Papers › Learning Spatial Similarity Distribution for Few-shot Object Counting
Learning Spatial Similarity Distribution for Few-shot Object Counting
Yuanwu Xu, Feifan Song, Haofeng Zhang
Few-shot object counting aims to count the number of objects in a query image that belong to the same class as the given exemplar images. Existing methods compute the similarity between the query image and exemplars in the 2D spatial domain and perform regression to obtain the counting number. However, these methods overlook the rich information about the spatial distribution of similarity on the exemplar images, leading to significant impact on matching accuracy. To address this issue, we propose a network learning Spatial Similarity Distribution (SSD) for few-shot object counting, which preserves the spatial structure of exemplar features and calculates a 4D similarity pyramid point-to-point between the query features and exemplar features, capturing the complete distribution information for each point in the 4D similarity space. We propose a Similarity Learning Module (SLM) which applies the efficient center-pivot 4D convolutions on the similarity pyramid to map different similarity distributions to distinct predicted density values, thereby obtaining accurate count. Furthermore, we also introduce a Feature Cross Enhancement (FCE) module that enhances query and exemplar features mutually to improve the accuracy of feature matching. Our approach outperforms state-of-the-art methods on multiple datasets, including FSC-147 and CARPK. Code is available at https://github.com/CBalance/SSD.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Object Counting | FSC147 | SSD | MAE(test) | 9.58 | #5 of 19 | Archive leaderboard | report |
| Object Counting | FSC147 | SSD | MAE(val) | 9.73 | #5 of 19 | Archive leaderboard | report |
| Object Counting | FSC147 | SSD | RMSE(test) | 64.13 | #5 of 19 | Archive leaderboard | report |
| Object Counting | FSC147 | SSD | RMSE(val) | 29.72 | #5 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.
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