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Crowd Counting On Ucf Qnrf
Crowd Counting On Ucf Qnrf
평가 지표
MAE
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
MAE
Paper Title
Repository
Cascaded-MTL
252
CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting
-
Resnet101
190
Deep Residual Learning for Image Recognition
-
CAN
107
Context-Aware Crowd Counting
-
Switch-CNN
228
Switching Convolutional Neural Network for Crowd Counting
-
CLIP-EBC (ResNet50)
80.5
CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
-
GauNet (ResNet-50)
81.6
Rethinking Spatial Invariance of Convolutional Networks for Object Counting
-
Idrees et al.
132
Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds
-
M-SFANet
85.6
Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting
-
APGCC
80.1
Improving Point-based Crowd Counting and Localization Based on Auxiliary Point Guidance
-
Encoder-Decoder
270
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
-
CSRNet-EBC
79.3
CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
-
SGANet + CL
87.6
Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss
-
PSL-Net
85.5
Crowd Counting and Individual Localization Using Pseudo Square Label
-
DMCount-EBC (32, dynamic)
76.06
CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
-
Densenet201
163
Densely Connected Convolutional Networks
-
SGANet
89.1
Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss
-
Idrees et al.
315
Multi-source Multi-scale Counting in Extremely Dense Crowd Images
-
MCNN
277
Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
DMCount-EBC
77.2
CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
-
DMCount-EBC (16, dynamic)
75.90
CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
-
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