Semi-supervised Counting via Pixel-by-pixel Density Distribution Modelling

• URL: http://arxiv.org/abs/2402.15297v1
• Date: Fri, 23 Feb 2024 12:48:02 GMT
• Title: Semi-supervised Counting via Pixel-by-pixel Density Distribution Modelling
• Authors: Hui Lin and Zhiheng Ma and Rongrong Ji and Yaowei Wang and Zhou Su and Xiaopeng Hong and Deyu Meng
• Abstract summary: 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. Our method clearly outperforms the competitors by a large margin under various labeled ratio settings.
• Score: 135.66138766927716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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-Modelli ng.

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