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

• URL: http://arxiv.org/abs/2403.01418v1
• Date: Sun, 3 Mar 2024 07:19:50 GMT
• Title: A Simple-but-effective Baseline for Training-free Class-Agnostic Counting
• Authors: Yuhao Lin, Haiming Xu, Lingqiao Liu, Javen Qinfeng Shi
• Abstract summary: Class-Agnostic Counting (CAC) seeks to accurately count objects in a given image with only a few reference examples. Recent efforts have shown that it's possible to accomplish this without training by utilizing pre-existing foundation models. We present a training-free solution that effectively bridges this performance gap, serving as a strong baseline.
• Score: 30.792198686654075
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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