Semi-supervised Counting via Pixel-by-pixel Density Distribution Modelling
<|reference_start|>Semi-supervised Counting via Pixel-by-pixel Density Distribution Modelling: This paper focuses on semi-supervised crowd counting, where only a small portion of the training data are labeled. We formulate the pixel-wise density value to regress as a probability distribution, instead of a single deterministic value. On this basis, we propose a semi-supervised crowd-counting model. Firstly, we design a pixel-wise distribution matching loss to measure the differences in the pixel-wise density distributions between the prediction and the ground truth; Secondly, we enhance the transformer decoder by using density tokens to specialize the forwards of decoders w.r.t. different density intervals; Thirdly, we design the interleaving consistency self-supervised learning mechanism to learn from unlabeled data efficiently. Extensive experiments on four datasets are performed to show that our method clearly outperforms the competitors by a large margin under various labeled ratio settings. Code will be released at https://github.com/LoraLinH/Semi-supervised-Counting-via-Pixel-by-pixel-Density-Distribution-Modelling.<|reference_end|>
@article{lin2024semi-supervised,
    title={Semi-supervised Counting via Pixel-by-pixel Density Distribution
  Modelling},
    author={Hui Lin and Zhiheng Ma and Rongrong Ji and Yaowei Wang and Zhou Su and
  Xiaopeng Hong and Deyu Meng},
    journal={arXiv preprint arXiv:2402.15297},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15297},
    primaryClass={cs.CV cs.LG}
}