no code implementations • 27 Feb 2024 • Jia Wan, Qiangqiang Wu, Wei Lin, Antoni B. Chan
The existing crowd counting models require extensive training data, which is time-consuming to annotate.
no code implementations • 8 Feb 2024 • Kitty Fung, Qizhen Zhang, Chris Lu, Jia Wan, Timon Willi, Jakob Foerster
Providing theoretical guarantees for M-FOS is hard because A) there is little literature on theoretical sample complexity bounds for meta-reinforcement learning B) M-FOS operates in continuous state and action spaces, so theoretical analysis is challenging.
no code implementations • 25 Jan 2024 • Zhen Wang, Yuelei Li, Jia Wan, Nuno Vasconcelos
Our proposed smoothed density map input for ControlNet significantly improves ControlNet's performance in generating crowds in the correct locations.
no code implementations • 25 Jan 2024 • Jia Wan, Wanhua Li, Jason Ken Adhinarta, Atmadeep Banerjee, Evelina Sjostedt, Jingpeng Wu, Jeff Lichtman, Hanspeter Pfister, Donglai Wei
Furthermore, we developed a zero-shot cortical blood vessel segmentation method named TriSAM, which leverages the powerful segmentation model SAM for 3D segmentation.
1 code implementation • CVPR 2022 • Weibo Shu, Jia Wan, Kay Chen Tan, Sam Kwong, Antoni B. Chan
By transforming the density map into the frequency domain and using the nice properties of the characteristic function, we propose a novel method that is simple, effective, and efficient.
no code implementations • CVPR 2021 • Jia Wan, Ziquan Liu, Antoni B. Chan
In this paper, we investigate learning the density map representation through an unbalanced optimal transport problem, and propose a generalized loss function to learn density maps for crowd counting and localization.
no code implementations • CVPR 2021 • Qiangqiang Wu, Jia Wan, Antoni B. Chan
In this paper, we propose a progressive unsupervised learning (PUL) framework, which entirely removes the need for annotated training videos in visual tracking.
no code implementations • 6 Feb 2021 • Ziquan Liu, Yufei Cui, Jia Wan, Yu Mao, Antoni B. Chan
On the one hand, when the non-adaptive learning rate e. g. SGD with momentum is used, the effective learning rate continues to increase even after the initial training stage, which leads to an overfitting effect in many neural architectures.
no code implementations • NeurIPS 2020 • Jia Wan, Antoni Chan
The annotation noise in crowd counting is not modeled in traditional crowd counting algorithms based on crowd density maps.
no code implementations • 13 Jul 2020 • Jia Wan, Nikil Senthil Kumar, Antoni B. Chan
Second, we propose a complementary attention model to share information between the two branches.
no code implementations • ICCV 2019 • Jia Wan, Antoni Chan
In particular, the density map could be considered as an intermediate representation used to train a crowd counting network.
1 code implementation • CVPR 2019 • Jia Wan, Wenhan Luo, Baoyuan Wu, Antoni B. Chan, Wei Liu
We also observe that the adversarial loss can be used to improve the quality of predicted density maps, thus leading to an improvement in crowd counting.