Papers › Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning
Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning
Dongwoo Park, Jong-Min Lee
Few-shot object detection (FSOD) aims to classify and detect few images of novel categories. Existing meta-learning methods insufficiently exploit features between support and query images owing to structural limitations. We propose a hierarchical attention network with sequentially large receptive fields to fully exploit the query and support images. In addition, meta-learning does not distinguish the categories well because it determines whether the support and query images match. In other words, metric-based learning for classification is ineffective because it does not work directly. Thus, we propose a contrastive learning method called meta-contrastive learning, which directly helps achieve the purpose of the meta-learning strategy. Finally, we establish a new state-of-the-art network, by realizing significant margins. Our method brings 2.3, 1.0, 1.3, 3.4 and 2.4% AP improvements for 1-30 shots object detection on COCO dataset. Our code is available at: https://github.com/infinity7428/hANMCL
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Object Detection | MS-COCO (1-shot) | hANMCL | AP | 13.4 | #2 of 7 | Archive leaderboard | report |
| Few-Shot Object Detection | MS-COCO (10-shot) | hANMCL | AP | 22.4 | #8 of 33 | Archive leaderboard | report |
| Few-Shot Object Detection | MS-COCO (30-shot) | hANMCL | AP | 25.0 | #8 of 25 | 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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