A Simple-but-effective Baseline for Training-free Class-Agnostic Counting
<|reference_start|>A Simple-but-effective Baseline for Training-free Class-Agnostic Counting: 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.<|reference_end|>
@article{lin2024a,
    title={A Simple-but-effective Baseline for Training-free Class-Agnostic
  Counting},
    author={Yuhao Lin, Haiming Xu, Lingqiao Liu, Javen Qinfeng Shi},
    journal={arXiv preprint arXiv:2403.01418},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01418},
    primaryClass={cs.CV}
}