A Simple-but-Effective Baseline for Training-Free Class-Agnostic Counting

Abstract

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.