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個(gè)人簡(jiǎn)介：

周武杰，教授，博士后，博士生導(dǎo)師，浙江省省級(jí)人才，"中國(guó)視谷"專家委員會(huì)特聘專家，浙江省優(yōu)秀碩士論文指導(dǎo)教師，浙江省電子學(xué)會(huì)理事，IEEE Senior Member，CCF Senior Member，通信學(xué)會(huì)高級(jí)會(huì)員，CSIG/CAAI/CAA Member，CAA模式識(shí)別與機(jī)器智能專委會(huì)委員，CCF多媒體技術(shù)專委會(huì)AC委員，CCF Yocsef 杭州AC委員，CSIG多媒體技術(shù)專委會(huì)委員，CSIG視覺(jué)大數(shù)據(jù)專委會(huì)委員，CSIG三維視覺(jué)專委會(huì)委員；2012年入選“青年骨干教師”，2015年入選“優(yōu)秀青年教師資助計(jì)劃”，2016年入選“科大青年英才”，入選斯坦福全球前2%頂尖科學(xué)家榜單2025“終身科學(xué)影響力排行榜”（人工智能與圖像處理領(lǐng)域），并連續(xù)4年入選“年度科學(xué)影響力排行榜”（2022-2025），2024年入選“科大領(lǐng)軍人才”，2024年入選浙江省高層次人才特殊支持計(jì)劃“萬(wàn)人計(jì)劃”青年拔尖人才，2026年入選全球頂尖前10萬(wàn)科學(xué)家榜單（國(guó)內(nèi)計(jì)算機(jī)科學(xué)學(xué)科入選者排名：382/703）。浙江大學(xué)博士后（導(dǎo)師：虞露），國(guó)家留學(xué)基金委公派新加坡南洋理工大學(xué)訪問(wèn)學(xué)者（導(dǎo)師：Weisi Lin, IEEE Fellow），浙江大學(xué)訪問(wèn)學(xué)者（導(dǎo)師：楊易, IEEE Fellow）。主要從事人工智能大模型、多模態(tài)信息處理、計(jì)算機(jī)視覺(jué)與模式識(shí)別等方向的研究；近幾年以第一作者在TIP、TMM、TCSVT、TCAS-I、TII、TITS、TNNLS、JSTSP、TSMC、TBC、TGRS、IEEE IoT Journal、TASE、TAI、TCI、TIM、MIS、TCDS、TETCI、TIV、TBDATA、IEEE Sensors Journal、JSTARS、PR、Information Fusion和中國(guó)科學(xué)等國(guó)際權(quán)威SCI期刊或核心期刊上發(fā)表學(xué)術(shù)論文100多篇，其中SCI收錄70多篇（中科院一區(qū)57篇, IEEE Journal/Transactions/Magazine 78篇，ESI熱點(diǎn)論文/高被引論文10多篇，10余篇論文入選TIP、TCSVT、TMM、MIS和TETCI 等期刊Top 50 Popular Articles），H指數(shù) (h-index)46 (Google Scholar)，被引頻次總計(jì)7800+ (Google Scholar))；申請(qǐng)國(guó)家發(fā)明專利70多項(xiàng)，授權(quán)50多項(xiàng)，多項(xiàng)已轉(zhuǎn)讓投產(chǎn)；第一完成人獲浙江省自然科學(xué)獎(jiǎng)1項(xiàng)，參與獲市科學(xué)技術(shù)獎(jiǎng)1項(xiàng)，浙江省青年科技工作者優(yōu)秀論文獎(jiǎng)1項(xiàng)；擔(dān)任國(guó)家基金通訊評(píng)審專家，浙江省科技專家?guī)鞂＜?，廣東省基金項(xiàng)目評(píng)審專家；擔(dān)任TIP、TNNLS、TCSVT、TCYB、TMM、TBC、JSTSP、TSMC、SPL等國(guó)外權(quán)威SCI期刊稿件評(píng)審人。目前，主持國(guó)家自然科學(xué)基金3項(xiàng)（面上2項(xiàng)和青年1項(xiàng))，省自然科學(xué)基金3項(xiàng)（重點(diǎn)、一般和青年各1項(xiàng)），中國(guó)博士后基金1項(xiàng)，企業(yè)重大橫向課題4項(xiàng)，重中之重實(shí)驗(yàn)室開(kāi)放基金2項(xiàng)和教育廳科研項(xiàng)目1項(xiàng)。

入選全球頂尖前10萬(wàn)科學(xué)家榜單： https://aiie.zust.edu.cn/info/1003/1440.htm

入選斯坦福全球前2%頂尖科學(xué)家榜單：https://www.zust.edu.cn/info/1089/19088.htm

E-mail: wujiezhou@163.com

微信號(hào)：zwjzust     （歡迎加微信交流）

招收研究生（含聯(lián)合培養(yǎng)、轉(zhuǎn)專業(yè)等）:

視覺(jué)智能感知與理解實(shí)驗(yàn)室(中央支持地方高校改革發(fā)展專項(xiàng)資助建設(shè)，項(xiàng)目編號(hào)：303011-2019-0008)招收碩士研究生（學(xué)碩：先進(jìn)制造與信息化，專碩：電子信息、應(yīng)用統(tǒng)計(jì)），主要研究方向：人工智能大模型、多模態(tài)信息處理、計(jì)算機(jī)視覺(jué)與模式識(shí)別等。共計(jì)培養(yǎng)了40多名研究生（所有研究生均按時(shí)畢業(yè)）。其中，20多名畢業(yè)生選擇進(jìn)入長(zhǎng)三角人工智能相關(guān)企業(yè)工作，另有20多名畢業(yè)生（絕大部分為專碩學(xué)生）攻讀國(guó)內(nèi)外名校的博士學(xué)位，如北京大學(xué)、University of Liverpool、University of North Texas、University of Technology Sydney、Macquarie University、University of Georgia、Oregon State University、武漢大學(xué)、同濟(jì)大學(xué)、南開(kāi)大學(xué)、北京理工大學(xué)、湖南大學(xué)、華南理工大學(xué)、西安電子科技大學(xué)、北京郵電大學(xué)、西北大學(xué)、南京理工大學(xué)、南昌大學(xué)、上海大學(xué)、湘潭大學(xué)和寧波大學(xué)等高校。目前，指導(dǎo)的研究生中14名獲國(guó)家獎(jiǎng)學(xué)金（獎(jiǎng)金2萬(wàn)/人），4名獲卓越學(xué)子獎(jiǎng)學(xué)金（獎(jiǎng)金3萬(wàn)/人），1名獲校“大學(xué)生年度人物”，2篇論文獲省優(yōu)秀碩士論文，2篇論文獲校優(yōu)秀碩士論文。預(yù)加入實(shí)驗(yàn)室請(qǐng)發(fā)個(gè)人簡(jiǎn)歷和本科成績(jī)（可系統(tǒng)截圖）到E-mail: wujiezhou@163.com

2篇論文獲省優(yōu)秀碩士論文：https://yjs.zust.edu.cn/info/1035/3822.htm

實(shí)驗(yàn)室"卓越學(xué)子"視頻（視頻中第2位同學(xué)--吳君委）：https://mp.weixin.qq.com/s/vYokNzDeHmtVKmIkOcpnnw

實(shí)驗(yàn)室"大學(xué)生年度人物"（視頻中第8位同學(xué)--劉勁夫）：https://mp.weixin.qq.com/s/ALDUnCtIs8dbnKoGHvDd3Q

實(shí)驗(yàn)室”卓越學(xué)子”簡(jiǎn)介（范曉敏）https://mp.weixin.qq.com/s/hJ9owybCYjAHLWCy5O4BLw

獎(jiǎng)項(xiàng)榮譽(yù)

1、認(rèn)知啟發(fā)式視覺(jué)質(zhì)量評(píng)價(jià)的理論與方法，2023年度浙江省自然科學(xué)獎(jiǎng)三等獎(jiǎng)，第一完成人

2、立體視覺(jué)信息隱藏相關(guān)理論與關(guān)鍵技術(shù)，2019年度寧波市科學(xué)技術(shù)獎(jiǎng)二等獎(jiǎng)，第三完成人

3、面向立體全景生成的智能視覺(jué)計(jì)算理論與關(guān)鍵技術(shù)研究，2025年度山東省計(jì)算機(jī)學(xué)會(huì)自然科學(xué)二等獎(jiǎng)，第四完成人

4、基于多視圖神經(jīng)隱式表示的三維場(chǎng)景感知理論與方法研究，第六屆山東省人工智能自然科學(xué)獎(jiǎng)二等獎(jiǎng)，第三完成人

科研項(xiàng)目

1、國(guó)家自然科學(xué)基金面上項(xiàng)目，知識(shí)蒸餾驅(qū)動(dòng)的高效多模態(tài)人群密度估計(jì)方法研究，主持

2、國(guó)家自然科學(xué)基金面上項(xiàng)目，視覺(jué)認(rèn)知啟發(fā)式雙目視覺(jué)顯著性物體檢測(cè)模型研究，主持

3、國(guó)家自然科學(xué)基金青年項(xiàng)目，基于數(shù)據(jù)挖掘與感知分析的非對(duì)稱失真視覺(jué)質(zhì)量評(píng) 價(jià)模型研究，主持

4、國(guó)家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目，數(shù)據(jù)和知識(shí)聯(lián)合驅(qū)動(dòng)城市易腐垃圾炭化與綠色可持續(xù)利用的關(guān)鍵技術(shù)及應(yīng)用，主研

5、浙江省自然科學(xué)基金重點(diǎn)項(xiàng)目，面向智慧城市視頻監(jiān)控系統(tǒng)的多模態(tài)人群密度估計(jì)關(guān)鍵技術(shù)研究，主持

6、浙江省自然科學(xué)基金一般項(xiàng)目，基于雙目視覺(jué)機(jī)理挖掘的立體視頻質(zhì)量評(píng)價(jià)模型研究，主持

7、浙江省自然科學(xué)基金青年項(xiàng)目，基于立體感知特性分析的非對(duì)稱失真視覺(jué)質(zhì)量客觀評(píng)價(jià)模型研究，主持

8、中國(guó)博士后基金面上項(xiàng)目，基于視覺(jué)感知挖掘的非對(duì)稱失真視覺(jué)質(zhì)量評(píng)價(jià)模型， 主持

9、企業(yè)委托項(xiàng)目，智慧海洋漁船信息智能化管理系統(tǒng)開(kāi)發(fā)項(xiàng)目，主持

10、企業(yè)委托項(xiàng)目，生活垃圾投放智能化監(jiān)管系統(tǒng)開(kāi)發(fā)，主持

11、企業(yè)委托項(xiàng)目，基于機(jī)器視覺(jué)的晶振相關(guān)產(chǎn)品缺陷圖像識(shí)別算法，主持

12、企業(yè)委托項(xiàng)目，基于深度學(xué)習(xí)的催化劑表面缺陷智能檢測(cè)系統(tǒng)開(kāi)發(fā)與應(yīng)用研究，主持

代表作（中科院一區(qū)或IEEE Trans.或CCF A類）

[1] W. Zhou (周武杰), Y. Li, Q. Jiang, L. Liao, R. Cong and W. Lin, "Decouple-Then-Synergize: A Self-Paced Collaborative Learning Network for RGB-T Snowy Urban Scene Parsing," IEEE Transactions on Image Processing, doi: 10.1109/TIP.2026.3696109. (CCF A類)

[2] W. Zhou (周武杰), B. Tang, R. Cong and Q. Jiang, "Turbidity–Similarity Decoupling: Feature-Consistent Mutual Learning for Underwater Salient Object Detection," IEEE Transactions on Image Processing, vol. 35, pp. 495–510, 2026. (CCF A類)

[3] W. Zhou*(周武杰), J. Liu, J. Lei, L. Yu and J.-N. Hwang, “GMNet: Graded-Feature Multilabel-Learning Network for RGB-Thermal Urban Scene Semantic Segmentation,” IEEE Transactions on Image Processing, vol. 30, pp. 7790–7802, 2021. (CCF A類)

[4] W. Zhou*(周武杰), Y. Zhu*, J. Lei, R. Yang, L. Yu, “LSNet: Lightweight Spatial Boosting Network for Detecting Salient Objects in RGB-Thermal Images,” IEEE Transactions on Image Processing, vol. 32, pp. 1329–1340, 2023. (CCF A類)

[5] W. Zhou(周武杰), F. Sun, Q. Jiang, R. Cong, J.-N. Hwang, “WaveNet: Wavelet Network with Knowledge Distillation for RGB-T Salient Object Detection,” IEEE Transactions on Image Processing, vol. 32, pp. 3027–3039, 2023. (CCF A類)

[6] W. Zhou*(周武杰), L. Yu, Y. Zhou, W. Qiu, M.-W. Wu, and T. Luo, “Local and Global Feature Learning for Blind Quality Evaluation of Screen Content and Natural Scene Images,” IEEE Transactions on Image Processing, vol. 27, no. 5, pp. 2086–2095, May 2018. (CCF A類)

[7] W. Zhou*(周武杰), Y. Zhu, J. Lei, J. Wan, and L. Yu, “CCAFNet: Crossflow and cross-scale adaptive fusion network for detecting salient objects in RGB-D images,” IEEE Transactions on Multimedia, vol. 24, pp. 2192–2204, 2022.  (CCF A類)

[8] W. Zhou*(周武杰), J. Wu, J. Lei, J.-N. Hwang and L. Yu, “Salient Object Detection in Stereoscopic 3D Images Using a Deep Convolutional Residual Autoencoder,” IEEE Transactions on Multimedia, vol. 23, pp. 3388–3399, 2021.  (CCF A類)

[9] W. Zhou*(周武杰), X. Lin, J. Lei, L. Yu and J.-N. Hwang, “MFFENet: Multiscale Feature Fusion and Enhancement Network for RGB–Thermal Urban Road Scene Parsing,” IEEE Transactions on Multimedia, vol. 24, pp. 2526–2538, 2022.  (CCF A類)

[10] W. Zhou*(周武杰), E. Yang, J. Lei, J. Wan, and L. Yu, “PGDENet: Progressive Guided Fusion and Depth Enhancement Network for RGB-D Indoor Scene Parsing,” IEEE Transactions on Multimedia, vol. 25, pp. 3483–3494, 2023. (CCF A類)

[11] W. Zhou*(周武杰), L. Yu, “Binocular Responses for No-Reference 3D Image Quality Measurement,” IEEE Transactions on Multimedia, vol. 16, no. 6, pp. 1077–1084, 2016.  (CCF A類)

[12] W. Zhou*(周武杰), Y. Cai, L. Zhang, W. Yan and L. Yu, "UTLNet: Uncertainty-aware Transformer Localization Network for RGB-Depth Mirror Segmentation," IEEE Transactions on Multimedia, vol. 26, pp. 4564–4574, 2024. (CCF A類)

[13] W. Zhou (周武杰), H. Wu, M. Fang and Q. Jiang, “Multiscale Graph-Refinement-Aware Network for RGB-D Mirror Segmentation,” IEEE Transactions on Multimedia, (已錄用，待在線發(fā)表) (CCF A類)

[14] W. Zhou (周武杰), Y. Wang, X. Qian, Y. Liu, and L. Yu, “W. Zhou (周武杰), H. Wu, M. Fang and Q. Jiang, “Multiscale Graph-Refinement-Aware Network for RGB-D Mirror Segmentation,” IEEE Transactions on Multimedia, (已錄用，待在線發(fā)表) (CCF A類)

[15] W. Zhou (周武杰), Z. Yang, C. Xu,J. Zhang,T. Luo,Y. Liu,R. Cong, "Unify then Align: A Consistency-Aligned CLIP Representation Network for RGB-D Indoor Scene Analysis," IEEE Transactions on Multimedia, (已錄用，待在線發(fā)表) (CCF A類)

[16] W. Zhou* (周武杰), C. Liu, J. Lei, and L. Yu, “Remaking learning: A Lightweight Network for Saliency Redetection on RGB-D Images,” SCIENCE CHINA Information Sciences, vol. 65, no. 5, Art. no. 160107, 2022. (CCF A類)

[17] W. Zhou* (周武杰), S. Dong, C. Xu, Y. Qian, “Edge-aware Guidance Fusion Network for RGB–Thermal Scene Parsing,” in Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), vol. 36, no. 3, pp. 3571–3579, 2022. (CCF A類, 人工智能頂級(jí)會(huì)議)

[18] W. Zhou*(周武杰), Q. Guo, J. Lei, L. Yu and J.-N. Hwang, “ECFFNet: Effective and Consistent Feature Fusion Network for RGB-T Salient Object Detection,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 3, pp. 1224–1235, March 2022.

[19] W. Zhou*(周武杰), H. Zhang, W. Yan, and W. Lin, “MMSMCNet: Modal Memory Sharing and Morphological Complementary Networks for RGB-T Urban Scene Semantic Segmentation,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 12, pp. 7096–7108, Dec. 2023. 

[20] W. Zhou (周武杰), J. Hong, W. Yan and Q. Jiang, "Modal Evaluation Network via Knowledge Distillation for No-Service Rail Surface Defect Detection," IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 5, pp. 3930–3942, May 2024.

[21] W. Zhou (周武杰), B. Jian, X. Dong and Q. Jiang, “DGPINet-KD: Deep Guided and Progressive Integration Network with Knowledge Distillation for RGB-D Indoor Scene Analysis,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 9, pp. 7844–7855, Sept. 2024.

[22] W. Zhou (周武杰), H. Wu and Q. Jiang, “MDNet: Mamba-Effective Diffusion-Distillation Network for RGB-Thermal Urban Dense Prediction,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 35, no. 4, pp. 3222–3233, April 2025.

[23] W. Zhou (周武杰), Z. Ju, R. Cong and W. Yan, "RCNet: Dual-Network Resonance Collaboration via Mutual Learning for RGB-D Road Defect Detection," IEEE Transactions on Circuits and Systems for Video Technology, vol. 36, no. 3, pp. 3989–4003, March 2026. 

[24] W. Zhou (周武杰), Z. Ju, L. Liao, and R. Cong, "Priors Meet Hindsight: An Asymmetric Collaborative Evolution Framework for RGB-D Road Defect Detection," IEEE Transactions on Circuits and Systems for Video Technology, doi: 10.1109/TCSVT.2026.3700185. 

[25] W. Zhou (周武杰), Y. Wang, and X. Qian, "Knowledge Distillation and Contrastive Learning for Detecting Visible-Infrared Transmission Lines using Separated Stagger Registration Network," IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 72, no. 8, pp. 4140–4152, Aug. 2025.

[26] W. Zhou(周武杰), C. Ji, and M. Fang, “Transmission Line Detection through Bidirectional Guided Registration with Knowledge Distillation,” IEEE Transactions on Industrial Informatics, vol. 20, no. 4, pp. 5671–5682, April 2024.

[27] W. Zhou*(周武杰), Q. Guo, J. Lei, L. Yu and J.-N. Hwang, “IRFR-Net: Interactive Recursive Feature-reshaping Network for Detecting Salient Objects in RGB-D Images,” IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 3, pp. 4132–4144, March 2025.

[28] W. Zhou (周武杰), X. Sun, X. Qian, and M. Fang, “Asymmetrical Contrastive Learning Network for No-Service Rail Surface Defect Detection,” IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 7, pp. 12469–12482, July 2025.

[29] W. Zhou (周武杰), B. Tang, X. Dong, and F. Qiang, “Prompt then Refine:Prompt-Free SAM-Enhanced CollaborativeLearning Network for Detecting SalientObjects in Underwater Images, IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2026.3673090.

[30] W. Zhou (周武杰), J. Xie, and C. Xu, Semantic Prompt and Graph-Convolution-Structure Distillation Framework for Semantic Segmentation of Remote Sensing Images, IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2026.3675381.

[31] W. Zhou (周武杰), B. Jian, Y. Liu, and Q. Jiang, “Multiattentive Perception and Multilayer Transfer Network Using Knowledge Distillation for RGB-D Indoor Scene Parsing,” IEEE Transactions on Neural Networks and Learning Systems,  vol. 36, no. 10, pp. 18287–18299, Oct. 2025.

[32] W. Zhou*(周武杰), Y. Lv, J. Lei and L. Yu, “Global and Local-Contrast Guides Content-Aware Fusion for RGB-D Saliency Prediction,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 6, pp. 3641–3649, June 2021. 

[33] W. Zhou*(周武杰), T. Gong, J. Lei and L. Yu, “DBCNet: Dynamic Bilateral Cross-Fusion Network for RGB-T Urban Scene-Understanding in Intelligent Vehicles,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 12, pp. 7631–7641, Dec. 2023.

[34] W. Zhou (周武杰), T. Gong, and W. Yan, "Knowledge Distillation SegFormer-Based Network for RGB-T Semantic Segmentation," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 55, no. 3, pp. 2170–2182, March 2025.

[35] W. Zhou*(周武杰), E. Yang, J. Lei, and L. Yu, “FRNet: Feature Reconstruction Network for RGB-D Indoor Scene Parsing,” IEEE Journal of Selected Topics in Signal Processing, vol. 16, no. 4, pp. 677–687, June 2022. 

[36] W. Zhou*(周武杰), J. Jin, J. Lei, and L. Yu, “CIMFNet: Cross-layer Interaction and Multiscale Fusion Network for Semantic Segmentation of High-Resolution Remote Sensing Images,” IEEE Journal of Selected Topics in Signal Processing, vol. 16, no. 4, pp. 666–676, June 2022.

[37] W. Zhou*(周武杰), Y. Zhang, W. Yan, L. Ye, “An Efficient RGB-D Indoor Scene-Parsing Solution via Lightweight Multi-flow Intersection and Knowledge Distillation,” IEEE Journal of Selected Topics in Signal Processing, vol. 18, no. 3, pp. 336–345, April 2024.

[38] W. Zhou*(周武杰), Y. Pan, L. Y, J. Lei, and L. Yu, “DEFNet: Dual-Branch Enhanced Feature Fusion Network for RGB-T Crowd Counting,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 12, pp. 24540–24549, Dec. 2022. 

[39] W. Zhou*(周武杰), Y. Lv, J. Lei, and L. Yu, “Embedded Control Gate Fusion and Attention Residual Learning for RGB–Thermal Urban Scene Parsing,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 5, pp. 4794–4803, May 2023. 

[40] W. Zhou*(周武杰), X. Yang, J. Lei, W. Yan and L. Yu, "MC3Net: Multimodality Cross-Guided Compensation Coordination Network for RGB-T Crowd Counting," IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 5, pp. 4156–4165, May 2024.

[41] W. Zhou (周武杰), J. Hong, X. Ran, W. Yan and Q. Jiang, "DSANet-KD: Dual Semantic Approximation Network via Knowledge Distillation for Rail Surface Defect Detection," IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 10, pp. 13849–13862, Oct. 2024.

[42] W. Zhou (周武杰), Y. Cai, F. Qiang, "Morphology-Guided Network via Knowledge Distillation for RGB-D Mirror Segmentation," IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 11, pp. 17382–17391, Nov. 2024.

[43] W. Zhou* (周武杰), J. Jin, J. Lei, and J.-N. Hwang, “CEGFNet: Common Extraction and Gate Fusion Network for Scene Parsing of Remote Sensing Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, 2022, Art no. 5405110.

[44] W. Zhou (周武杰), X. Fan, W. Yan, S. Shan, Q. Jiang, and J.-N. Hwang, “Graph Attention Guidance Network with Knowledge Distillation for Semantic Segmentation of Remote Sensing Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, 2023, Art no. 4506015.

[45] W. Zhou (周武杰), Y. Li, J. Huang, W. Yan, M. Fang and Q. Jiang, “GSGNet-S*: Graph Semantic Guidance Network via Knowledge Distillation for Optical Remote Sensing Image Scene Analysis,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, 2023, Art no. 4508512.

[46] W. Zhou (周武杰), Y. Li, J. Huang, Y. Liu and Q. Jiang, "MSTNet-KD: Multilevel Transfer Networks Using Knowledge Distillation for the Dense Prediction of Remote-Sensing Images," IEEE Transactions on Geoscience and Remote Sensing, vol. 62, 2024, Art no. 4504612.

[47] W. Zhou (周武杰), P. Yang, W. Qiu and F. Qiang, "STONet-S*: A Knowledge-Distilled Approach for Semantic Segmentation in Remote-Sensing Images," IEEE Transactions on Geoscience and Remote Sensing, vol. 62, 2024, Art no. 4414413.

[48] W. Zhou (周武杰), P. Yang, Y. Liu, R. Cong and Q. Jiang, "Remote Sensing Image Scene Classification via Graph Template Enhancement and Supplementation Network with Dual-Teacher Knowledge Distillation," IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1–13, 2024, Art no. 3490559.

[49] W. Zhou (周武杰), P. Yang and Y. Liu, "HLMamba: Hybrid Lightweight Mamba-Based Fusion Network for Dense Prediction of Remote Sensing Images," IEEE Transactions on Geoscience and Remote Sensing, vol. 63, pp. 1–11, 2025, Art no. 4414211.

[50] W. Zhou (周武杰), J. Xie, C. Xu, Y. Liu and Y. Wang, "Adapt, Generate, and Supervise: Geometry-Aware Diffusion-Guided SAM Framework for Remote Sensing Semantic Segmentation," IEEE Transactions on Geoscience and Remote Sensing, vol. 64, pp. 1–14, 2026, Art no. 4402014.

[51] W. Zhou (周武杰), X. Yang, X. Dong, “MJPNet-S*: Multistyle Joint-perception Network with Knowledge Distillation for Drone RGB-Thermal Crowd Density Estimation in Smart Cities,” IEEE Internet of Things Journal, vol. 11, no. 11, pp. 20327–20339, June 2024.

[52] W. Zhou (周武杰), X. Yang, W. Yan and Q. Jiang, “Hybrid Knowledge Distillation for RGB-T Crowd Density Estimation in Smart Surveillance Systems,” IEEE Internet of Things Journal, vol. 12, no. 7, pp. 9276–9289, April1, 2025.

[53] W. Zhou (周武杰), B. Jian and Y. Liu, "Feature Contrast Difference and Enhanced Network for RGB-D Indoor Scene Classification in Internet of Things," IEEE Internet of Things Journal, vol. 12, no. 11, pp. 17610–17621, June, 2025.

[54] W. Zhou (周武杰), Y. Xiao, W. Yan, and L. Yu, “CMPFFNet: Cross-Modal and Progressive Feature Fusion Network for RGB-D Indoor Scene Semantic Segmentation,” IEEE Transactions on Automation Science and Engineering, vol. 21, no. 4, pp. 5523–5533, Oct. 2024.

[55] W. Zhou (周武杰), J. Yang, et al. “RDNet-KD: Recursive Encoder, Bimodal Screening Fusion, and Knowledge Distillation Network for Rail Defect Detection,” IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 2031–2040, 2025.

[56] W. Zhou (周武杰), W. Qiu, M. Wu, “MSNet: Multiple Strategy Network with Bidirectional Fusion for Detecting Salient Objects in RGB-D Images,” IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 4341–4353, 2025.

[57] W. Zhou (周武杰), H. Zhang, Y. Liu and T. Luo, "Enhancing RGB-D Mirror Segmentation with a Neighborhood-Matching and Demand-Modal Adaptive Network using Knowledge Distillation," IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 12679–12692, 2025.

[58] W. Zhou* (周武杰), W. Qiu, M. Wu, “Utilizing Dictionary Learning and Machine Learning for Blind Quality Assessment of 3D Images,” IEEE Transactions on Broadcasting, vol. 63, no. 2, pp. 404–415, June 2017.

[59] W. Zhou* (周武杰), S. Dong, J. Lei, and L. Yu, “MTANet: Multitask-Aware Network with Hierarchical Multimodal Fusion for RGB-T Urban Scene Understanding,” IEEE Transactions on Intelligent Vehicles, vol. 8, no. 1, pp. 48–58, Jan. 2023. 

[60] W. Zhou (周武杰), S. Dong, M. Fang and L. Yu, "CACFNet: Cross-Modal Attention Cascaded Fusion Network for RGB-T Urban Scene Parsing," IEEE Transactions on Intelligent Vehicles, vol. 9, no. 1, pp. 1919–1929, Jan. 2024. 

[61] W. Zhou (周武杰), H. Wu, and Q. Jiang, "MGSGNet-S*: Multilayer Guided Semantic Graph Network via Knowledge Distillation for RGB-Thermal Urban Scene Parsing," IEEE Transactions on Intelligent Vehicles, vol. 10, no. 5, pp. 3543–3559, May 2025.

[62] W. Zhou (周武杰), H. Zhang, and W. Qiu, "Differential Modal Multistage Adaptive Fusion Networks via Knowledge Distillation for RGB-D Mirror Segmentation," IEEE Transactions on Big Data, vol. 11, no. 4, pp. 1959–1969, Aug. 2025.

[63] W. Zhou (周武杰), Y. Li, and Y. Liu, "Asymmetric Dual Encoder Network with Clustering and Mutual Contrast Loss for the Semantic Segmentation of Remote Sensing Images," IEEE Transactions on Big Data, vol. 11, no. 6, pp. 3103–3115, Dec. 2025.

[64] W. Zhou (周武杰), Y. Wu, F. Qiang and W. Yan, "Lightweight Scope Integration Network for Rail Surface Defect Detection," IEEE Transactions on Big Data, vol. 12, no. 2, pp. 321–329, April 2026. 

[65] W. Zhou (周武杰), Y. Xiao, Y. Liu, and Q. Jiang, “FIMKD: Feature-Implicit Mapping Knowledge Distillation for RGB-D Indoor Scene Semantic Segmentation,” IEEE Transactions on Artificial Intelligence, vol. 5, no. 12, pp. 6488–6499, Dec. 2024.

[66] W. Zhou (周武杰), B. Wang, X. Dong, J. Meng, “Location, Neighborhood, and Semantic Guidance Network for RGB-D Co-Salient Object Detection,” IEEE Transactions on Artificial Intelligence, vol. 6, no. 12, pp. 3297–3311, Dec. 2025.

[67] W. Zhou* (周武杰), J. Lei, T. Luo, “TSNet: Three-stream Self-attention Network for RGB-D Indoor Semantic Segmentation,” IEEE Intelligent Systems, vol. 36, no. 4, pp. 73–78, July-Aug. 2021.

[68] W. Zhou* (周武杰), S. Lv, J. Lei, and L. Yu, “RFNet: Reverse Fusion Network with Attention Mechanism for RGB-D Indoor Scene Understanding,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 2, pp. 598–603, April 2023.

[69] W. Zhou* (周武杰), Y. Zhu, J. Lei, J. Wan, and L. Yu, “APNet: Adversarial-Learning-Assistance and Perceived Importance Fusion Network for All-Day RGB-T Salient Object Detection,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 4, pp. 957–968, Aug. 2022.

[70] W. Zhou* (周武杰), S. Pan, J. Lei, and L. Yu, “TMFNet: Three-Input Multilevel Fusion Network for Detecting Salient Objects in RGB-D Images,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 3, pp. 593–601, June 2022.

[71] W. Zhou (周武杰), G. Xu, “ACENet: Auxiliary Context-Information Enhancement Network for RGB-D Indoor Scene Semantic Segmentation,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 8, no. 2, pp. 1125–1129, April 2024.

[72] W. Zhou (周武杰), T. Gong, "BFTNet: Boundary-Induced Four-Phase Transformer Network for RGB-Thermal Urban Road Scene Parsing," IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 9, no. 4, pp. 2703–2712, Aug. 2025.

[73] W. Zhou (周武杰), Y. Wu, J. Meng and X. Dong, "Morphology Recognition Network via Contrastive Distillation for Detecting Rail Surface Defects," IEEE Transactions on Emerging Topics in Computational Intelligence, doi: 10.1109/TETCI.2025.3593905.

[74] W. Zhou* (周武杰), W. Liu, J. Lei, T. Luo, L. Yu, “Deep Binocular Fixation Prediction Using Hierarchical Multimodal Fusion Network,” IEEE Transactions on Cognitive and Developmental Systems, vol. 15, no. 2, pp. 476–486, June 2023.

[75] W. Zhou* (周武杰), J. Lei, Q. Jiang, L. Yu and T. Luo, “Blind Binocular Visual Quality Predictor Using Deep Fusion Network,” IEEE Transactions on Computational Imaging, vol. 6, pp. 883–893, 2020.

[76] W. Zhou* (周武杰), and J. Hong, “FHENet: Lightweight Feature Hierarchical Exploration Network for Real-Time Rail Surface Defect Inspection in RGB-D Images,” IEEE Transactions on Instrumentation and Measurement, vol. 72, 2023, Art no. 5005008.  

[77] W. Zhou (周武杰), C. Ji and M. Fang, “Effective Dual-Feature Fusion Network for Transmission Line Detection,” IEEE Sensors Journal, vol. 24, no. 1, pp. 101–109, Jan. 2024.

[78] W. Zhou* (周武杰), X. Fan, L. Yu, and J. Lei, “MISNet: Multiscale Cross-layer Interactive and Similarity Refinement Network for Scene Parsing of Aerial Images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 2025–2034, 2023. 

[79] W. Zhou*(周武杰), Y. Yue, M. Fang, X. Qian, R. Yang, L. Yu, “BCINet: Bilateral Cross-Modal Interaction Network for Indoor Scene Understanding in RGB-D Images,” Information Fusion, vol. 94, pp. 32–42, 2023.

[80] W. Zhou*(周武杰), Y. Cai, X. Dong, F. Qiang, W. Qiu, “ADRNet-S*: Asymmetric depth registration network via contrastive knowledge distillation for RGB-D mirror segmentation,” Information Fusion, vol. 108, 2024, Art no. 102392.

[81] W. Zhou*(周武杰), L. Yu, Y. Zhou, W. Qiu, M.-W. Wu, Ting Luo, “Blind quality estimator for 3D images based on binocular combination and extreme learning machine,” Pattern Recognition, vol. 71, pp. 207–217, Nov. 2017. 

[82] W. Zhou*(周武杰), L. Yu, W. Qiu, Y. Zhou, M. Wu, “Local Gradient Patterns (LGP): an Effective Local Statistical Features Extraction Scheme for No-Reference Image Quality Assessment,” Information Sciences, vol. 397–398, pp. 1–14, Aug. 2017.

[83] W. Zhou (周武杰), Y. Wu, W. Qiu, C. Xu, and F. Qiang, “Effective Bi-decoding Networks for Rail-Surface Defect Detection by Knowledge Distillation,” Applied Soft Computing, vol. 167, 2024, Art. no. 112422.

[84] W. Zhou (周武杰), Y. Xiao, F. Qiang, X. Dong, C. Xu, L. Yu, “AESeg: Affinity-enhanced segmenter using feature class mapping knowledge distillation for efficient RGB-D semantic segmentation of indoor scenes,” Neural Networks, vol. 188, 2025, Art. no. 107438.

指導(dǎo)研究生第一作者發(fā)表SCI論文

[1] S. Dong (研究生), W. Zhou*, C. Xu, and W. Yan, "EGFNet: Edge-aware guidance fusion network for RGB–thermal urban scene parsing," IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 1, pp. 657–669, Jan. 2024.

[2] J. Xie (研究生), W. Zhou, C. Xu, Y. Liu and F. Qiang, "HCL-Net: Heterogeneous Collaborative Learning for Lightweight Remote Sensing Image Segmentation," IEEE Transactions on Geoscience and Remote Sensing, vol. 64, pp. 1–14, 2026, Art no. 4401014.

[3] X. Sun (研究生), W. Zhou* and X. Qian, "Normalized Cyclic Loop Network for Rail Surface Defect Detection Using Knowledge Distillation," IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 11, pp. 16561–16573, Nov. 2024.

[4] Y. Li (研究生), W. Zhou*, “Lightweight and Efficient Multimodal Prompt Injection Network for Scene Parsing of Remote-Sensing Scene Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, 2024, Art no. 4417109.

[5] Z. Tu (研究生), W. Zhou*, X. Qian, "Hybrid Knowledge Distillation Network for RGB-D Co-Salient Object Detection," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 55, no. 4, pp. 2695–2706, April 2025. 

[6] Y. Wang (研究生), W. Zhou* and X. Qian, “Transmission Line Detection Through Auxiliary Feature Registration with Knowledge Distillation,” IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 9413–9425, 2025.

[7] Z. Tu (研究生), X. Qian and W. Zhou*, "Efficient RGB-D Co-Salient Object Detection via Modality-Aware Prompting," IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 12911–12921, 2025.

[8] B. Wang (研究生), W. Zhou*, W. Yan, Q. Jiang and R. Cong, “PENet-KD: Progressive Enhancement Network via Knowledge Distillation for Rail Surface Defect Detection,” IEEE Transactions on Instrumentation and Measurement, vol. 72, 2023, Art no. 5032811.

[9] P. Yang (研究生), W. Zhou* and Y. Liu, "Frequency-Aware Integrity Learning Network for Semantic Segmentation of Remote Sensing Images," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 18, pp. 3398–3409, 2025.

[10] X. Yang (研究生), W. Zhou*, W. Yan, X. Qian, “CAGNet: Coordinated attention guidance network for RGB-T crowd counting,” Expert Systems with Applications, vol. 243, 2024, Art no. 122753.

[11] Z. Tu (研究生), X. Qian, W. Zhou*, "Distilled wave-aware network: Efficient RGB-D co-salient object detection via wave learning and three-stage distillation," Information Sciences, vol. 700, 2025, Art. no. 121855.

[12] X. Fan (研究生), W. Zhou*, X. Qian, W. Yan, “Progressive adjacent-layer coordination symmetric cascade network for semantic segmentation of multimodal remote sensing images,” Expert Systems with Applications, vol. 238, 2024, Art. no. 121999.

[13] B. Wang (研究生), F. Qiang, W. Zhou*, “Hierarchical Cross-Modal multianchor distillation for rail surface defect detection,” Measurement, vol. 253, 2025, Art. no. 117600.

[14] Y. Zhang (研究生), F. Qiang, W. Zhou*, “IIBNet: Inter- and Intra-Information Balance Network with Self-Knowledge Distillation and Region Attention for RGB-D Indoor Scene Parsing,” Expert Systems with Applications, vol. 281, 2025, Art. no. 127670.

[15] Y. Xiao (研究生), J. Meng, F. Qiang, X. Dong and W. Zhou*, "Depth Enhancement Mask Mapping Network With Multi-Teacher Distillation for RGB-D Scene Parsing," IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 20572–20583, 2025

[16] J. Jin (研究生), W. Zhou*, L. Ye, J. Lei, L. Yu, X. Qian, T. Luo, “DASFNet: Dense-Attention–Similarity-Fusion Network for scene classification of dual-modal remote-sensing images,” International Journal of Applied Earth Observation and Geoinformation, vol. 115, 2022, Art. no. 103087.

[17] X. Guo (研究生), W. Zhou*, T. Liu, “Contrastive Learning-Based Knowledge Distillation for RGB-Thermal Urban Scene Semantic Segmentation,” Knowledge-Based Systems, vol. 292, 2024, Art. no. 111588. 

[18] X. Guo (研究生), W. Zhou*, T. Liu, “Multilevel attention imitation knowledge distillation for RGB-thermal transmission line detection,” Expert Systems with Applications, vol. 260, 2025, Art. no. 125406.

[19] X. Guo (研究生),…, W. Zhou* et al., "Transferring Prior Thermal Knowledge for Snowy Urban Scene Semantic Segmentation," IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 8, pp. 12474–12487, Aug. 2025.

[20] Y. Pan (研究生), W. Zhou*, X. Qian, S. Mao, R. Yang, and L. Yu, “CGINet: Cross-modality grade interaction network for RGB-T crowd counting,” Engineering Applications of Artificial Intelligence, vol. 126, 2023, Art. no. 106885.

[21] E. Yang (研究生), W. Zhou*, X., Qian, J. Lei, and L. Yu, “DRNet: Dual-stage refinement network with boundary inference for RGB-D semantic segmentation of indoor scenes,” Engineering Applications of Artificial Intelligence, vol. 125, 2023, Art. no. 106729.

[22] T. Gong (研究生), W. Zhou*, X. Qian, J. Lei, and L. Yu, “Global contextually guided lightweight network for RGB-thermal urban scene understanding,” Engineering Applications of Artificial Intelligence, vol. 117, 2023, Art. no. 105510.

[23] J. Wu (研究生), W. Zhou*, T. Luo, L. Yu, and J. Lei, “Multiscale multilevel context and multimodal fusion for RGB-D salient object detection,” Signal Processing, vol. 178, 2021, Art. no. 107766.

[24] C. Ji (研究生), W. Zhou*, J. Lei and L. Ye, "Infrared and Visible Image Fusion via Multiscale Receptive Field Amplification Fusion Network," IEEE Signal Processing Letters, vol. 30, pp. 493–497, 2023.

[25] H. Zhang (研究生), X. Ran and W. Zhou*, "Self-Knowledge Distillation-Based Staged Extraction and Multiview Collection Network for RGB-D Mirror Segmentation," IEEE Signal Processing Letters, vol. 31, pp. 1029–1033, 2024.

[26] Y. Wu (研究生), F. Qiang, W. Zhou* and W. Yan, "PFCNet: Enhancing Rail Surface Defect Detection With Pixel-Aware Frequency Conversion Networks," IEEE Signal Processing Letters, vol. 32, pp. 606–610, 2025.

[27] C. Li (研究生), W. Zhou*, X. Zhou and W. Yan, "Semantic Progressive Guidance Network for RGB-D Mirror Segmentation," IEEE Signal Processing Letters, vol. 31, pp. 2780–2784, 2024.

[28] Y. Zhang (研究生), W. Zhou*, X. Ran and M. Fang, "Lightweight Dual Stream Network With Knowledge Distillation for RGB-D Scene Parsing," IEEE Signal Processing Letters, vol. 31, pp. 855–859, 2024.

[29] Y. Wang (研究生), X. Qian and W. Zhou*, "Transformer-Prompted Network: Efficient Audio–Visual Segmentation via Transformer and Prompt Learning," IEEE Signal Processing Letters, vol. 32, pp. 516–520, 2025.

[30] C. Liu (研究生), W. Zhou*, Y. Chen and J. Lei, "Asymmetric Deeply Fused Network for Detecting Salient Objects in RGB-D Images," IEEE Signal Processing Letters, vol. 27, pp. 1620–1624, 2020.

[31] C. Li (研究生), C. Xu, W. Zhou*, “FDFNet-S*: frequency domain fusion networks for RGB-D mirror segmentation by contrastive knowledge refinement,” Multimedia Systems, vol. 31, no. 3, 2025, Art. no. 186.

[32] X. Fan (研究生), W. Zhou*, “Multidimensional knowledge distillation for multimodal scene classification of remote sensing images,” Digital Signal Processing, vol. 157, 2025, Art. no. 104876.

[33] J. Yang (研究生), W. Zhou*, “Cross-attention fusion and edge-guided fully supervised contrastive learning network for rail surface defect detection,” Applied Intelligence, vol. 55, 2025, Art. no. 421.

[34] Z. Tu (研究生), X. Qian and W. Zhou*, "SACNet: Saliency-Aided Aggregation Consensus Network for RGB-D Co-Salient Object Detection," IEEE Signal Processing Letters, vol. 32, pp. 2000–2004, 2025.

[35] Y. Wu (研究生), F. Qiang, W. Zhou* and W. Yan, "PFCNet: Enhancing Rail Surface Defect Detection With Pixel-Aware Frequency Conversion Networks," IEEE Signal Processing Letters, vol. 32, pp. 606–610, 2025.

[36] Y. Lv (研究生), W. Zhou*, J. Lei, L. Ye, T. Luo, “Attention-based fusion network for human eye-fixation prediction in 3D images,” Optics Express, vol. 27, no. 23, 2019, pp. 34056–34066.

[37] Y. Zhu (研究生), W. Zhou*, Q. Li and L. Yu, "Parallax-Estimation-Enhanced Network With Interweave Consistency Feature Fusion for Binocular Salient Object Detection," IEEE Signal Processing Letters, vol. 28, pp. 927–931, 2021.

[38] Y. Yue (研究生), W. Zhou*, J. Lei and L. Yu, "Two-Stage Cascaded Decoder for Semantic Segmentation of RGB-D Images," IEEE Signal Processing Letters, vol. 28, pp. 1115–1119, 2021.

[39] J. Wu (研究生), W. Zhou*, “Boundary-enhanced attention-aware network for detecting salient objects in RGB-depth images,” Journal of Electronic Imaging, vol. 60 , no. 32, 2021, pp. 63032–0630.

[40] Q. Guo (研究生), W. Zhou*, J. Lei and L. Yu, "TSFNet: Two-Stage Fusion Network for RGB-T Salient Object Detection," IEEE Signal Processing Letters, vol. 28, pp. 1655–1659, 2021

[41] F. Sun (研究生), W. Zhou*, L. Ye and L. Yu, "Hierarchical Decoding Network Based on Swin Transformer for Detecting Salient Objects in RGB-T Images," IEEE Signal Processing Letters, vol. 29, pp. 1714–1718, 2022.

[42] Y. Yue (研究生), W. Zhou*, J. Lei and L. Yu, "RTLNet: Recursive Triple-Path Learning Network for Scene Parsing of RGB-D Images," IEEE Signal Processing Letters, vol. 29, pp. 429–433, 2022

[43] Y. Pan (研究生), W. Zhou*, L. Ye, L. Yu, “HFFNet: hierarchical feature fusion network for blind binocular image quality prediction,” Applied Optics, vol. 61, no. 26, pp.7602–7607, 2022.

[44] J. Liu (研究生), W. Zhou*, Y. Cui, L. Yu, T. Luo, “GCNet: Grid-like context-aware network for RGB-thermal semantic segmentation,” Neurocomputing, vol. 506, pp. 60–67, 2022.

[45] J. Wu (研究生), W. Zhou*, W. Qiu and L. Yu, "Depth Repeated-Enhancement RGB Network for Rail Surface Defect Inspection," IEEE Signal Processing Letters, vol. 29, pp. 2053–2057, 2022.

[46] E. Yang (研究生), W. Zhou*, X. Qian and L. Yu, "MGCNet: Multilevel Gated Collaborative Network for RGB-D Semantic Segmentation of Indoor Scene," IEEE Signal Processing Letters, vol. 29, pp. 2567–2571, 2022.

[47] S. Dong (研究生), W. Zhou*, X. Qian and L. Yu, "GEBNet: Graph-Enhancement Branch Network for RGB-T Scene Parsing," IEEE Signal Processing Letters, vol. 29, pp. 2273–2277, 2022.

[48] G. Xu (研究生), W. Zhou, X. Qian, L. Ye, J. Lei, L. Yu, “CCFNet: Cross-complementary fusion network for RGB-D scene parsing of clothing images,” Journal of Visual Communication and Image Representation, vol. 90, 2023, Art. no. 103727. 

[49] J. Wu (研究生), W. Zhou*, X. Qian, J. Lei, L. Yu, T. Luo, “MFENet: Multitype fusion and enhancement network for detecting salient objects in RGB-T images,” Digital Signal Processing, vol. 133, 2023, Art. no. 103827.

[50] J. Jin (研究生), W. Zhou*, R. Yang, L. Ye and L. Yu, "Edge Detection Guide Network for Semantic Segmentation of Remote-Sensing Images," IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, 2023, Art no. 5000505.

[51] J. Ma (研究生), W. Zhou*, J. Lei and L. Yu, "Adjacent Bi-Hierarchical Network for Scene Parsing of Remote Sensing Images," IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1–5, 2023, Art no. 3000705.

[52] J. Wu (研究生), W. Zhou*, X. Qian, J. Lei, L. Yu, T. Luo, “MENet: Lightweight multimodality enhancement network for detecting salient objects in RGB-thermal images,” Neurocomputing, 527, 2023, pp. 119–129.

[53] G. Xu (研究生), W. Zhou*, X. Qian, Y. Zhang, J. Lei, L. Yu, “THCANet: Two-layer hop cascaded asymptotic network for robot-driving road-scene semantic segmentation in RGB-D images,” Digital Signal Processing, vol. 136, 2023, Art. no. 104011.

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[55] J. Liu (研究生), W. Zhou*, M. Fang, S. Mao, R. Yang, “Lightweight cross-guided contextual perceptive network for visible–infrared urban road scene parsing,” Infrared Physics & Technology, vol. 137, 2024, Art. no. 105167.

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