Papers › Learning To Count Everything
Learning To Count Everything
Viresh Ranjan, Udbhav Sharma, Thu Nguyen, Minh Hoai
Existing works on visual counting primarily focus on one specific category at a time, such as people, animals, and cells. In this paper, we are interested in counting everything, that is to count objects from any category given only a few annotated instances from that category. To this end, we pose counting as a few-shot regression task. To tackle this task, we present a novel method that takes a query image together with a few exemplar objects from the query image and predicts a density map for the presence of all objects of interest in the query image. We also present a novel adaptation strategy to adapt our network to any novel visual category at test time, using only a few exemplar objects from the novel category. We also introduce a dataset of 147 object categories containing over 6000 images that are suitable for the few-shot counting task. The images are annotated with two types of annotation, dots and bounding boxes, and they can be used for developing few-shot counting models. Experiments on this dataset shows that our method outperforms several state-of-the-art object detectors and few-shot counting approaches. Our code and dataset can be found at https://github.com/cvlab-stonybrook/LearningToCountEverything.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Exemplar-Free Counting | FSC147 | FamNet | MAE(test) | 32.27 | #9 of 9 | Archive leaderboard | report |
| Exemplar-Free Counting | FSC147 | FamNet | MAE(val) | 32.15 | #9 of 9 | Archive leaderboard | report |
| Exemplar-Free Counting | FSC147 | FamNet | RMSE(test) | 131.46 | #9 of 9 | Archive leaderboard | report |
| Exemplar-Free Counting | FSC147 | FamNet | RMSE(val) | 98.75 | #9 of 9 | Archive leaderboard | report |
| Object Counting | FSC147 | FamNet | MAE(test) | 22.08 | #19 of 19 | Archive leaderboard | report |
| Object Counting | FSC147 | FamNet | MAE(val) | 23.75 | #19 of 19 | Archive leaderboard | report |
| Object Counting | FSC147 | FamNet | RMSE(test) | 99.54 | #19 of 19 | Archive leaderboard | report |
| Object Counting | FSC147 | FamNet | RMSE(val) | 69.07 | #19 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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