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Found 6 papers, 3 papers with code
Completely Self-Supervised Crowd Counting via Distribution Matching
Dense crowd counting is a challenging task that demands millions of head annotations for training models.
Crafting Data-free Universal Adversaries with Dilate Loss
We introduce a method to create Universal Adversarial Perturbations (UAP) for a given CNN in a data-free manner.
Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection
We introduce a detection framework for dense crowd counting and eliminate the need for the prevalent density regression paradigm.
Ranked #7 on
Crowd Counting
on UCF CC 50
Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN
Our model starts from a base CNN density regressor, which is trained in equivalence on all types of crowd images.
Ranked #13 on
Crowd Counting
on WorldExpo’10
Top-Down Feedback for Crowd Counting Convolutional Neural Network
But the current state-of-the-art CNN regressors for crowd counting are feedforward and use only limited spatial context to detect people.
Switching Convolutional Neural Network for Crowd Counting
It is observed that the switch relays an image patch to a particular CNN column based on density of crowd.
Ranked #4 on
Crowd Counting
on Venice
