Papers › GCA-SUNet: A Gated Context-Aware Swin-UNet for Exemplar-Free Counting
GCA-SUNet: A Gated Context-Aware Swin-UNet for Exemplar-Free Counting
Yuzhe Wu, Yipeng Xu, Tianyu Xu, Jialu Zhang, Jianfeng Ren, Xudong Jiang
Exemplar-Free Counting aims to count objects of interest without intensive annotations of objects or exemplars. To achieve this, we propose a Gated Context-Aware Swin-UNet (GCA-SUNet) to directly map an input image to the density map of countable objects. Specifically, a set of Swin transformers form an encoder to derive a robust feature representation, and a Gated Context-Aware Modulation block is designed to suppress irrelevant objects or background through a gate mechanism and exploit the attentive support of objects of interest through a self-similarity matrix. The gate strategy is also incorporated into the bottleneck network and the decoder of the Swin-UNet to highlight the features most relevant to objects of interest. By explicitly exploiting the attentive support among countable objects and eliminating irrelevant features through the gate mechanisms, the proposed GCA-SUNet focuses on and counts objects of interest without relying on predefined categories or exemplars. Experimental results on the real-world datasets such as FSC-147 and CARPK demonstrate that GCA-SUNet significantly and consistently outperforms state-of-the-art methods. The code is available at https://github.com/Amordia/GCA-SUNet.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Exemplar-Free Counting | FSC147 | GCA-SUN | MAE(test) | 14.00 | #3 of 9 | Archive leaderboard | report |
| Exemplar-Free Counting | FSC147 | GCA-SUN | MAE(val) | 16.06 | #3 of 9 | Archive leaderboard | report |
| Exemplar-Free Counting | FSC147 | GCA-SUN | RMSE(test) | 92.19 | #3 of 9 | Archive leaderboard | report |
| Exemplar-Free Counting | FSC147 | GCA-SUN | RMSE(val) | 53.04 | #3 of 9 | Archive leaderboard | report |
| Object Counting | FSC147 | GCA-SUN | MAE(test) | 14.00 | #11 of 19 | Archive leaderboard | report |
| Object Counting | FSC147 | GCA-SUN | MAE(val) | 16.06 | #11 of 19 | Archive leaderboard | report |
| Object Counting | FSC147 | GCA-SUN | RMSE(test) | 92.19 | #11 of 19 | Archive leaderboard | report |
| Object Counting | FSC147 | GCA-SUN | RMSE(val) | 53.04 | #11 of 19 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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