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| 1 | Pre-course student performance prediction with multi-instance multi-label learning显示文摘Dear editor,Studying courses is one of the most basic and important tasks for college students.For each new course,the initial period of learning is crucial for students,and seriously influences subsequent learning activities.However,given a large number of classes in universities,it has become impossible for teachers to keep track of the individual performance of each student.In these circumstances,it is desirable to predict each student’s performance on a certain course prior to its commencement. | Yuling MA Chaoran CUI Xiushan NIE Gongping YANG Kashif SHAHEED Yilong YIN | 2019 | Science China(Information Sciences)2019，62，2： | 5 |
| 2 | Robust video hashing based on representative-dispersive frames显示文摘This study proposes a robust video hashing for video copy detection.The proposed method,which is based on representative-dispersive frames(R-D frames),can reveal the global and local information of a video.In this method,a video is represented as a graph with frames as vertices.A similarity measure is proposed to calculate the weights between edges.To select R-D frames,the adjacency matrix of the generated graph is constructed,and the adjacency number of each vertex is calculated,and then some vertices that represent the R-D frames of the video are selected.To reveal the temporal and spatial information of the video,all R-D frames are scanned to constitute an image called video tomography image,the fourth-order cumulant of which is calculated to generate a hash sequence that can inherently describe the corresponding video.Experimental results show that the proposed video hashing is resistant to geometric attacks on frames and channel impairments on transmission. | NIE XiuShan LIU Ju SUN JianDe WANG LianQi YANG XiaoHui | 2013 | Science China(Information Sciences)2013，56，6： | 3 |
| 3 | Robust video Hashing based on double-layer embedding显示文摘 | NIE Xiushan LIU Ju SUN Jiande | 2011 | IEEE Signal Processing Letters2011，18，5： | 1 |
| 4 | Key-frame based robust video Hashing using isometric feature mapping显示文摘 | NIE Xiushan LIU Ju SUN Jiande | 2011 | Journal of Computational Information Systems2011，7，6： | 1 |
| 5 | Coarse-to-Fine Video Instance Segmentation With Factorized Conditional Appearance Flows显示文摘We introduce a novel method using a new generative model that automatically learns effective representations of the target and background appearance to detect,segment and track each instance in a video sequence.Differently from current discriminative tracking-by-detection solutions,our proposed hierarchical structural embedding learning can predict more highquality masks with accurate boundary details over spatio-temporal space via the normalizing flows.We formulate the instance inference procedure as a hierarchical spatio-temporal embedded learning across time and space.Given the video clip,our method first coarsely locates pixels belonging to a particular instance with Gaussian distribution and then builds a novel mixing distribution to promote the instance boundary by fusing hierarchical appearance embedding information in a coarse-to-fine manner.For the mixing distribution,we utilize a factorization condition normalized flow fashion to estimate the distribution parameters to improve the segmentation performance.Comprehensive qualitative,quantitative,and ablation experiments are performed on three representative video instance segmentation benchmarks(i.e.,YouTube-VIS19,YouTube-VIS21,and OVIS)and the effectiveness of the proposed method is demonstrated.More impressively,the superior performance of our model on an unsupervised video object segmentation dataset(i.e.,DAVIS19)proves its generalizability.Our algorithm implementations are publicly available at http://gffzz188fe103f8f1460asw9n6qopf9bf96pq6.ffgz.tsg.suse.edu.cn/zyqin19/HEVis. | Zheyun Qin Xiankai Lu Xiushan Nie Dongfang Liu Yilong Yin Wenguan Wang | 2023 | IEEE/CAA Journal of Automatica Sinica2023，10，5： | 1 |
| 6 | Spherical torus-based video hashing for near-duplicate video detection显示文摘Dear editor,With the rapid development of multimedia technologies,users can easily generate and share multiple videos through the Internet.Similarly,numerous illegal and useless near-duplicate videos generated through simple reformatting,transformation,and editing appear on the web.These nearduplicate videos inconvenience users when they are surfing the Internet and are considered a copyright infringement.Robust video hashing,which is also called video fingerprinting,does not require access to the video contents at the time of creation and can be used to detect existing contents.In general, | Xiushan NIE Yane CHAI Ju LIU Jiande SUN Yilong YIN | 2016 | Science China(Information Sciences)2016，59，5： | 1 |
| 7 | Difficulty-aware bi-network with spatial attention constrained graph for axillary lymph node segmentation显示文摘Axillary lymph node(ALN)segmentation in ultrasound images is important for the diagnosis and treatment of breast cancer.Recently,deep learning methods for automatic medical image segmentation have improved significantly.However,two problems arise.(1)A unified model is often employed to segment all images without considering the difficulty diversity.(2)The relationship between elements in the learned class probability map is disregarded.To address these two issues,we propose a novel difficulty-aware binetwork with a spatial attention constrained graph.First,a difficulty grading module(DGM)is developed to learn the difficulty grade of input images.Based on the difficulty grade of images,a novel bi-network architecture is proposed to segment the image adaptively using different branches.In complex branches,a novel spatial attention module(SAM)and graph-based energy with spatial attention constraint are proposed.The learned spatial attention map can provide additional discriminative information.Moreover,the graph-based segmentation framework can capture the relationship between pixels,further improving the segmentation performance for complex images.We conducted an experiment on our ultrasound database using 216 cases.The overall dice similarity coefficient,Jaccard coefficient,volumetric overlap error,and false positive rate are 83.41%,74.4%,12.02%,and 13.36%for ALN segmentation,respectively.The comparison results demonstrated that the proposed method outperforms other deep learning methods. | Qing XU Xiaoming XI Xianjing MENG Zheyun QIN Xiushan NIE Yongjian WU Dongsheng ZHOU Yi QU Chenglong LI Yilong YIN | 2022 | Science China(Information Sciences)2022，65，9： | 0 |
| 8 | Learned local similarity prior embedding active contour model for choroidal neovascularization segmentation in optical coherence tomography images显示文摘Dear editor,Choroidal neovascularization(CNV)is characterized by the growth of new blood vessels in the choroid layer.These new blood vessels break beneath the retina and damage the surrounding reti- | Xiaoming XI Xianjing MENG Lu YANG Xiushan NIE Zhilou YU Chunyun ZHANG Haoyu CHEN Yilong YIN Xinjian CHEN | 2018 | Science China(Information Sciences)2018，61，9： | 0 |
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