no code implementations • 12 Mar 2024 • Haiyang Zheng, Nan Pu, Wenjing Li, Nicu Sebe, Zhun Zhong
In this paper, we study the problem of Generalized Category Discovery (GCD), which aims to cluster unlabeled data from both known and unknown categories using the knowledge of labeled data from known categories.
no code implementations • 23 May 2023 • Nan Pu, Zhun Zhong, Xinyuan Ji, Nicu Sebe
On each client, GCL builds class-level contrastive learning with both local and global GMMs.
1 code implementation • CVPR 2023 • Nan Pu, Zhun Zhong, Nicu Sebe
This leads traditional novel category discovery (NCD) methods to be incapacitated for GCD, due to their assumption of unlabeled data are only from novel categories.
1 code implementation • CVPR 2021 • Nan Pu, Wei Chen, Yu Liu, Erwin M. Bakker, Michael S. Lew
In this work we explore a new and challenging ReID task, namely lifelong person re-identification (LReID), which enables to learn continuously across multiple domains and even generalise on new and unseen domains.
no code implementations • 11 Mar 2021 • Theodoros Georgiou, Sebastian Schmitt, Thomas Bäck, Nan Pu, Wei Chen, Michael Lew
The output of such simulations, in particular the calculated flow fields, are usually very complex and hard to interpret for realistic three-dimensional real-world applications, especially if time-dependent simulations are investigated.
1 code implementation • 6 Aug 2020 • Nan Pu, Wei Chen, Yu Liu, Erwin M. Bakker, Michael S. Lew
To solve the problem, we present a carefully designed dual Gaussian-based variational auto-encoder (DG-VAE), which disentangles an identity-discriminable and an identity-ambiguous cross-modality feature subspace, following a mixture-of-Gaussians (MoG) prior and a standard Gaussian distribution prior, respectively.