Search Results for author:
Found 8 papers, 1 papers with code
Crowd Counting in the Frequency Domain
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.
Progressive Unsupervised Learning for Visual Object Tracking
In this paper, we propose a progressive unsupervised learning (PUL) framework, which entirely removes the need for annotated training videos in visual tracking.
A Generalized Loss Function for Crowd Counting and Localization
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.
Weight Rescaling: Effective and Robust Regularization for Deep Neural Networks with Batch Normalization
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.
Modeling Noisy Annotations for Crowd Counting
The annotation noise in crowd counting is not modeled in traditional crowd counting algorithms based on crowd density maps.
Fine-Grained Crowd Counting
Second, we propose a complementary attention model to share information between the two branches.
Adaptive Density Map Generation for Crowd Counting
In particular, the density map could be considered as an intermediate representation used to train a crowd counting network.
Residual Regression With Semantic Prior for Crowd Counting
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.
