三种JPEG软解码方法：TV[2]，DicTV[4]，DTPD[12]）(其中，“DTPD algorithm [12]”为我们的代表性论文[3])。

3)日本国立情报学研究院Gene Cheung副教授在其发表于IEEE SIGNAL
PROCESSING LETTERS 上关于压缩图象恢复的文章中（见附件，典型引文3）指出“[3]–[5] assume a sparse signal model, where the targeted block is approximated as a sparse combination of atoms from an over-complete dictionary. ”（参考译文：文献[3]–[5]假设了一个稀疏模型，目标图像块被近似为过完备字典元素的稀疏组合表达）(其中，“[5]”为我们的代表性论文[3])。

课题5典型引文
1)印度安纳大学N.Kumaratharan教授在其发表于National Conference on
Communication and Informatics上关于图像去噪的文章中引用我们文章中的结论，“The BM3D smooths artifacts caused byspreading the same values of uncorrupted pixels to the wideneighborhood in highly corrupted images [12].”
（参考译文：BM3D算法能够消除一些在高度失真图像中因为误差传递造成的失真[12]）（其中“[12]为我们的代表性论文[5]”）。

黑山共和国大学Igor Djurovic在其发表于EURASIP Journal on Image and Video Processing上关于图像去噪的文章中引用我们文章中的结论，“The BM3D looks for similar patches in suchimages and filters them together, reducing both effects ofthe artifacts and errors related to the edges [18]”（参考译文：BM3D 算法通过在图像中寻找相似块共同滤波，能够有效去除在边缘处的噪声和失真。

）（其中“[18]为我们的代表性论文[5]”）。