Comparison with the state-of-the-art approaches on the FSC147 dataset under the few-shot counting scenario. The latency unit is milliseconds (ms).

Comparison with the state-of-the-art approaches on the FSC147 dataset under the few-shot counting scenario. The latency unit is milliseconds (ms).

<p>Comparison with the state-of-the-art approaches on the FSC147 dataset under the few-shot counting scenario. The latency unit is milliseconds (ms).</p>

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Bibliographic Details
Main Author:	Qinghua Yang (606019) (author)
Other Authors:	Bin Liu (5899) (author), Yan Tian (298301) (author), Yangming Shi (21499520) (author), Xinxin Du (470642) (author), Fangyuan He (6455462) (author), Jikun Guo (21499523) (author)
Published:	2025
Subjects:	Sociology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified similar objects within shanghaitech datasets demonstrate scale fusion modules reducing training costs optimize computational efficiency many practical applications learnable shape embedding equally critical factor enhance feature representation encoder applies self relatively little attention integrate adjacent features agnostic counting framework model &# 8217 general ai model shot object counting scale hybrid encoder shot learning techniques hybrid encoder level features attention exclusively agnostic low shot ). xlink "> rapid adaptation level characteristics generalizability compared extensive experiments existing methods enhancing performance efficient low challenging problem annotated samples annotated data achieves real
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