Publications

Tutorials

2016

• S. Zheng, A. Arnab, B. Romera-Paredes. Holistic Image Understanding with Deep Learning and Dense Random Fields, European Conference on Computer Vision (ECCV), Amsterdam, Netherlands, 2016. (Guest tutorial)

Publications [by years] [by topics] [Google_Scholar_Profile] [Bibtex]

2019

• B.A. Plummer, M. H. Kiapour, S. Zheng, R. Piramuthu. Give me a hint! Navigating Image Databases using Human-in-the-loop Feedback. WACV 2019. (arXiv:1809.08714)
• H. Wu, S. Zheng, J. Zhang, and K. Huang. GP-GAN: Towards Realistic High-Resolution Image Blending. ACM Multimedia, Nice, France, 2019. ( arXiv:1703.07195)

2018

• S. Zheng, F. Yang, K. Kaipour, R. Piramuthu. ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations. ACM Multimedia, Seoul, South Korea, 2018.(Full Paper, Oral Presentation) [data][leaderboard][Slides PDF 48.44MB]
• B.A. Plummer, P. Kordas, M. H. Kiapour, S. Zheng, R. Piramuthu, S. Lazebnik.Conditional Image-Text Embedding Networks. ECCV 2018.[code]

  H. Wu, S. Zheng, J. Zhang, K. Huang. Fast End-to-End Trainable Guided Filter. CVPR. 2018. [code]
• A. Arnab*, S. Zheng*, S. Jayasumana, B. Romera-Paredes, M. Larsson, A. Kirillov, B. Savchynskyy, C. Rother, F. Kahl, and P. H. S. Torr.  Conditional Random Fields meet Deep Neural Networks for Semantic Segmentation: combing probabilistic graphical models with deep learning for structured prediction. IEEE Signal Processing Magazine Special Issue in Deep Learning for Visual Understanding White Paper. vol 3, issue 1, pp. 37-52, 2018. (* joint author)

2017

• M. Larsson, A. Arnab, F. Kahl, S. Zheng, and P. H. S. Torr. Learning Arbitrary Potentials in CRFs with Gradient Descent. International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2017.

2016

• S. Zheng. Holistic image understanding with deep learning and dense random fields. DPhil thesis, University of Oxford, 2016.

• A. Arnab, S. Jayasumana, S. Zheng, P. H. S. Torr. Higher Order Conditional Random Fields in Deep Neural Networks. European Conference on Computer Vision (ECCV), Amsterdam, Netherlands, 2016. (Technical report appears on arXiv:1511.08119. 2015)

• J.Thewlis, S. Zheng, P. H. S. Torr, A. Vedaldi. Fully-trainable deep matching. British Machine Vision Conference (BMVC), York, United Kingdom, 2016. (oral)

• A. Kirillov, D. Schlesinger, S. Zheng, B.  Savchynskyy, P. H. S. Torr, C. Rother. Efficient Likelihood Learning of a Generic CNN-CRF Model for Semantic Segmentation. Asian Conference on Computer Vision (ACCV), 2016. (Technical report appears on https://arxiv.org/abs/1511.05067)

2015

• S. Zheng*, S. Jayasumana*, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, P. H. S. Torr. Conditional Random Fields as Recurrent Neural Networks. International Conference on Computer Vision (IEEE ICCV), Santiago, Chile, 2015.[project][live_demo][code][bib] (* shared equal contribution)

• M. Cheng, V. Prisacariu, S. Zheng, P. Torr, C Rother. “DenseCut: Densely Connected CRFs for Realtime GrabCut” , Computer Graphics Forum (CGF), special issue of Pacific Graphics, Beijing, China, 2015. (oral & journal) [Project][pdf][bib][code]

• S. Zheng, V. Prisacariu, M Averkiou, M. Cheng, N. Mitra, J. Shotton, P. Torr, C. Rother. “Object Proposal Estimation in Depth Images using Compact 3D Shape Manifolds”, German Conference on Pattern Recognition (GCPR), Aachen, Germany, 2015. (oral)[pdf][bib]

2014

• S. Zheng, M. Cheng, J. Warrell, P. Sturgess, V. Vineet, C Rother, P. H. S. Torr. “Dense Semantic Image Segmentation with Objects and Attributes” , IEEE International Conference on Computer Vision and Pattern Recognition (IEEE CVPR), Columbus, Ohio, U.S.A., 2014. [pdf][bib][Project][code]
• M.M. Cheng*, S. Zheng*, W. Lin, V. Vineet, P. Sturgess, N. Crook, N. Mitra, and P. H. S. Torr. “ImageSpirit: Verbal Guided Image Parsing” , ACM Transactions on Graphics (ACM TOG), Los Angeles, California, U.S.A., 2014. [pdf][bib][code]  (* shared equal contribution)

2013

• S. Zheng, P. Sturgess, and P. H. S. Torr, “Approximate Structured Output Learning for Constrained Local Models with Application to Real-time Facial Feature Detection and Tracking on Low-power Devices“, IEEE International Conference on Automatic Face and Gesture Recognition (IEEE FG), Shanghai, China, 2013.(spotlight) [pdf][bib]

• M. Cheng, J.Warrell, W. Lin, S. Zheng, V. Vineet, and N. Crook, “Efficient Salient Region Detection with Soft Image Abstraction,” in International Conference on Computer Vision (IEEE ICCV), Sydney, Australia, 2013. [pdf][exe][bib]

• W. Lin, M. Cheng, S. Zheng, J. Lu, and N. Crook. “Robust Non-parametric Data Fitting for Correspondence Modeling“, in International Conference on Computer Vision (IEEE ICCV),Sydney, Australia, 2013. [pdf][bib][[MATLABCode]

2012

• S. Zheng, K. Huang, T. Tan and D. Tao, ” A Cascade Fusion Scheme for Gait and Cumulative Foot Pressure Image Recognition“,45 (10), Pattern Recognition, pp.3603-3610, 2012.[CASIAGaitPartD][bib]

2011

• S. Zheng, J. Zhang, K. Huang, R. He, and T. Tan, “Robust View Transformation Model for Gait Recognition,” in International Conference on Image Processing, Brussels, Belgium, 2011.[GEI_CASIAGaitPartB][code_zip_14.99KB][bib]

• S. Zheng, K. Huang, and T. Tan., “Evaluation framework on translation-invariant representation for cumulative foot pressure image,” in International Conference on Image Processing, Brussels, Belgium, 2011.[dataset][bib]

• S. Zheng, B. Xie, D. Tao, and K. Huang, “Multi-View Pedestrian Recognition using Shared Dictionary Learning with Group Sparsity,” in International Conference on Neural Information Processing, Shanghai, China, 2011. (oral) [slides][dataset][code][bib]

• J. Zhang, Y. Yu, S. Zheng, and K. Huang, “An Empirical Study of Visual Features for Part-Based Model,” in  Asian Conference on Pattern Recognition, Beijing, China, 2011. (poster) [bib] -How we win PASCAL VOC Challenge with improved Part Based Model?

2010

• Y. Huang, S. Zheng, W. Ren, Y. Yu, J. Zhang, K. Huang, and T. Tan, “SVM classifier and saliency coding for Image classification,” in PASCAL Visual Object Challenge Workshop, European Conference on Computer Vision (ECCVW), Crete, Greece, 2010.

• Y. Yu, J. Zhang, Y. Huang, S. Zheng, W. Ren, K. Huang, and T. Tan, “Object Detection by Context and Boosted HOG-LBP“, in PASCAL Visual Object Challenge Workshop, European Conference on Computer Vision (ECCVW), Crete, Greece, 2010. [bib]

Notes: IEEE TPAMI is the #1 IEEE publication. ACM TOG is the most-cited ACM Transaction. IEEE TPAMI, ACM TOG, and IEEE TVCG are CCF Tier-A Journals. SIGGRAPH, CVPR, and ICCV are CCF Tier-A Conferences and they have CiteSeer impact factor ranking top 0.7%, top 5%, and top 5% of all computer science journals and conferences respectively.

Patents

• S. Zheng, et.al. “Camera Platform and Object Inventory Control“
• S. Zheng, et.al. “Intelligent online personal assistant with image text localization“
• S. Zheng, et.al. “Generating personalized banner images using machine learning“
• S. Zheng, et.al. “Generating a digital image using a generative adversarial network“
• S.Zheng et.al. “Camera platform incorporating schedule and stature“
• S.Zheng et.al. “Computer Vision and Image Characteristic Search“