TFCounter:Polishing Gems for Training-Free Object Counting
<|reference_start|>TFCounter:Polishing Gems for Training-Free Object Counting: Object counting is a challenging task with broad application prospects in security surveillance, traffic management, and disease diagnosis. Existing object counting methods face a tri-fold challenge: achieving superior performance, maintaining high generalizability, and minimizing annotation costs. We develop a novel training-free class-agnostic object counter, TFCounter, which is prompt-context-aware via the cascade of the essential elements in large-scale foundation models. This approach employs an iterative counting framework with a dual prompt system to recognize a broader spectrum of objects varying in shape, appearance, and size. Besides, it introduces an innovative context-aware similarity module incorporating background context to enhance accuracy within messy scenes. To demonstrate cross-domain generalizability, we collect a novel counting dataset named BIKE-1000, including exclusive 1000 images of shared bicycles from Meituan. Extensive experiments on FSC-147, CARPK, and BIKE-1000 datasets demonstrate that TFCounter outperforms existing leading training-free methods and exhibits competitive results compared to trained counterparts.<|reference_end|>
@article{ting2024tfcounter:polishing,
    title={TFCounter:Polishing Gems for Training-Free Object Counting},
    author={Pan Ting, Jianfeng Lin, Wenhao Yu, Wenlong Zhang, Xiaoying Chen, Jinlu
  Zhang, Binqiang Huang},
    journal={arXiv preprint arXiv:2405.02301},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02301},
    primaryClass={cs.CV}
}