A Simple-but-effective Baseline for Training-free Class-Agnostic Counting

arXiv:2403.01418v1 Announce Type: new
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 …

abstract arxiv cac class count cs.cv examples foundation free image objects reference segment segment anything simple training type