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 • 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 • 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 • 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.
no code implementations • 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.