Visual Recognition

• Zilin Gao, Jiangtao Xie, Qilong Wang and Peihua Li. Global Second-order Pooling Convolutional Networks. IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR), 2019. [Code in PyTorch][pdf][bibtex]

• Qilong Wang, Peihua Li, Qinghua Hu, Pengfei Zhu, Wangmeng Zuo. Deep Global Generalized Gaussian Networks. IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR), 2019. [Code in PyTorch][pdf][bibtex]

• Peihua Li, Jiangtao Xie, Qilong Wang and Zilin Gao. Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization. IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 947-955, 2018. [Code in PyTorch][Code in TensorFlow][Code in MatConvNet][pdf][bibtex]

• Qilong Wang, Zilin Gao, Jiangtao Xie, Wangmeng Zuo and Peihua Li. Global Gated Mixture of Second-order Pooling for Improving Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems (NIPS), 2018. [Code in PyTorch] [pdf][bibtex]

• Peihua Li, Jiangtao Xie, Qilong Wang and Wangmeng Zuo. Is Second-order Information Helpful for Large-scale Visual Recognition? IEEE Int. Conf. on Computer Vision (ICCV),  pp. 2070-2078, 2017. [Code in MatConvNet][pdf][bibtex]

• Peihua Li, Qilong Wang, Hui Zeng, Lei Zhang. Local Log-Euclidean Multivariate Gaussian Descriptor and Its Application to Image Classification. IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 39(4): 803-817, 2017. [pdf][code][bibtex]

• Qilong Wang, Peihua Li, Lei Zhang. G2DeNet: Global Gaussian Distribution Embedding Network and Its Application to Visual Recognition. The IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 2730-2739. (Oral presentation) [pdf][code][bibtex]

• Hao Wang, Qilong Wang, Mingqi Gao, Peihua Li, Wangmeng Zuo. Multi-Scale Location-Aware Kernel Representation for Object Detection. IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 1248-1255, 2018. [pdf][code][bibtex]

• Hongliang Yan, Yukang Ding, Peihua Li, Qilong Wang, Yong Xu, Wangmeng Zuo. Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2272-2281. [pdf][code][bibtex]

• Qilong Wang, Peihua Li, Wangmeng Zuo, Lei Zhang. RAID-G: Robust Estimation of Approximate Infinite Dimensional Gaussian with Application to Materiel Recognition. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4433-4441, 2016. [pdf][code][bibtex][slides][poster]

• Peihua Li, Hui Zeng, Qilong Wang, Simon C. K. Shiu, Lei Zhang. High-order Local Pooling and Encoding Gaussians Over A Dictionary of Gaussians. IEEE Trans. on Image Processing (TIP), 26(7):3372-3384, 2017. [pdf] [code][bibtex]

• Qilong Wang, Peihua Li, Lei Zhang,Wangmeng Zuo. Towards Effective Codebookless Model for Image Classification. Journal of Pattern Recognition, in press, 2016.[pdf] [code][bibtex]

• Hua Hao, Qilong Wang, Peihua Li, Lei zhang. Evaluation of Ground Distances and Features in EMD-based GMM Matching for Texture Classification. Journal of Pattern Recognition, accepted for publication, 2016. [pdf] [codes][bibtex]

• Peihua Li, Xiaoxiao Lu, Qilong Wang. From Dictionary of Visual Words to Subspaces: Locality-constrained Affine Subspace Coding. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.[pdf] [Project] [bibtex]

• Peihua Li, Qilong Wang, Lei Zhang. A Novel Earth Mover's Distance Methodology for Image Matching with Gaussian Mixture Models. IEEE Int. Conf. on Computer Vision (ICCV), 2013. [Project page]

• Peihua Li, Qilong Wang, Wangmeng Zuo, Lei Zhang. Log-Euclidean Kernels for Sparse Representation and Dictionary Learning. IEEE Int. Conf. on Computer Vision (ICCV), 2013. [Project page]

• Peihua Li, Qilong Wang. Local Log-Euclidean Covariance Matrix (L2ECM) for Image Representation and Its Applications. European Conf. on Computer Vision, ECCV (3) 2012 : 469-482. Preprint version and published version. [Matlab source code, Poster].

• Yuwei Wu, Yunde Jia, Peihua Li, Jian Zhang, and Junsong Yuan. Manifold Kernel Sparse Representation of Symmetric Positive Definite Matrices and Its Applications. IEEE Transactions on Image Processing (TIP), accepted for publication, 2015.

• Qilong Wang, Wangmeng Zuo, Lei Zhang, Peihua Li. Shrinkage Expansion Adaptive Metric Learning. European Conf. on Computer Vision, ECCV (7) 2014 : 456-471 [pdf, supplement, code]

• Guanglei Yang, Hui Zeng, Peihua Li, Lei Zhang. High-order Information for Robust Iris Recognition Under Less Controlled Conditions. Int. Conf. on Image Processing, accepted for publication, 2015.

• Peihua Li, Hongwei Ma. Iris Recognition in Non-Ideal Imaging Conditions. Pattern Recognition Letters, 2012, 33(8):1000-1005

• Peihua Li, Xiaomin Liu, Nannan Zhao. Weighted Co-occurrence Phase Histogram for Iris Recognition. Pattern Recognition Letters , 2012, 33(8):1012-1018.

• Peihua Li, Xiaomin Liu, Lijuan Xiao, Qi Song. Robust and Accurate Iris Segmentation in Very Noisy Iris Images. Image and Vision Computing, 2010, 28(2):246-253.

• Peihua Li. Tensor-SIFT based Earth Mover's Distance for Contour Tracking. Journal of Mathematical Imaging and Vision, 2013,46(1): 44-65.

• Peihua Li. An Efficient Particle Filter-Based Tracking Method Using Graphics Processing Unit (GPU). Journal of Signal Processing Systems for Signal Image and Video Technology, 2012, 68(3): 317-332.

• Peihua Li. An Adaptive Binning Color Model For Mean Shift Tracking. IEEE Trans. Circuits and Systems for Video Technology, 18(9): 1293-1299, 2008.

• Peihua Li, Tianwen Zhang, Bo Ma. Unscented Kalman Filter for Visual Curve Tracking. Image and Vision Computing. 2004, 22(2): 157-164 .

• Peihua Li, Tianwen Zhang, Arthur E.C. Pece. Visual Contour Tracking Based on Particle Filters. Image and Vision Computing. 2003, 21 (1): 111-123.

• Peihua Li, Qilong Wang. Robust Registration-based Tracking by Sparse Representation with Model Update. 11th Asian Conf. on Computer Vision (ACCV), 205-216. Daejeon, Korean, 2012.

• Peihua Li, Qi Sun. Tensor-based Covariance Matrices for Object Tracking. IEEE Workshop on Visual Surveillance, in Conjunction with ICCV 2011. Spain, November 2011.