Publications

Conference Papers

• H. Wang, Z. Li and W. Zhang.
  Get the best of both worlds: Improving accuracy and transferability by Grassmann class representation.
  Proc. International Conf. on Computer Vision (ICCV), 2023. [code]
  
  

• Y. Li, X. Wang, L. Yang, L. Feng, W. Zhang and Y. Gao.
  Diverse cotraining makes strong semi-supervised segmentor.
  Proc. International Conf. on Computer Vision (ICCV), 2023.
  
  

• X. Wang, X. Yang, S. Zhang, Y. Li, L. Feng, S. Fang, C. Lyu, K. Chen and W. Zhang.
  Consistent-Teacher: Towards reducing inconsistent pseudo-targets in semi-supervised object detection.
  Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2023. (Highlight, Acceptance rate: 2.5%) [code]
  
  

• L. Yang, L. Qi, L. Feng, W. Zhang and Y. Shi.
  Revisiting weak-to-strong consistency in semi-supervised semantic segmentation.
  Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2023.
  
  

• L. Zhu, X. Wang, Z. Ke, W. Zhang and R. Lau.
  BiFormer: Vision transformer with bi-level routing attention.
  Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2023. [code]
  
  

• J. Yang, W. Peng, X. Li, Z. Guo, L. Chen, B. Li, Z. Ma, K. Zhou, W. Zhang, C.C. Loy and Z. Liu.
  Panoptic video scene graph generation.
  Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2023.
  
  

• J. Yang, P. Wang, D. Zou, Z. Zhou, K. Ding, W. Peng, H. Wang, G. Chen, B. Li, Y. Sun, X. Du, K. Zhou, W. Zhang, D. Hendrycks, Y. Li and Z. Liu.
  OpenOOD: Benchmarking generalized out-of-distribution detection.
  Proc. NeurIPS 2022 Datasets and Benchmarks Track, 2022. [code]
  
  

• J. Yang, Y. Z. Ang, Z. Guo, K. Zhou, W. Zhang and Z. Liu.
  Panoptic scene graph generation.
  Proc. European Conf. on Computer Vision (ECCV), 2022.
  
  

• H. Wang, Z. Li, L. Feng and W. Zhang.
  ViM: Out-of-distribution with virtual-logit matching.
  Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2022. [code] [Project Page] [blog (in Chinese)]
  
  

• Y. Li, Y. Duan, Z. Kuang, Y. Chen, W. Zhang and X. Li.
  Uncertainty estimation via response scaling for pseudo-mask noise mitigation in weakly-supervised semantic segmentation.
  Proc. AAAI Conference on Artificial Intelligence (AAAI), 2022. [code]
  
  

• Z. Kuang, H. Sun, Z. Li, X. Yue, T.H. Lin, J. Chen, H. Wei, Y. Zhu, T. Gao, W. Zhang, K. Chen, W. Zhang and D. Lin.
  MMOCR: A comprehensive toolbox for text detection, recognition and understanding.
  Proc. ACM Multimedia (MM) Open Source Software Competition, 2021. [code]
  
  

• X. Yue, S. Sun, Z. Kuang, M. Wei, P. Torr, W. Zhang and D. Lin.
  Vision transformer with progressive sampling.
  Proc. International Conf. on Computer Vision (ICCV), 2021. [code]
  
  

• J. Yang, H. Wang, L. Feng, X. Yan, H. Zheng, W. Zhang and Z. Liu.
  Semantically coherent out-of-distribution detection.
  Proc. International Conf. on Computer Vision (ICCV), 2021. [code] [Project Page]
  
  

• Y. Li, Z. Kuang, L. Liu, Y. Chen and W. Zhang.
  Pseudo-mask matters in weakly-supervised semantic segmentation.
  Proc. International Conf. on Computer Vision (ICCV), 2021. [code]
  
  

• L. Liu, S. Zhang, Z. Kuang, J.-H. Xue, A. Zhou, X. Wang, Y. Chen, W. Yang, Q. Liao and W. Zhang.
  Group fisher pruning for practical network compression.
  International Conf. on Machine Learning (ICML), 2021. (Acceptance rate: 21.5%) [paper] [code]
  
  

• Y. Zhu, J. Chen, L. Liang, Z. Kuang, L. Jin and W. Zhang.
  Fourier contour embedding for arbitrary-shaped text detection.
  Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2021. [code in MMOCR]
  
  

• L. Liu, Y. Li, Z. Kuang, J.-H. Xue, Y. Chen, W. Yang, Q. Liao and W. Zhang.
  Towards impartial multi-task learning.
  International Conf. on Learning Representations (ICLR), 2021. [video]
  
  

• J. Yang, W. Chen, L. Feng, X. Yan, H. Zheng and W. Zhang.
  Webly supervised image classification with metadata: Automatic noisy label correction via visual-semantic graph.
  Proc. ACM Multimedia (MM), 2020. (Oral presentation, Acceptance rate: 8.9%) [paper]
  
  

• J. Yang, L. Feng, W. Chen, X. Yan, H. Zheng, P. Luo and W. Zhang.
  Webly supervised image classification with self-contained confidence.
  Proc. European Conf. on Computer Vision (ECCV), 2020. (Acceptance rate: 27%) [paper] [code]
  
  

• X. Yue, Z. Kuang, C. Lin, H. Sun and W. Zhang.
  RobustScanner: Dynamically enhancing positional clues for robust text recognition.
  Proc. European Conf. on Computer Vision (ECCV), 2020. (Acceptance rate: 27%) [paper] [blog (in Chinese)] [code in MMOCR]
  
  

• J. Wu, Z. Kuang, L. Wang, W. Zhang and G. Wu.
  Context-aware RCNN: A baseline for action detection in videos.
  Proc. European Conf. on Computer Vision (ECCV), 2020. (Acceptance rate: 27%) [paper] [code]
  
  

• X. Wang, S. Zhang, Z. Yu, L. Feng and W. Zhang.
  Scale-equalizing pyramid convolution for object detection.
  Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2020. [blog (in Chinese)] [code]
  
  

• C. Lin, S. Wang, D. Xu, Y. Lu and W. Zhang.
  Object instance mining for weakly supervised object detection.
  Proc. AAAI Conference on Artificial Intelligence (AAAI), 2020. [github] [blog (in Chinese)]
  
  

• Z. Kuang, Y. Gao, G. Li, P. Luo, Y. Chen, L. Lin and W. Zhang.
  Fashion retrieval via graph reasoning networks on a similarity pyramid.
  Proc. International Conf. on Computer Vision (ICCV), 2019. (Oral presentation)
  
  

• Y. Xu, J. Duan, Z. Kuang, X. Yue, H. Sun, Y. Guan and W. Zhang.
  Geometry normalization networks for accurate scene text detection.
  Proc. International Conf. on Computer Vision (ICCV), 2019. [github] [blog (in Chinese)]
  
  

• Z. Chen, Z. Kuang, W. Zhang and K.-Y. K. Wong.
  Learning local similarity with spatial relations for object retrieval.
  Proc. ACM Multimedia (MM), 2019. (Acceptance rate: 26.5%)
  
  

• W. Yu, Z. Huang, W. Zhang, L. Feng and N. Xiao.
  Gradual network for single image de-raining.
  Proc. ACM Multimedia (MM), 2019. (Oral presentation)
  
  

• S. Tang, L. Feng, W. Shao, Z. Kuang, W. Zhang and Z. Lu.
  Learning efficient detector with semi-supervised adaptive distillation.
  British Machine Vision Conference (BMVC), 2019.
  
  

• Y. Li, Z. Kuang, Y. Chen and W. Zhang.
  Data-driven neuron allocation for scale aggregation networks.
  Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2019. [paper] [github] [blog (in Chinese)]
  
  

• S. Xie, J. Huang, L. Lei, C. Liu, Z. Ma, W. Zhang and L. Lin.
  NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning.
  International Conf. on Learning Representations (ICLR), 2019. [paper]
  
  

• L. Feng, Z. Li, Z. Kuang and W. Zhang.
  Extractive video summarizer with memory augmented neural networks.
  Proc. ACM Multimedia (MM), 2018. (Acceptance rate: 27.5%)
  
  

• Z. Zhang, Z. Kuang, P. Luo, L. Feng and W. Zhang.
  Temporal sequence distillation: Towards few-frame action recognition in videos.
  Proc. ACM Multimedia (MM), 2018. (Acceptance rate: 27.5%) [paper]
  
  

• X. Yue, Z. Kuang, Z. Zhang, Z. Chen, P. He, Y. Qiao and W. Zhang.
  Boosting up scene text detectors with guided CNN.
  British Machine Vision Conference (BMVC), 2018. (Oral presentation, acceptance rate: 6.5%) [paper]
  
  

• S. Tang, L. Feng, Z. Kuang, Y. Chen and W. Zhang.
  Fast video shot transition localization with deep structured models.
  Proc. Asian Conf. on Computer Vision (ACCV), 2018. [paper]
  
  

• K. Wang, L. Lin, C. Ren, W. Zhang and W. Sun.
  Convolutional memory blocks for depth data representation learning.
  Proc. International Joint Conf. on Artificial Intelligence (IJCAI), 2018.
  
  

• S. Sun, Z. Kuang, L. Sheng, W. Ouyang and W. Zhang.
  Optical flow guided feature: A fast and robust motion representation for video action recognition.
  Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2018. [paper] [code]
  
  

• Z. Chen, Z. Kuang, K.-Y. K. Wong and W. Zhang.
  Aggregated deep feature from activation clusters for particular object retrieval.
  Proc. ACM Multimedia (MM) Thematic Workshops, 2017.
  
  

• Z. Kuang, C. Huang and W. Zhang.
  Deeply learned rich coding for cross-dataset facial age estimation.
  Proc. IEEE Conf. on Computer Vision (ICCV) Workshop, 2015. (3rd place winner of ICCV 2015 apparent age estimation challenge)
  
  

• M. Chen, W. Li, W. Zhang and X. Wang.
  Dimensionality reduction with generalized linear models.
  Proc. International Joint Conf. on Artificial Intelligence (IJCAI), 2013.
  
  

• W. Zhang, X. Wang, D. Zhao and X. Tang.
  Graph degree linkage: Agglomerative clustering on a directed graph.
  Proc. European Conf. on Computer Vision (ECCV), 2012. [doi] [full version] [code] [github]
  
  

• W. Zhang, X. Wang and X. Tang.
  Coupled information-theoretic encoding for face photo-sketch recognition.
  Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2011. (Oral presentation, acceptance rate: 3.5%) [doi] [slides] [CUFSF Database]
  
  

• R. Liu, Z. Lin, W. Zhang and Z. Su.
  Learning PDEs for image restoration via optimal control.
  Proc. European Conf. on Computer Vision (ECCV), 2010. (Acceptance rate: 27%) [doi] [Supp. Material]
  
  

• W. Zhang, X. Wang and X. Tang.
  Lighting and pose robust face sketch synthesis.
  Proc. European Conf. on Computer Vision (ECCV), 2010. (Acceptance rate: 27%) [doi] [More Results] [Project Page]
  
  

Journal Articles

• A. Rao, L. Xu, Z. Li, Q. Huang, Z. Kuang, W. Zhang and D. Lin.
  A coarse-to-fine framework for automatic video unscreen.
  IEEE Trans. on Multimedia (TMM), 2022. [doi]
  
  

• Y. Gao, Z. Kuang, G. Li, P. Luo, Y. Chen, L. Lin and W. Zhang.
  Fashion retrieval via graph reasoning networks on a similarity pyramid.
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 45, no. 6, pp. 7019-7034, 2023. [doi]
  
  

• L. Liu, B. Wang, Z. Kuang, J.-H. Xue, Y. Chen, W. Yang, Q. Liao and W. Zhang.
  GenDet: Meta learning to generate detectors from few shots.
  IEEE Trans. on Neural Networks and Learning Systems (TNNLS), vol. 33, no. 8, pp. 3448-3460, 2022. [doi]
  
  

• L. Liu, Z. Kuang, Y. Chen, J.-H. Xue, W. Yang and W. Zhang.
  IncDet: In defense of elastic weight consolidation for incremental object detection.
  IEEE Trans. on Neural Networks and Learning Systems (TNNLS), vol. 32, no. 6, pp. 2306-2319, 2021. [doi]
  
  

• Y. Gao, Z. Kuang, G. Li, W. Zhang and L. Lin.
  Hierarchical reasoning network for human-object interaction detection.
  IEEE Transactions on Image Processing (TIP), vol. 30, pp. 8306-8317, 2021. [doi]
  
  

• W. Zhang, C. Cao, S. Chen, J. Liu and X. Tang.
  Style transfer via image component analysis.
  IEEE Trans. on Multimedia (TMM), vol. 15, no. 7, pp. 1594-1601, 2013. [doi]
  
  

• W. Zhang, D. Zhao and X. Wang.
  Agglomerative clustering via maximum incremental path integral.
  Pattern Recognition (PR), vol. 46, no. 11, pp. 3056-3065, 2013. [doi] [code] [github]
  
  

• R. Liu, Z. Lin, W. Zhang, K. Tang and Z. Su.
  Toward designing intelligent PDEs for computer vision: An optimal control approach.
  Image and Vision Computing (IVC), vol. 31, no. 1, pp. 43-56, 2013. [doi] [Preprint]
  
  

• Y. Li, M. Chen, Q. Li and W. Zhang.
  Enabling multi-level trust in privacy preserving data mining.
  IEEE Trans. on Knowledge and Data Engineering (TKDE), vol. 24, no. 9, pp. 1598-1612, 2012. [doi] [full version]
  
  

• M. Ben-Ezra, Z. Lin, B. Wilburn and W. Zhang.
  Penrose pixels for super-resolution.
  IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), vol. 33, no. 7, pp. 1370-1383, 2011. [doi]
  
  

• W. Zhang, Z. Lin and X. Tang.
  Learning semi-Riemannian metrics for semisupervised feature extraction.
  IEEE Trans. on Knowledge and Data Engineering (TKDE), vol. 23, no. 4, pp. 600-611, 2011. [doi]
  
  

• W. Zhang, Z. Lin and X. Tang.
  Tensor linear Laplacian discrimination (TLLD) for feature extraction.
  Pattern Recognition (PR), vol. 42, no. 9, pp. 1941-1948, 2009. [doi]
  
  

Technical Reports

• Z. Lin, W. Zhang and X. Tang.
  Designing partial differential equations for image processing by combining differential invariants.
  Microsoft Technical Report, MSR-TR-2009-192, 2009.
  
  

• Z. Lin, W. Zhang and X. Tang.
  Learning partial differential equations for computer vision.
  Microsoft Technical Report, MSR-TR-2008-189, 2008.
  
  

Patents

• US8,024,152. Tensor linear laplacian discrimination for feature extraction.

• WO2012027904, US9,569,699. System and method for synthesizing portrait sketch from a photo.

Copyright © 2023 Wayne Zhang