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Crowd Counting On Ucf Cc 50
Crowd Counting On Ucf Cc 50
Metriken
MAE
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
MAE
Paper Title
Zhang et al.
467.0
Cross-Scene Crowd Counting via Deep Convolutional Neural Networks
Idrees et al.
419.5
Multi-source Multi-scale Counting in Extremely Dense Crowd Images
MCNN
377.6
Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
Liu et al.
337.6
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank
Cascaded-MTL
322.8
CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting
Switch-CNN
318.1
Switching Convolutional Neural Network for Crowd Counting
CP-CNN
295.8
Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs
IG-CNN
291.4
Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN
ACSCP
291.0
Crowd Counting via Adversarial Cross-Scale Consistency Pursuit
D-ConvNet
288.4
Crowd Counting With Deep Negative Correlation Learning
CSRNet
266.1
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
ic-CNN
260.9
Iterative Crowd Counting
SANet
258.4
Scale Aggregation Network for Accurate and Efficient Crowd Counting
SPANet
232.6
Learning Spatial Awareness to Improve Crowd Counting
LSC-CNN
225.6
Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection
SGANet
224.6
Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss
SGANet + CL
221.9
Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss
CAN
212.2
Context-Aware Crowd Counting
DM-Count
211.0
Distribution Matching for Crowd Counting
GauNet (ResNet-50)
186.3
Rethinking Spatial Invariance of Convolutional Networks for Object Counting
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