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Semi-Supervised Crowd Counting with Contextual Modeling: Facilitating Holistic Understanding of Crowd Scenes

The list of images in the validation set for UCF-QNRF is the same as that in BL. Please set the learning rate to 2e-5, weight decay to 4e-5 when training on QNRF.

Some more details will be provided later

Results

Pretrained Model

The pretrained models could be downloaded from Google Drive or OneDrive. Please test the pre-trained model on the images pre-processed with our code. We just realize the results would siginificantly change if the quality of the images changes for QNRF.

Eniviroment

timm==0.5.4<br /> python < 3.10<br /> pytorch >=1.4<br /> opencv-python<br /> scipy==1.6.2<br /> h5py <br /> pillow<br /> tqdm<br />

If you find our work useful, please cite:

@ARTICLE{Semi2024Qian,
  author={Qian, Yifei and Hong, Xiaopeng and Guo, Zhongliang and Arandjelović, Ognjen and Donovan, Carl R.},
  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
  title={Semi-Supervised Crowd Counting With Contextual Modeling: Facilitating Holistic Understanding of Crowd Scenes}, 
  year={2024},
  volume={34},
  number={9},
  pages={8230-8241},
  doi={10.1109/TCSVT.2024.3392500}}