Related papers: Regressor-Segmenter Mutual Prompt Learning for Crowd Counting

Regressor-Segmenter Mutual Prompt Learning for Crowd Counting

URL: http://arxiv.org/abs/2312.01711v3
Date: Wed, 3 Jan 2024 09:35:21 GMT
Title: Regressor-Segmenter Mutual Prompt Learning for Crowd Counting
Authors: Mingyue Guo, Li Yuan, Zhaoyi Yan, Binghui Chen, Yaowei Wang, Qixiang Ye
Abstract summary: We propose mutual prompt learning (mPrompt) to solve bias and inaccuracy caused by annotation variance. Experiments show that mPrompt significantly reduces the Mean Average Error (MAE)
Score: 70.49246560246736
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Crowd counting has achieved significant progress by training regressors to predict instance positions. In heavily crowded scenarios, however, regressors are challenged by uncontrollable annotation variance, which causes density map bias and context information inaccuracy. In this study, we propose mutual prompt learning (mPrompt), which leverages a regressor and a segmenter as guidance for each other, solving bias and inaccuracy caused by annotation variance while distinguishing foreground from background. In specific, mPrompt leverages point annotations to tune the segmenter and predict pseudo head masks in a way of point prompt learning. It then uses the predicted segmentation masks, which serve as spatial constraint, to rectify biased point annotations as context prompt learning. mPrompt defines a way of mutual information maximization from prompt learning, mitigating the impact of annotation variance while improving model accuracy. Experiments show that mPrompt significantly reduces the Mean Average Error (MAE), demonstrating the potential to be general framework for down-stream vision tasks.

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