-澳门游戏娱乐场棋牌

��<html xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="urn:schemas-microsoft-com:office:word" xmlns:m="http://schemas.microsoft.com/office/2004/12/omml" xmlns="http://www.w3.org/tr/rec-html40"> <head> <meta http-equiv=content-type content="text/html; charset=unicode"> <meta name=progid content=word.document> <meta name=generator content="microsoft word 15"> <meta name=originator content="microsoft word 15"> <link rel=file-list href="2019.files/filelist.xml"> <link rel=edit-time-data href="2019.files/editdata.mso">  <title>2019��e</title>  <link rel=themedata href="2019.files/themedata.thmx"> <link rel=colorschememapping href="2019.files/colorschememapping.xml">  <style>  </head> <body lang=zh-cn link="#0563c1" vlink="#954f72" style='tab-interval:21.0pt'> <div class=wordsection1> <img width=1002 height=120 id="_x0000_i1025" src="../nlpr.jpg"><o:p></o:p> <div align=center> <table class=msonormaltable border=1 cellspacing=0 cellpadding=0 width=1117 style='width:838.0pt;border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt; mso-yfti-tbllook:1184;mso-padding-alt:0cm 0cm 0cm 0cm'> <colgroup><col width="1117" style="width: 838pt"></colgroup> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes;height:15.0pt'> <td width=1117 style='width:838.0pt;border:solid windowtext 1.0pt;border-bottom: none;mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windowtext .5pt; mso-border-right-alt:solid windowtext .5pt;padding:.75pt .75pt 0cm .75pt; height:15.0pt'> 2019��e�vu_ <o:p></o:p> </td> </tr> <tr style='mso-yfti-irow:1;height:15.0pt'> <td width=1117 style='width:838.0pt;border-top:none;border-left:solid windowtext 1.0pt; border-bottom:none;border-right:solid windowtext 1.0pt;mso-border-left-alt: solid windowtext .5pt;mso-border-right-alt:solid windowtext .5pt;padding: .75pt .75pt 0cm .75pt;height:15.0pt'>  list of publications<o:p></o:p> </td> </tr> <tr style='mso-yfti-irow:2;mso-yfti-lastrow:yes;height:15.0pt'> <td width=1117 style='width:838.0pt;border:solid windowtext 1.0pt;border-top: none;mso-border-left-alt:solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; mso-border-right-alt:solid windowtext .5pt;padding:.75pt .75pt 0cm .75pt; height:15.0pt'>   <o:p></o:p> <a name="_toc40189707" id="_toc532562395"></a><a name="_toc24098141" id="_toc532562225"></a><a name="_toc532562225" id="_toc532391194"></a><a name="_toc532391194">�n �w�</a><a name="_toc24465890"></a>books / chapters<o:p></o:p> <![if !supportlists]>1.         <![endif]>sergio escalera, stephane ayache, jun wan, meysam madadi, umut guclu, xavier baro(eds.), inpainting and denoising challenges, isbn:978-3-030-25613-5, springer <![if !supportlists]>2.         <![endif]>�[b�^�yw� _�[�o��e,gpencc�c�2019�nns'yf[�qhr>y <![if !supportlists]>3.         <![endif]>xu-yao zhang, yi-chao, fei yin, cheng-lin liu, deep learning based handwritten chinese character and text recognition, in: k. huang, a. hussain, q.-f. wang, r. zhang (eds.), deep learning: fundamentals, theory and applications, 2019, pp.58-88, springer. <o:p> </o:p> <a name="_toc40189708"></a><a name="_toc24098142"></a><a name="_toc532562227"></a><a name="_toc532391196">�ve� rir</a><a name="_toc24465891"></a><a name="_toc24360999"></a><a name="_toc532562397"></a>international journals<o:p></o:p> <a name="_toc40189709"></a><a name="_toc24098143"></a><a name="_toc532562228"></a><a name="_toc532391197">��{:gɖɉ</a><a name="_toc24465892"></a><a name="_toc24361000"></a><a name="_toc532562398">computer vision</a><o:p></o:p> <![if !supportlists]>1.         <![endif]>fuzhang wu, yan kong, weiming dong, yanjun wu, gradient-aware blind face inpainting for deep face verification. neurocomputing, vol. 331, pp. 301-311, 2019. <![if !supportlists]>2.         <![endif]>yucheng zhao, fan tang, weiming dong, feiyue huang, xiaopeng zhang, joint face alignment and segmentation via deep multi-task learning, multimedia tools and applications, vol. 78, no. 10, pp. 13131-13148, 2019. <![if !supportlists]>3.         <![endif]>wangli hao, zhaoxiang zhang, spatiotemporal distilled dense-connectivity network for video action recognition, pattern recognition, vol.92, pp. 13-24, 2019. <![if !supportlists]>4.         <![endif]>guibo zhu, zhaoxiang zhang, jinqiao wang, yi wu, hanqing lu, dynamic collaborative tracking, ieee transactions on neural networks and learning systems (tnnls), vol.30, pp. 3035-3046, 2019. <![if !supportlists]>5.         <![endif]>xinchu shi, haibin ling, yu pang, weiming hu, peng chu, and junliang xing, rank-1 tensor approximation for high-order association in multi-target tracking, international journal of computer vision (ijcv), vol. 127, no. 8, pp. 1063-1083, 2019. <![if !supportlists]>6.         <![endif]>hainan cui, shuhan shen, wei gao, hongmin liu, zhiheng wang. efficient and robust large-scale structure-from-motion via track selection and camera prioritization . isprs journal of photogrammetry and remote sensing, vol. 156, october 2019, pp.202-214. <![if !supportlists]>7.         <![endif]>jianwei li, wei gao, yihong wu, high-quality 3d reconstruction with depth super-resolution and completion , ieee access, no. 7, pp. 19370-19381, 2019. <![if !supportlists]>8.         <![endif]>yangdong liu, wei gao, zhanyi hu, 3d scanning of high dynamic scenes using an rgb-d sensor and an imu on a mobile device , ieee access, no. 7, pp. 24057-24070, 2019. <![if !supportlists]>9.         <![endif]>wei wang, wei gao, efficient multi-plane extraction from massive 3d points for modeling large-scale urban scenes , the visual computer, vol. 35, no. 5, pp. 625-638, 2019. <![if !supportlists]>10.     <![endif]>xiang gao, shuhan shen, zhanyi hu, zhiheng wang. ground and aerial meta-data integration for localization and reconstruction: a review. pattern recognition letters, 127: 202-214, 2019. <![if !supportlists]>11.     <![endif]>yang zhou, shuhan shen, zhanyi hu. detail preserved surface reconstruction from point cloud. sensors, 19(6): 1278, 2019. <![if !supportlists]>12.     <![endif]>yihong wu, haoren wang, fulin tang, zhiheng wang. efficient conic fitting with a polar-n-direction geometric distance. pattern recognition, vol. 90, pp. 415-423, 2019. <![if !supportlists]>13.     <![endif]>xiaomei zhao, yihong wu. automatically extract semi-transparent motion-blurred hand from a single image. ieee trans. on signal processing letters, vol. 26, no. 11, pp. 1598- 1602, 2019. <![if !supportlists]>14.     <![endif]>zichang tan, yang yang, jun wan*, hanyuan huang, guodong guo and stan z. li, "attention based pedestrian attribute analysis", ieee transactions on image processing (tip), vol 28, no. 12, 6126-6140, 2019. <![if !supportlists]>15.     <![endif]>xiangyu zhu, hao liu, zhen lei, hailin shi, fan yang, dong yi, guojun qi, stan z. li. large-scale bisample learning on id versus spot face recognition. international journal of computer vision (ijcv), 2019, 127(6-7): 684-700. <![if !supportlists]>16.     <![endif]>zhiwei liu, xiangyu zhu, ming tang, zhen lei, jinqiao wang.efficient face alignment with fast normalization and contour fitting loss,acm transactions on multimedia computing, communications, and applications (tomm), 2019, 15: 89. <![if !supportlists]>17.     <![endif]>xinchu shi, haibin ling, yu pang, weiming hu, peng chu, and junliang xing, rank-1 tensor approximation for high-order association in multi-target tracking, international journal of computer vision (ijcv), vol. 127, no. 8, pp. 1063-1083, 2019. <![if !supportlists]>18.     <![endif]>yunze gao, yingying chen, jinqiao wang, ming tang, hanqing lu, reading scene text with fully convolutional sequence modeling. neurocomputing 339: 161-170, 2019 <![if !supportlists]>19.     <![endif]>guibo zhu, jinqiao wang, peisong wang, yi wu, hanqing lu, feature distilled tracking. ieee trans. cybernetics 49(2): 440-452, 2019 <![if !supportlists]>20.     <![endif]>xiyan liu, gaofeng meng and chunhong pan. scene text detection and recognition with advances in deep learning: a survey[j]. international journal on document analysis and recognition (ijdar), 2019, 22(2): 143-162. <![if !supportlists]>21.     <![endif]>yabei li, zhang zhang, yanhua cheng, liang wang and tieniu tan, mapnet: multi-modal attentive pooling network for rgb-d indoor scene classification, pattern recognition, vol. 90, pp. 436-449, 2019. <![if !supportlists]>22.     <![endif]>huaibo huang, ran he, zhenan sun and tieniu tan, wavelet domain generative adversarial network for multi-scale face hallucination, international journal of computer vision, vol. 127, no. 6-7, pp. 763-784, 2019. <![if !supportlists]>23.     <![endif]>feng yu, qiang liu, shu wu, liang wang and tieniu tan, attention-based convolutional approach for misinformation identification from massive and noisy microblog posts, computers & security, vol. 83, pp. 106-121, 2019. <![if !supportlists]>24.     <![endif]>yupei wang, xin zhao, xuecai hu, yin li, kaiqi huang, focal boundary guided salient object detection, ieee trans. on image processing, vol. 28, no. 6, pp. 2813-2824, 2019. <![if !supportlists]>25.     <![endif]>dangwei li, zhang zhang, xiaotang chen, kaiqi huang, a richly annotated pedestrian dataset for person retrieval in real surveillance scenarios, ieee trans. on image processing, vol. 28, no. 4, pp. 1575-1590, 2019. <![if !supportlists]>26.     <![endif]>da li, zhang zhang, kai yu, kaiqi huang and tieniu tan, isee: an intelligent scene exploration and evaluation platform for large-scale visual surveillance, ieee trans. on parallel and distributed systems, vol. 30, no.12, pp. 2743-2758. <o:p> </o:p> <a name="_toc40189710"></a><a name="_toc24098144"></a><a name="_toc532562229"></a><a name="_toc532391198">�va�/</a>ɖ��ytnr�g<a name="_toc24465893"></a><a name="_toc24361001"></a><a name="_toc532562399">image/video processing </a>and analysis<o:p></o:p> <![if !supportlists]>27.     <![endif]>changde du, changying du, lijie huang, huiguang he, reconstructing perceived images from human brain activities with bayesian deep multiview learning , ieee trans. neural netw. learning syst, 30(8): page(s): 2310-2323, 2019. <![if !supportlists]>28.     <![endif]>zhao zhong, zichen yang, wentao feng, wei wu, yangyang hu, cheng-lin liu, decision controller for object tracking with deep reinforcement learning , ieee access, 7: 28069-28079, 2019. <![if !supportlists]>29.     <![endif]>yingying chen, jinqiao wang, bingke zhu, ming tang, hanqing lu, pixelwise deep sequence learning for moving object detection. ieee trans. circuits syst. video techn. 29(9): 2567-2579, 2019 <![if !supportlists]>30.     <![endif]>songyan liu, chaoyang zhao, yunze gao, jinqiao wang, ming tang, adversarial image generation by combining content and style, ieee transactions on image processing, 13(14): 2716-2723, 2019 <![if !supportlists]>31.     <![endif]>congqi cao, cuiling lan, yifan zhang, wenjun zeng, hanqing lu, yanning zhang, skeleton-based action recognition with gated convolutional neural networks. ieee trans. circuits syst. video techn. 29(11): 3247-3257, 2019 <![if !supportlists]>32.     <![endif]>yousong zhu, chaoyang zhao, haiyun guo, jinqiao wang, xu zhao, hanqing lu, attention couplenet: fully convolutional attention coupling network for object detection. ieee trans. image processing 28(1): 113-126, 2019 <![if !supportlists]>33.     <![endif]>haiyun guo, kuan zhu, ming tang, jinqiao wang, two-level attention network with multi-grain ranking loss for vehicle re-identification, ieee trans. image processing, 28(9): 4328-4338, 2019 <![if !supportlists]>34.     <![endif]>lingfeng wang, chunhong pan: visual object tracking via a manifold regularized discriminative dual dictionary model. pattern recognition 91: 272-280 (2019). <![if !supportlists]>35.     <![endif]>jie gu, gaofeng meng, shiming xiang, chunhong pan: blind image quality assessment via learnable attention-based pooling. pattern recognition 91: 332-344 (2019), if:5.898 <![if !supportlists]>36.     <![endif]>tingzhao yu, lingfeng wang, chaoxu guo, huxiang gu, shiming xiang, chunhong pan: pseudo low rank video representation. pattern recognition 85: 50-59 (2019) <![if !supportlists]>37.     <![endif]>tingzhao yu, lingfeng wang, cheng da, huxiang gu, shiming xiang, chunhong pan: weakly semantic guided action recognition. ieee trans. multimedia 21(10): 2504-2517 (2019) <![if !supportlists]>38.     <![endif]>yunbo wang, jian liang, dong cao, zhenan sun, local semantic-aware deep hashing with hamming-isometric quantization, ieee trans. image processing, vol. 28, no. 6, pp. 2665-2679, 2019. <![if !supportlists]>39.     <![endif]>hongwen zhang, qi li, zhenan sun, adversarial learning semantic volume for 2d/3d face shape regression in the wild, ieee trans. on image processing, vol. 28, no. 9, pp. 4526-4540, 2019. <o:p> </o:p> <a name="_toc40189711"></a><a name="_toc24098145"></a><a name="_toc532562230"></a><a name="_toc532391199">!j_ƌ r</a><a name="_toc24465894"></a><a name="_toc24361002"></a><a name="_toc532562400">pattern recognition</a><o:p></o:p> <![if !supportlists]>40.     <![endif]>lei wang, weiliang meng, runping xi, chengcheng ma, yanning zhang, ling lu and xiaopeng zhang. 3d point cloud analysis and classification in large-scale scene based on deep learning. ieee access. 2019, 7(1): 55649-55658. <![if !supportlists]>41.     <![endif]>hongjun li, weiliang meng, xinying liu, shiming xiang and xiaopeng zhang. parameter optimization criteria guided 3d point cloud classification. multimedia tools and applications.2019, 2019(78): 5081-5104. <![if !supportlists]>42.     <![endif]>hao yang, chunfengyuan, bingli, yang du, junliangxing, weiming hu, and stephen j. maybank, asymmetric 3d convolutional neural networks for action recognition, pattern recognition, vol. 85, pp. 1-12, 2019. <![if !supportlists]>43.     <![endif]>haiqing ren, weiqiang wang, chenglin liu, recognizing online handwritten chinese characters using rnns with new computing architectures , pattern recognition, 93: 179-192, 2019. <![if !supportlists]>44.     <![endif]>xinyu zuo, yubo chen, kang liu and jun zhao, event coreference resolution via a multi-loss neural network without using argument information, science china information sciences2019, vol. 62, no. 11, 2019. <![if !supportlists]>45.     <![endif]>hao yang, chunfengyuan, bingli, yang du, junliangxing, weiming hu, and stephen j. maybank, asymmetric 3d convolutional neural networks for action recognition, pattern recognition, vol. 85, pp. 1-12, 2019. <![if !supportlists]>46.     <![endif]>zhiwei fang, jing liu, yong li, yanyuan qiao, hanqing lu. improving visual question answering using dropout and enhanced question encoder. pattern recognition 90: 404-414, 2019 <![if !supportlists]>47.     <![endif]>lingxiao he, haiqing li, qi zhang, and zhenan sun, dynamic feature matching for partial face recognition , ieee trans. on image processing, vol. 28, no. 2, pp. 791-802, 2019. <![if !supportlists]>48.     <![endif]>man zhang, zhaofeng he, hui zhang, tieniu tan and zhenan sun, toward practical remote iris recognition: a boosting based framework, neurocomputing, vol.330, pp. 238-252, 2019. <![if !supportlists]>49.     <![endif]>yi li, lingxiao song, xiang wu, ran he and tieniu tan, learning a bi-level adversarial network with global and local perception for makeup-invariant face verification, pattern recognition, vol. 90, pp. 99-108, 2019. <![if !supportlists]>50.     <![endif]>jie cao, yibo hu, bing yu, ran he, zhenan sun, 3d aided duet gans for multi-view face image synthesis, ieee trans. information forensics and security, vol. 14, no. 8, pp. 2028-2042, 2019. <![if !supportlists]>51.     <![endif]>jian liang, ran he, zhenan sun and tieniu tan, aggregating randomized clustering-promoting invariant projections for domain adaptation, ieee trans. on pattern analysis and machine intelligence (pami), vol. 41, no. 5, pp. 1027-1042, 2019. <![if !supportlists]>52.     <![endif]>ran he, xiang wu, zhenan sun and tieniu tan, wasserstein cnn: learning invariant features for nir-vis face recognition, ieee trans. on pattern analysis and machine intelligence (pami), vol. 41, no. 7, pp. 1761-1773, 2019. <![if !supportlists]>53.     <![endif]>chunshui cao, yongzhen huang, yi yang, liang wang, zilei wang and tieniu tan, feedback convolutional neural network for visual localization and segmentation, ieee trans. on pattern analysis and machine intelligence (pami), vol. 41, no. 7, pp. 1627-1640, 2019. <![if !supportlists]>54.     <![endif]>chunfeng song, yongzhen huang, yan huang, ning jia, and liang wang, gaitnet: an end-to-end network for video-based human identication, pattern recognition, 2019. <![if !supportlists]>55.     <![endif]>yuqi zhang, yongzhen huang, liang wang, shiqi yu, a comprehensive study on gait biometrics via a joint cnn-based method, pattern recognition, vol. 93, pp. 228-236, 2019. <![if !supportlists]>56.     <![endif]>yanyun wang, chunfeng song, yan huang, zhenyu wang, liang wang, learning view invariant gait features with two-stream gan, neurocomputing, vol. 339, pp. 245-254, 2019. <![if !supportlists]>57.     <![endif]>linjiang huang, yan huang, wanli ouyang, liang wang, part-aligned pose-guided recurrent network for action recognition, pattern recognition, vol. 92, pp. 162-176. <![if !supportlists]>58.     <![endif]>yuwei wang, yi zeng, jianbo tang and bo xu. biological neuron coding inspired binary word embeddings. cognitive computation, springer, 11:676 684, 2019. <o:p> </o:p> <a name="_toc40189712"></a><a name="_toc24098146"></a><a name="_toc532562231"></a><a name="_toc532391200">:ghvf[`n</a><a name="_toc24465895"></a><a name="_toc24361003"></a><a name="_toc532562401">machine learning</a><o:p></o:p> <![if !supportlists]>59.     <![endif]>hanyang peng, cheng-lin liu, discriminative feature selection via employing smooth and robust hinge loss , ieee trans. neural networks and learning systems, 30(3): 788-802, 2019. <![if !supportlists]>60.     <![endif]>ting-bing xu, peipei yang, xu-yao zhang, cheng-lin liu, lightweightnet: toward fast and lightweight convolutional neural networks via architecture distillation , pattern recognition, 88: 272-284, 2019. <![if !supportlists]>61.     <![endif]>xiao-bo jin, xu-yao zhang, kaizhu huang, guang-gang geng, stochastic conjugate gradient algorithm with variance reduction , ieee trans. neural networks and learning systems, 30(5): 1360-1369, 2019. <![if !supportlists]>62.     <![endif]>guibo zhu, zhaoxiang zhang, jinqiao wang, yi wu, hanqing lu, dynamic collaborative tracking. ieee trans. neural netw. learning syst. 30(10): 3035-3046, 2019 <![if !supportlists]>63.     <![endif]>qi zhang, jianlong chang, gaofeng menga, shibiao xua, shiming xiang, chunhong pan. learning graph structure via graph convolutional networks. pattern recognition, 2019: 308-318. <![if !supportlists]>64.     <![endif]>cheng da, gaofeng meng, shiming xiang, kun ding, shibiao xu, qing yang, chunhong pan: nonlinear asymmetric multi-valued hashing. ieee transactions on pattern analysis and machine intelligence, 41(11): 2660-2676 (2019), if: 17.730 <![if !supportlists]>65.     <![endif]>peipei li, yibo hu, ran he, zhenan sun, global and local consistent wavelet-domain age synthesis, ieee trans. on information forensics and security, vol. 14, no. 11, pp. 2943-2957, 2019. <o:p> </o:p> <a name="_toc40189713"></a><a name="_toc24098147"></a><a name="_toc532562232"></a><a name="_toc532391201">pencc�c</a><a name="_toc24465896"></a><a name="_toc24361004"></a><a name="_toc532562402">data mining</a><o:p></o:p> <![if !supportlists]>66.     <![endif]>qiang cui, shu wu, yan huang, liang wang, a hierarchical contextual attention-based network for sequential recommendation, neurocomputing, vol. 358, pp. 141-149, 2019. <o:p> </o:p> <a name="_toc40189714"></a><a name="_toc24098148"></a><a name="_toc532562233"></a><a name="_toc532391202">��{:g�vb_f[</a><a name="_toc24465897"></a><a name="_toc24361005"></a><a name="_toc532562403">computer graphics</a><o:p></o:p> <![if !supportlists]>67.     <![endif]>meng yang, juntao ye, frank ding, yubo zhang, dong-ming yan. a semi-explicit surface tracking mechanism for multi-phase immiscible liquids. ieee trans. visualization and computer graphics, 25(10), pages 2873-2885, 2019. <![if !supportlists]>68.     <![endif]>jianwei guo, shibiao xu, dong-ming yan, zhanglin cheng, marc jaeger, xiaopeng zhang. realistic procedural plant modeling from multiple view images. ieee transactions on visualization and computer graphics, 2019, online <![if !supportlists]>69.     <![endif]>jianwei guo, fan ding, xiaohong jia, dong-ming yan. automatic and high-quality surface mesh generation for cad models. computer-aided design, volume 109, pages 49-59, 2019. <![if !supportlists]>70.     <![endif]>yiqun wang, dong-ming yan, xiaohan liu, chengcheng tang, jianwei guo, xiaopeng zhang, peter wonka: isotropic surface remeshing without large and small angles. ieee trans. vis. comput. graph. 25(7): 2430-2442 (2019) <![if !supportlists]>71.     <![endif]>qunce xu, dong-ming yan, wenbin li, yong-liang yang. anisotropic surface remeshing without obtuse angles. computer graphics forum (pacific graphics), 2019 <![if !supportlists]>72.     <![endif]>m. yuan, l. dai, d.-m. yan, l. zhang, x. zhang. fast and error-bounded space-variant bilateral filtering. journal of computer science and technology. 2019 <![if !supportlists]>73.     <![endif]>c. rao, l. tian, d.-m. yan, s. liao, d. oliver, l. lu. consistently fitting orthopaedic casts. computer-aided geometric design, 2019 <![if !supportlists]>74.     <![endif]>y. gao, l. wu, d.-m. yan, l. nan. near support-free multi-directional 3d printig via global-optimal decomposition. graphical models, 2019 <![if !supportlists]>75.     <![endif]>bin fan, qingqun kong, xinchao wang, zhiheng wang, shiming xiang, chunhong pan, and pascal fua. "a performance evaluation of local features for image based 3d reconstruction". ieee transactions on image processing, 28(10): 4774 - 4789, 2019. <o:p> </o:p> <a name="_toc40189715"></a><a name="_toc24098149"></a><a name="_toc532562234"></a><a name="_toc532391203">y�zso��{</a><a name="_toc24465898"></a><a name="_toc24361006"></a><a name="_toc532562404">multimedia computing</a><o:p></o:p> <![if !supportlists]>76.     <![endif]>yingying deng, fan tang, weiming dong, fuzhang wu, oliver deussen, changsheng xu, selective clustering for representative paintings selection, multimedia tools and applications, vol. 78, no. 14, pp. 19305-19323, 2019. <![if !supportlists]>77.     <![endif]>xiaoshan yang, changsheng xu: image captioning by asking questions. acm transactions on multimedia computing, communications, and applications (tomm) 15(2s): 55:1-55:19, 2019. <![if !supportlists]>78.     <![endif]>fan qi, xiaoshan yang, tianzhu zhang, and changsheng xu: discriminative multimodal embedding for event classification, neurocomputing, 2019. <![if !supportlists]>79.     <![endif]>cuirong long, xiaoshan yang, changsheng xu: cross-domain personalized image captioning, multimedia tools and applications, 2019: 1-16. <![if !supportlists]>80.     <![endif]>hantao yao, feng dai, shiliang zhang, yongdong zhang, qi tian, changsheng xu, dr2-net: deep residual reconstruction network for image compressive sensing . neurocomputing 359: 483-493 (2019) <![if !supportlists]>81.     <![endif]>hantao yao, shiliang zhang, richang hong, yongdong zhang, changsheng xu, qi tian, deep representation learning with part loss for person re-identification . ieee trans. image processing 28(6): 2860-2871 (2019) <![if !supportlists]>82.     <![endif]>feng xue, jianwei wang, shengsheng qian, tianzhu zhang, xueliang liu, and changsheng xu: multi-modal max-margin supervised topic model for social event analysis. multimedia tools appl. 78(1): 141-160 (2019) <![if !supportlists]>83.     <![endif]>junyu gao, tianzhu 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zhixing tian, yuanzhe zhang, kang liu, jun zhao and xiangwen liao. delai qiu, liang bao, zhixing tian, yuanzhe zhang, kang liu, jun zhao and xiangwen liao, the eighteenth china national conference on computational linguistics (ccl 2019), pp93-104, 10.18-10.20, kunming china� <![if !supportlists]>4.         <![endif]>guirong bai, shizhu he, kang liu and jun zhao. utterance alignment in custom service by integer programming, the eighteenth china national conference on computational linguistics (ccl 2019), pp703-714, 10.18-10.20, kunming china <![if !supportlists]>5.         <![endif]>�n�s� _'yo�v��^ns�c�vt� b�tq�~l�:nr�g�v�so(u7b;u�p�g�^n�^(u ,{as�nj\hq�v�n:g��f[/go��ncmmsc2019 ��pp�644-650,2019.08.15-17�r�wm��[ <![if !supportlists]>6.         <![endif]>�pwzs�v��^ns�)nck�h �f_l�q ��s׋f �:_%f�� y��penc�^ꁨros�e�lxvz ,{as�nj\hq�v�n:g��f[/go��ncmmsc2019 �pp:256-268,2019.08.14-17�r�wm��[ <![if !supportlists]>7.         <![endif]>:_%f��s׋f�f_l�q��pwzs�)nck�h�ѐz��e� _^�v��^ns� 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