A Simple-but-effective Baseline for Training-free Class-Agnostic
  Counting
Yuhao Lin Haiming Xu Lingqiao Liu Javen Qinfeng Shi
Class-Agnostic Counting (CAC) seeks to accurately count objects in a given
image with only a few reference examples. While previous methods achieving this
relied on additional training, recent efforts have shown that it's possible to
accomplish this without training by utilizing pre-existing foundation models,
particularly the Segment Anything Model (SAM), for counting via instance-level
segmentation. Although promising, current training-free methods still lag
behind their training-based counterparts in terms of performance. In this
research, we present a straightforward training-free solution that effectively
bridges this performance gap, serving as a strong baseline. The primary
contribution of our work lies in the discovery of four key technologies that
can enhance performance. Specifically, we suggest employing a superpixel
algorithm to generate more precise initial point prompts, utilizing an image
encoder with richer semantic knowledge to replace the SAM encoder for
representing candidate objects, and adopting a multiscale mechanism and a
transductive prototype scheme to update the representation of reference
examples. By combining these four technologies, our approach achieves
significant improvements over existing training-free methods and delivers
performance on par with training-based ones.