2 code implementations • CVPR 2019 • Zhun Zhong, Liang Zheng, Zhiming Luo, Shaozi Li, Yi Yang
To achieve this goal, an exemplar memory is introduced to store features of the target domain and accommodate the three invariance properties.
Domain Adaptive Person Re-Identification Person Re-Identification +1
2 code implementations • 7 Mar 2023 • Juanjuan Weng, Zhiming Luo, Zhun Zhong, Shaozi Li, Nicu Sebe
In this work, we provide a comprehensive investigation of the CE loss function and find that the logit margin between the targeted and untargeted classes will quickly obtain saturation in CE, which largely limits the transferability.
1 code implementation • 3 Dec 2019 • Fengxiang Yang, Ke Li, Zhun Zhong, Zhiming Luo, Xing Sun, Hao Cheng, Xiaowei Guo, Feiyue Huang, Rongrong Ji, Shaozi Li
This procedure encourages that the selected training samples can be both clean and miscellaneous, and that the two models can promote each other iteratively.
Ranked #9 on Unsupervised Domain Adaptation on Market to Duke
1 code implementation • CVPR 2021 • Zhun Zhong, Enrico Fini, Subhankar Roy, Zhiming Luo, Elisa Ricci, Nicu Sebe
In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes.
1 code implementation • CVPR 2021 • Yuyang Zhao, Zhun Zhong, Fengxiang Yang, Zhiming Luo, Yaojin Lin, Shaozi Li, Nicu Sebe
In this paper, we study the problem of multi-source domain generalization in ReID, which aims to learn a model that can perform well on unseen domains with only several labeled source domains.
2 code implementations • CVPR 2017 • Zhiming Luo, Akshaya Mishra, Andrew Achkar, Justin Eichel, Shaozi Li, Pierre-Marc Jodoin
Saliency detection aims to highlight the most relevant objects in an image.
Ranked #2 on RGB Salient Object Detection on UCF
1 code implementation • CVPR 2021 • Fengxiang Yang, Zhun Zhong, Zhiming Luo, Yuanzheng Cai, Yaojin Lin, Shaozi Li, Nicu Sebe
This paper considers the problem of unsupervised person re-identification (re-ID), which aims to learn discriminative models with unlabeled data.
1 code implementation • 29 Jun 2023 • Yingxin Lai, Zhiming Luo, Zitong Yu
The rapid advancements in computer vision have stimulated remarkable progress in face forgery techniques, capturing the dedicated attention of researchers committed to detecting forgeries and precisely localizing manipulated areas.
1 code implementation • 1 Aug 2023 • Fan Sun, Zhiming Luo, Shaozi Li
However, current losses for medical image segmentation mainly focus on overall segmentation results, with fewer losses proposed to guide boundary segmentation.
1 code implementation • IEEE Transactions on Image Processing (TIP) 2022 • Jinliang Lin, Zhedong Zheng, Zhun Zhong, Zhiming Luo, Shaozi Li, Yi Yang, Nicu Sebe
Inspired by the human visual system for mining local patterns, we propose a new framework called RK-Net to jointly learn the discriminative Representation and detect salient Keypoints with a single Network.
Ranked #2 on Drone navigation on University-1652
1 code implementation • 7 Jun 2021 • Yuyang Zhao, Zhun Zhong, Zhiming Luo, Gim Hee Lee, Nicu Sebe
Second, CPSS can reduce the influence of noisy pseudo-labels and also avoid the model overfitting to the target domain during self-supervised learning, consistently boosting the performance on the target and open domains.
1 code implementation • 5 Nov 2018 • Fengxiang Yang, Zhun Zhong, Zhiming Luo, Sheng Lian, Shaozi Li
For training of deep re-ID model, we divide it into three steps: 1) pre-training a coarse re-ID model by using virtual data; 2) collaborative filtering based positive pair mining from the real data; and 3) fine-tuning of the coarse re-ID model by leveraging the mined positive pairs and virtual data.
1 code implementation • 11 Sep 2023 • Haoke Xiao, Lv Tang, Bo Li, Zhiming Luo, Shaozi Li
Despite recent advancements in deep learning models, these models still rely on training with well-annotated CoSOD datasets.
1 code implementation • 2 May 2023 • Juanjuan Weng, Zhiming Luo, Dazhen Lin, Shaozi Li, Zhun Zhong
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-box models.
1 code implementation • 7 Dec 2023 • Guoqing Yang, Zhiming Luo, Jianzhe Gao, Yingxin Lai, Kun Yang, Yifan He, Shaozi Li
Human behavior anomaly detection aims to identify unusual human actions, playing a crucial role in intelligent surveillance and other areas.
no code implementations • 24 May 2017 • Clement Zotti, Zhiming Luo, Alain Lalande, Olivier Humbert, Pierre-Marc Jodoin
In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge.
no code implementations • 1 Aug 2019 • Zhun Zhong, Liang Zheng, Zhiming Luo, Shaozi Li, Yi Yang
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.
Ranked #7 on Unsupervised Domain Adaptation on Market to MSMT
no code implementations • CVPR 2021 • Zhun Zhong, Linchao Zhu, Zhiming Luo, Shaozi Li, Yi Yang, Nicu Sebe
In this paper, we tackle the problem of discovering new classes in unlabeled visual data given labeled data from disjoint classes.
no code implementations • 3 Mar 2022 • Yongguo Ling, Zhun Zhong, Donglin Cao, Zhiming Luo, Yaojin Lin, Shaozi Li, Nicu Sebe
In this manner, the model will focus on reducing the inter-modality discrepancy while paying less attention to intra-identity variations, leading to a more effective modality alignment.
no code implementations • 5 Mar 2022 • Fengxiang Yang, Zhun Zhong, Zhiming Luo, Shaozi Li, Nicu Sebe
During local training, the DFS are used to synthesize novel domain statistics with the proposed domain hallucinating, which is achieved by re-weighting DFS with random weights.
no code implementations • 20 Jun 2023 • Juanjuan Weng, Zhiming Luo, Dazhen Lin, Shaozi Li
Furthermore, we conduct a comprehensive evaluation of different loss functions within consistent training frameworks, including noise-based and generator-based.
no code implementations • 18 Jan 2024 • Yunpeng Gong, Zhun Zhong, Zhiming Luo, Yansong Qu, Rongrong Ji, Min Jiang
For instance, infrared images are typically grayscale, unlike visible images that contain color information.
no code implementations • 19 Mar 2024 • Yingxin Lai, Guoqing Yang Yifan He, Zhiming Luo, Shaozi Li
To solve this problem, we proposed a novel framework Selective Domain-Invariant Feature (SDIF), which reduces the sensitivity to face forgery by fusing content features and styles.