  Crowd counting and localization have become increasingly important in
computer vision due to their wide-ranging applications. While point-based
strategies have been widely used in crowd counting methods, they face a
significant challenge, i.e., the lack of an effective learning strategy to
guide the matching process. This deficiency leads to instability in matching
point proposals to target points, adversely affecting overall performance. To
address this issue, we introduce an effective approach to stabilize the
proposal-target matching in point-based methods. We propose Auxiliary Point
Guidance (APG) to provide clear and effective guidance for proposal selection
and optimization, addressing the core issue of matching uncertainty.
Additionally, we develop Implicit Feature Interpolation (IFI) to enable
adaptive feature extraction in diverse crowd scenarios, further enhancing the
model's robustness and accuracy. Extensive experiments demonstrate the
effectiveness of our approach, showing significant improvements in crowd
counting and localization performance, particularly under challenging
conditions. The source codes and trained models will be made publicly
available.
