748 citations · 1.1k across the 20 of their papers we have counts for
20 papers
Boundary Difference Over Union Loss For Medical Image Segmentation
Fan Sun, Zhiming Luo, Shaozi Li
Medical image segmentation is crucial for clinical diagnosis. However, current losses for medical image segmentation mainly focus on overall segmentation results, with fewer losses…
Comparative Evaluation of Recent Universal Adversarial Perturbations in Image Classification
Juanjuan Weng, Zhiming Luo, Dazhen Lin +1
The vulnerability of Convolutional Neural Networks (CNNs) to adversarial samples has recently garnered significant attention in the machine learning community. Furthermore, recent…
Boosting Adversarial Transferability via Fusing Logits of Top-1 Decomposed Feature
Juanjuan Weng, Zhiming Luo, Dazhen Lin +2
Recent research has shown that Deep Neural Networks (DNNs) are highly vulnerable to adversarial samples, which are highly transferable and can be used to attack other unknown black…
Logit Margin Matters: Improving Transferable Targeted Adversarial Attack by Logit Calibration
Juanjuan Weng, Zhiming Luo, Zhun Zhong +2
Previous works have extensively studied the transferability of adversarial samples in untargeted black-box scenarios. However, it still remains challenging to craft targeted advers…
Federated and Generalized Person Re-identification through Domain and Feature Hallucinating
Fengxiang Yang, Zhun Zhong, Zhiming Luo +2
In this paper, we study the problem of federated domain generalization (FedDG) for person re-identification (re-ID), which aims to learn a generalized model with multiple decentral…
Cross-Modality Earth Mover's Distance for Visible Thermal Person Re-Identification
Yongguo Ling, Zhun Zhong, Donglin Cao +4
Visible thermal person re-identification (VT-ReID) suffers from the inter-modality discrepancy and intra-identity variations. Distribution alignment is a popular solution for VT-Re…
Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification
Fengxiang Yang, Zhun Zhong, Zhiming Luo +4
This paper considers the problem of unsupervised person re-identification (re-ID), which aims to learn discriminative models with unlabeled data. One popular method is to obtain ps…
SFANet: A Spectrum-aware Feature Augmentation Network for Visible-Infrared Person Re-Identification
Haojie Liu, Shun Ma, Daoxun Xia +1
Visible-Infrared person re-identification (VI-ReID) is a challenging matching problem due to large modality varitions between visible and infrared images. Existing approaches usual…
An Online Prediction Approach Based on Incremental Support Vector Machine for Dynamic Multiobjective Optimization
Dejun Xu, Min Jiang, Weizhen Hu +3
Real-world multiobjective optimization problems usually involve conflicting objectives that change over time, which requires the optimization algorithms to quickly track the Pareto…
Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification
Yuyang Zhao, Zhun Zhong, Fengxiang Yang +4
Recent advances in person re-identification (ReID) obtain impressive accuracy in the supervised and unsupervised learning settings. However, most of the existing methods need to tr…
OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in An Open World
Zhun Zhong, Linchao Zhu, Zhiming Luo +3
In this paper, we tackle the problem of discovering new classes in unlabeled visual data given labeled data from disjoint classes. Existing methods typically first pre-train a mode…
Asymmetric Co-Teaching for Unsupervised Cross Domain Person Re-Identification
Fengxiang Yang, Ke Li, Zhun Zhong +7
Person re-identification (re-ID), is a challenging task due to the high variance within identity samples and imaging conditions. Although recent advances in deep learning have achi…
Learning to Adapt Invariance in Memory for Person Re-identification
Zhun Zhong, Liang Zheng, Zhiming Luo +2
This work considers the problem of unsupervised domain adaptation in person re-identification (re-ID), which aims to transfer knowledge from the source domain to the target domain.…
Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification
Zhun Zhong, Liang Zheng, Zhiming Luo +2
This paper considers the domain adaptive person re-identification (re-ID) problem: learning a re-ID model from a labeled source domain and an unlabeled target domain. Conventional…
Leveraging Virtual and Real Person for Unsupervised Person Re-identification
Fengxiang Yang, Zhun Zhong, Zhiming Luo +2
Person re-identification (re-ID) is a challenging problem especially when no labels are available for training. Although recent deep re-ID methods have achieved great improvement,…
Camera Style Adaptation for Person Re-identification
Zhun Zhong, Liang Zheng, Zhedong Zheng +2
Being a cross-camera retrieval task, person re-identification suffers from image style variations caused by different cameras. The art implicitly addresses this problem by learning…
Random Erasing Data Augmentation
Zhun Zhong, Liang Zheng, Guoliang Kang +2
In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN). In training, Random Erasing randomly selects a recta…
Re-ranking Person Re-identification with k-reciprocal Encoding
Zhun Zhong, Liang Zheng, Donglin Cao +1
When considering person re-identification (re-ID) as a retrieval process, re-ranking is a critical step to improve its accuracy. Yet in the re-ID community, limited effort has been…
Re-ranking Object Proposals for Object Detection in Automatic Driving
Zhun Zhong, Mingyi Lei, Shaozi Li +1
Object detection often suffers from a plenty of bootless proposals, selecting high quality proposals remains a great challenge. In this paper, we propose a semantic, class-specific…
Detecting Ground Control Points via Convolutional Neural Network for Stereo Matching
Zhun Zhong, Songzhi Su, Donglin Cao +1
In this paper, we present a novel approach to detect ground control points (GCPs) for stereo matching problem. First of all, we train a convolutional neural network (CNN) on a larg…