In this paper, we consider transferring the structure information from large networks to small ones for dense prediction tasks. Previous knowledge distillation strategies used for dense prediction tasks often directly borrow the distillation scheme for image classification and perform knowledge distillation for each pixel separately, leading to sub-optimal performance. Here we propose to distill structured knowledge from large networks to small networks, taking into account the fact that dense prediction is a structured prediction problem. Specifically, we study two structured distillation schemes: i) pair-wise distillation that distills the pairwise similarities by building a static graph; and ii) holistic distillation that uses adversarial training to distill holistic knowledge. The effectiveness of our knowledge distillation approaches is demonstrated by extensive experiments on three dense prediction tasks: semantic segmentation, depth estimation and object detection.

In this paper, we propose a novel method called Rotational Region CNN (R 2 CNN) for detecting arbitrary-oriented texts in natural scene images. The framework is based on Faster R-CNN [1] architecture. First, we use the Region Proposal Network (RPN) to generate axis-aligned bounding boxes that enclose the texts with different orientations. Second, for each axis-aligned text box proposed by RPN, we extract its pooled features with different pooled sizes and the concatenated features are used to simultaneously predict the text/non-text score, axis-aligned box and inclined minimum area box. At last, we use an inclined non-maximum suppression to get the detection results. Our approach achieves competitive results on text detection benchmarks : ICDAR 2015 and ICDAR 2013.

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Scene text detection attracts much attention in computer vision, because it can be widely used in many applications such as real-time text translation, automatic information entry, blind person assistance, robot sensing and so on. Though many methods have been proposed for horizontal and oriented texts, detecting irregular shape texts such as curved texts is still a challenging problem. To solve the problem, we propose a robust scene text detection method with adaptive text region representation. Given an input image, a text region proposal network is first used for extracting text proposals. Then, these proposals are verified and refined with a refinement network. Here, recurrent neural network based adaptive text region representation is proposed for text region refinement, where a pair of boundary points are predicted each time step until no new points are found. In this way, text regions of arbitrary shapes are detected and represented with adaptive number of boundary points. This gives more accurate description of text regions. Experimental results on five benchmarks, namely, CTW1500, TotalText, ICDAR2013, ICDAR2015 and MSRA-TD500, show that the proposed method achieves state-ofthe-art in scene text detection.

digital data processors, but others remain time-consuming. In particular, the rapidly increasing volume of image data as well as increasingly challenging computational tasks have become important driving forces for further improving the efficiency of image processing and analysis.Quantum information processing (QIP), which exploits quantum-mechanical phenomena such as quantum superpositions and quantum entanglement [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23], allows one to overcome the limitations of classical computation and reaches higher computational speed for certain problems like factoring large numbers [24,25] , searching an unsorted database [26], boson sampling [27][28][29][30][31][32], quantum simulation [33-40], solving linear systems of equations [41][42][43][44][45], and machine learning [46][47][48]. These unique quantum properties, such as quantum superposition and quantum parallelism, may also be used to speed up signal and data processing [49,50]. For quantum image processing, quantum image representation (QImR) plays a key role, which substantively determines the kinds of processing tasks and how well they can be performed. A number of QImRs [51-54] have been discussed.In this article, we demonstrate the basic framework of quan-arXiv:1801.01465v1 [quant-ph]

BackgroundThe efficacy of peer support in Chinese diabetes patients is still uncertain. The purpose of this study was to observe the effects of a peer support program on the outcomes of patients with type 2 diabetes who received community-based insulin therapy in rural communities of central China.Material/MethodsTwo hundred and eight eligible patients with type 2 diabetes were randomly assigned into the traditional training group (control group, n=111) and peer support intervention group (peer group, n=97) between June 2013 and January 2014 in 2 rural communities of Jingzhou area, China. Both groups received 3-month traditional training, followed by another 4-month traditional training or peer support training, respectively. At baseline and 7 months after treatment, the blood glycemic level was evaluated by biochemical detection. Capacities of self-management and knowledge related to insulin usage were assessed by questionnaire survey.ResultsNinety-seven and ninety patients completed this study in the control group and peer group, respectively. There was no significant difference in age, gender, diabetes duration, insulin usage time, and complications between the 2 groups at baseline (P>0.05). Compared with the control group, peer group patients achieved a more significant decrease in blood glycosylated hemoglobin levels (P<0.05), increase in knowledge related to insulin usage, and increase of diabetes self-management ability (P<0.05).ConclusionsPeer support intervention effectively improves outcomes of patients with type 2 diabetes in rural communities of central China.