Search Results for author: Jia Wan

Found 8 papers, 1 papers with code

Crowd Counting in the Frequency Domain

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.

Crowd Counting

Progressive Unsupervised Learning for Visual Object Tracking

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.

Contrastive Learning Visual Object Tracking +1

A Generalized Loss Function for Crowd Counting and Localization

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.

Crowd Counting

Weight Rescaling: Effective and Robust Regularization for Deep Neural Networks with Batch Normalization

no code implementations6 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.

Crowd Counting Image Classification +3

Modeling Noisy Annotations for Crowd Counting

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.

Crowd Counting

Fine-Grained Crowd Counting

no code implementations13 Jul 2020 Jia Wan, Nikil Senthil Kumar, Antoni B. Chan

Second, we propose a complementary attention model to share information between the two branches.

Crowd Counting Management +1

Adaptive Density Map Generation for Crowd Counting

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.

Crowd Counting

Residual Regression With Semantic Prior for Crowd Counting

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.

Crowd Counting regression

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