Crowd Counting On Worldexpo10

Métriques

Average MAE

Résultats

Résultats de performance de divers modèles sur ce benchmark

		Paper Title
Zhang et al.	12.9	Cross-Scene Crowd Counting via Deep Convolutional Neural Networks
MCNN	11.6	Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
IG-CNN	11.3	Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN
ic-CNN	10.3	Iterative Crowd Counting
Switch-CNN	9.4	Switching Convolutional Neural Network for Crowd Counting
DecideNet	9.23	DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation
D-ConvNet	9.1	Crowd Counting With Deep Negative Correlation Learning
CP-CNN	8.9	Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs
CSRNet	8.6	CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
SANet	8.2	Scale Aggregation Network for Accurate and Efficient Crowd Counting
SPANet	7.7	Learning Spatial Awareness to Improve Crowd Counting
ACSCP	7.5	Crowd Counting via Adversarial Cross-Scale Consistency Pursuit
CAN	7.4	Context-Aware Crowd Counting
M-SFANet	7.32	Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting
ECAN	7.2	Context-Aware Crowd Counting

0 of 15 row(s) selected.

Crowd Counting On Worldexpo10 | SOTA | HyperAI