1 code implementation • 8 Aug 2023 • Huibing Wang, Tianxiang Cui, Mingze Yao, Huijuan Pang, Yushan Du
In order to facilitate the feature learning of the GAN-based Scene Synthesis model, we adopt an online learning strategy that collaboratively learns the synthesized images and original images.
no code implementations • 6 Jan 2023 • Huibing Wang, Mingze Yao, Guangqi Jiang, Zetian Mi, Xianping Fu
To address the above issues, we propose a hashing algorithm based on auto-encoders for multi-view binary clustering, which dynamically learns affinity graphs with low-rank constraints and adopts collaboratively learning between auto-encoders and affinity graphs to learn a unified binary code, called Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering (GCAE).
no code implementations • 20 Jun 2020 • Jinjia Peng, Yang Wang, Huibing Wang, Zhao Zhang, Xianping Fu, Meng Wang
For PAL, a data adaptation module is employed for source domain, which generates the images with similar data distribution to unlabeled target domain as ``pseudo target samples''.
Unsupervised Vehicle Re-Identification Vehicle Re-Identification
no code implementations • 14 Jun 2020 • Xiangzhu Meng, Lin Feng, Huibing Wang
Unlike existing methods with additive parameters, the proposed method could automatically allocate a suitable weight for each view in multi-view information fusion.
5 code implementations • 5 May 2020 • Andreas Lugmayr, Martin Danelljan, Radu Timofte, Namhyuk Ahn, Dongwoon Bai, Jie Cai, Yun Cao, Junyang Chen, Kaihua Cheng, SeYoung Chun, Wei Deng, Mostafa El-Khamy, Chiu Man Ho, Xiaozhong Ji, Amin Kheradmand, Gwantae Kim, Hanseok Ko, Kanghyu Lee, Jungwon Lee, Hao Li, Ziluan Liu, Zhi-Song Liu, Shuai Liu, Yunhua Lu, Zibo Meng, Pablo Navarrete Michelini, Christian Micheloni, Kalpesh Prajapati, Haoyu Ren, Yong Hyeok Seo, Wan-Chi Siu, Kyung-Ah Sohn, Ying Tai, Rao Muhammad Umer, Shuangquan Wang, Huibing Wang, Timothy Haoning Wu, Hao-Ning Wu, Biao Yang, Fuzhi Yang, Jaejun Yoo, Tongtong Zhao, Yuanbo Zhou, Haijie Zhuo, Ziyao Zong, Xueyi Zou
This paper reviews the NTIRE 2020 challenge on real world super-resolution.
no code implementations • 3 May 2020 • Kai Zhang, Shuhang Gu, Radu Timofte, Taizhang Shang, Qiuju Dai, Shengchen Zhu, Tong Yang, Yandong Guo, Younghyun Jo, Sejong Yang, Seon Joo Kim, Lin Zha, Jiande Jiang, Xinbo Gao, Wen Lu, Jing Liu, Kwangjin Yoon, Taegyun Jeon, Kazutoshi Akita, Takeru Ooba, Norimichi Ukita, Zhipeng Luo, Yuehan Yao, Zhenyu Xu, Dongliang He, Wenhao Wu, Yukang Ding, Chao Li, Fu Li, Shilei Wen, Jianwei Li, Fuzhi Yang, Huan Yang, Jianlong Fu, Byung-Hoon Kim, JaeHyun Baek, Jong Chul Ye, Yuchen Fan, Thomas S. Huang, Junyeop Lee, Bokyeung Lee, Jungki Min, Gwantae Kim, Kanghyu Lee, Jaihyun Park, Mykola Mykhailych, Haoyu Zhong, Yukai Shi, Xiaojun Yang, Zhijing Yang, Liang Lin, Tongtong Zhao, Jinjia Peng, Huibing Wang, Zhi Jin, Jiahao Wu, Yifu Chen, Chenming Shang, Huanrong Zhang, Jeongki Min, Hrishikesh P. S, Densen Puthussery, Jiji C. V
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results.
no code implementations • 16 Mar 2020 • Huibing Wang, Jinjia Peng, Guangqi Jiang, Fengqiang Xu, Xianping Fu
In TCPM, triplet-center loss is introduced to ensure each part of vehicle features extracted has intra-class consistency and inter-class separability.
no code implementations • 12 Jan 2020 • Huibing Wang, Jinjia Peng, Dongyan Chen, Guangqi Jiang, Tongtong Zhao, Xianping Fu
Specially, an attribute-guided module is proposed in AGNet to generate the attribute mask which could inversely guide to select discriminative features for category classification.
no code implementations • 21 Dec 2019 • Jinjia Peng, Guangqi Jiang, Dongyan Chen, Tongtong Zhao, Huibing Wang, Xianping Fu
Vehicle re-identification (reID) often requires recognize a target vehicle in large datasets captured from multi-cameras.
no code implementations • 11 Dec 2019 • Guangqi Jiang, Huibing Wang, Jinjia Peng, Dongyan Chen, Xianping Fu
To address these problems, we propose a novel binary code algorithm for clustering, which adopts graph embedding to preserve the original data structure, called (Graph-based Multi-view Binary Learning) GMBL in this paper.
no code implementations • 23 Nov 2019 • Huibing Wang, Yang Wang, Zhao Zhang, Xianping Fu, Zhuo Li, Mingliang Xu, Meng Wang
With the popularity of multimedia technology, information is always represented or transmitted from multiple views.
no code implementations • 15 Nov 2019 • Xiangzhu Meng, Huibing Wang, Lin Feng
Two schemes based on pairwise-consensus and centroid-consensus are separately proposed to force multiple views to learn from each other and then an iterative alternating strategy is developed to obtain the optimal solution.
no code implementations • 10 Oct 2019 • Haohao Li, Huibing Wang
The proposed method aims to find a subspace for the high-dimensional data, in which the smooth reconstructive weights are preserved as much as possible.
no code implementations • 10 Jul 2019 • Yuxiao Yan, Yang Yan, Jinjia Peng, Huibing Wang, Xianping Fu
Different from the previous methods, this paper try to purify real image by extracting discriminative and robust features to convert outdoor real images to indoor synthetic images.
no code implementations • 20 May 2019 • Lin Feng, Xiangzhu Meng, Huibing Wang
Even though most of them can achieve satisfactory performance in some certain situations, they fail to fully consider the information from multiple views which are highly relevant but sometimes look different from each other.
no code implementations • 10 May 2019 • Lin Feng, Caifeng Liu, Shenglan Liu, Huibing Wang
Traditional face alignment based on machine learning usually tracks the localizations of facial landmarks employing a static model trained offline where all of the training data is available in advance.
no code implementations • 30 Apr 2019 • Jinjia Peng, Huibing Wang, Xianping Fu
To address this problem, this paper proposes a domain adaptation framework for vehicle reID (DAVR), which narrows the cross-domain bias by fully exploiting the labeled data from the source domain to adapt the target domain.
no code implementations • 1 Apr 2019 • Huibing Wang, Jinjia Peng, Xianping Fu
However, facing with features from multiple views, it's difficult for most dimension reduction methods to fully comprehended multi-view features and integrate compatible and complementary information from these features to construct low-dimensional subspace directly.
no code implementations • 19 Mar 2019 • Tongtong Zhao, Yuxiao Yan, Jinjia Peng, Huibing Wang, Xianping Fu
To solve this problem, the previous method learned a model to improve the realism of the synthetic images.
no code implementations • 19 Mar 2019 • Jinjia Peng, Huibing Wang, Tongtong Zhao, Xianping Fu
Vehicle re-identification (reID) is to identify a target vehicle in different cameras with non-overlapping views.
no code implementations • 19 Mar 2019 • Huibing Wang, Jinjia Peng, Xianping Fu
With the development of multimedia time, one sample can always be described from multiple views which contain compatible and complementary information.
no code implementations • 14 Mar 2019 • Tongtong Zhao, Yuxiao Yan, Ibrahim Shehi Shehu, Xianping Fu, Huibing Wang
To solve this problem, the previous method learned a model to improve the realism of the synthetic image.
1 code implementation • 24 Jan 2019 • Caifeng Liu, Lin Feng, Guochao Liu, Huibing Wang, Shenglan Liu
Music genre recognition based on visual representation has been successfully explored over the last years.
no code implementations • 10 Jan 2019 • Huibing Wang, Haohao Li, Xianping Fu
To address these issue, a novel multi-feature distance metric learning method for non-rigid 3D shape retrieval is presented in this study, which can make full use of the complimentary geometric information from multiple shape features by utilizing the KL-divergences.
no code implementations • 5 Jan 2019 • Huibing Wang, Haohao Li, Xianping Fu
Therefore, it is essential to fully exploit the complementary information embedded in multiple views to enhance the performances of many tasks.
1 code implementation • ECCV 2018 • Ruoxi Deng, Chunhua Shen, Shengjun Liu, Huibing Wang, Xinru Liu
Recent methods for boundary or edge detection built on Deep Convolutional Neural Networks (CNNs) typically suffer from the issue of predicted edges being thick and need post-processing to obtain crisp boundaries.
no code implementations • 26 Jul 2018 • Jing Zhang, Huibing Wang, Yong-Gong Ren
Therefore, our tracking method can fully learn both of the target object and background information to enhance the tracking performance, and it is evaluated in 20 challenge image sequences with different attributes including illumination, occlusion, deformation, etc., which achieves better performance than several state-of-the-art methods in terms of effectiveness and robustness.
no code implementations • 25 Jul 2018 • Huibing Wang, Lin Feng, Adong Kong, Bo Jin
With the development of feature extraction technique, one sample always can be represented by multiple features which locate in high-dimensional space.
1 code implementation • 4 Apr 2017 • Qichang Hu, Huibing Wang, Teng Li, Chunhua Shen
By applying our method to several fine-grained car recognition data sets, we demonstrate that the proposed method can achieve better performance than recent approaches in the literature.
Ranked #1 on Fine-Grained Image Classification on CarFlag-563