Joint CNN and Transformer Network via weakly supervised Learning for efficient crowd counting

Understand

Currently, for crowd counting, the fully supervised methods via density map estimation are the mainstream research directions.

• However, such methods need location-level annotation of persons in an image, which is time-consuming and laborious.
• Therefore, the weakly supervised method just relying upon the count-level annotation is urgently needed.
• Since CNN is not suitable for modeling the global context and the interactions between image patches, crowd counting with weakly supervised learning via CNN generally can not show good performance.