1 code implementation • CVPR 2021 • Yazhou Yao, Tao Chen, GuoSen Xie, Chuanyi Zhang, Fumin Shen, Qi Wu, Zhenmin Tang, Jian Zhang
To further mine the non-salient region objects, we propose to exert the segmentation network's self-correction ability.
1 code implementation • 4 Jun 2019 • Guodong Ding, Salman Khan, Zhenmin Tang, Jian Zhang, Fatih Porikli
With this insight, we design a novel Dispersion-based Clustering (DBC) approach which can discover the underlying patterns in data.
Ranked #19 on Unsupervised Person Re-Identification on Market-1501
1 code implementation • 18 Jul 2022 • Gensheng Pei, Fumin Shen, Yazhou Yao, Guo-Sen Xie, Zhenmin Tang, Jinhui Tang
Optical flow is an easily conceived and precious cue for advancing unsupervised video object segmentation (UVOS).
1 code implementation • 23 Jan 2021 • Huafeng Liu, Chuanyi Zhang, Yazhou Yao, Xiushen Wei, Fumin Shen, Jian Zhang, Zhenmin Tang
Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators.
1 code implementation • 22 Feb 2021 • Tao Chen, GuoSen Xie, Yazhou Yao, Qiong Wang, Fumin Shen, Zhenmin Tang, Jian Zhang
Then we utilize the fused prototype to guide the final segmentation of the query image.
no code implementations • 16 May 2018 • Guodong Ding, Shanshan Zhang, Salman Khan, Zhenmin Tang, Jian Zhang, Fatih Porikli
Our approach measures the affinity of unlabeled samples with the underlying clusters of labeled data samples using the intermediate feature representations from deep networks.
no code implementations • 20 Nov 2017 • Guodong Ding, Salman Khan, Zhenmin Tang, Fatih Porikli
Person re-identification aims at establishing the identity of a pedestrian from a gallery that contains images of multiple people obtained from a multi-camera system.
no code implementations • 22 Nov 2016 • Yazhou Yao, Jian Zhang, Fumin Shen, Xian-Sheng Hua, Jingsong Xu, Zhenmin Tang
To reduce the cost of manual labelling, there has been increased research interest in automatically constructing image datasets by exploiting web images.
no code implementations • 16 Mar 2017 • Yazhou Yao, Jian Zhang, Fumin Shen, Xian-Sheng Hua, Wankou Yang, Zhenmin Tang
To tackle these problems, in this work, we exploit general corpus information to automatically select and subsequently classify web images into semantic rich (sub-)categories.
no code implementations • 2 Dec 2014 • Fumin Shen, Chunhua Shen, Qinfeng Shi, Anton Van Den Hengel, Zhenmin Tang, Heng Tao Shen
In addition, a supervised inductive manifold hashing framework is developed by incorporating the label information, which is shown to greatly advance the semantic retrieval performance.
no code implementations • 4 Apr 2013 • Fumin Shen, Chunhua Shen, Rhys Hill, Anton Van Den Hengel, Zhenmin Tang
Minimization of the $L_\infty$ norm, which can be viewed as approximately solving the non-convex least median estimation problem, is a powerful method for outlier removal and hence robust regression.
no code implementations • CVPR 2013 • Fumin Shen, Chunhua Shen, Qinfeng Shi, Anton Van Den Hengel, Zhenmin Tang
We particularly show that hashing on the basis of t-SNE .
no code implementations • 26 May 2019 • Huafeng Liu, Yazhou Yao, Zeren Sun, Xiangrui Li, Ke Jia, Zhenmin Tang
Robust road segmentation is a key challenge in self-driving research.
no code implementations • 7 Jun 2019 • Yazhou Yao, Jian Zhang, Xian-Sheng Hua, Fumin Shen, Zhenmin Tang
Recent successes in visual recognition can be primarily attributed to feature representation, learning algorithms, and the ever-increasing size of labeled training data.
1 code implementation • 6 Aug 2020 • Chuanyi Zhang, Yazhou Yao, Xiangbo Shu, Zechao Li, Zhenmin Tang, Qi Wu
To this end, we propose a data-driven meta-set based approach to deal with noisy web images for fine-grained recognition.
no code implementations • CVPR 2021 • Yazhou Yao, Zeren Sun, Chuanyi Zhang, Fumin Shen, Qi Wu, Jian Zhang, Zhenmin Tang
Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels usually results in inferior model performance.
no code implementations • 13 Jul 2022 • Qingze Yin, GuanAn Wang, Guodong Ding, Qilei Li, Shaogang Gong, Zhenmin Tang
To strike a balance between the model accuracy and efficiency, we propose a novel Sub-space Consistency Regularization (SCR) algorithm that can speed up the ReID procedure by $0. 25$ times than real-value features under the same dimensions whilst maintaining a competitive accuracy, especially under short codes.