  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.
