  Existing class-agnostic counting models typically rely on a single type of
prompt, e.g., box annotations. This paper aims to establish a comprehensive
prompt-based counting framework capable of generating density maps for
concerned objects indicated by various prompt types, such as box, point, and
text. To achieve this goal, we begin by converting prompts from different
modalities into prompt masks without requiring training. These masks are then
integrated into a class-agnostic counting methodology for predicting density
maps. Furthermore, we introduce a fixed-point inference along with an
associated loss function to improve counting accuracy, all without introducing
new parameters. The effectiveness of this method is substantiated both
theoretically and experimentally. Additionally, a contrastive training scheme
is implemented to mitigate dataset bias inherent in current class-agnostic
counting datasets, a strategy whose effectiveness is confirmed by our ablation
study. Our model excels in prominent class-agnostic datasets and exhibits
superior performance in cross-dataset adaptation tasks.
