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Semi-supervised Counting via Pixel-by-pixel Density Distribution Modelling
- Publication Year :
- 2024
-
Abstract
- 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.<br />Comment: This is the technical report of a paper that was submitted to IEEE Transactions and is now under review
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2402.15297
- Document Type :
- Working Paper