Papers › FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding
FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding
Bo Sun, Banghuai Li, Shengcai Cai, Ye Yuan, Chi Zhang
Emerging interests have been brought to recognize previously unseen objects given very few training examples, known as few-shot object detection (FSOD). Recent researches demonstrate that good feature embedding is the key to reach favorable few-shot learning performance. We observe object proposals with different Intersection-of-Union (IoU) scores are analogous to the intra-image augmentation used in contrastive approaches. And we exploit this analogy and incorporate supervised contrastive learning to achieve more robust objects representations in FSOD. We present Few-Shot object detection via Contrastive proposals Encoding (FSCE), a simple yet effective approach to learning contrastive-aware object proposal encodings that facilitate the classification of detected objects. We notice the degradation of average precision (AP) for rare objects mainly comes from misclassifying novel instances as confusable classes. And we ease the misclassification issues by promoting instance level intra-class compactness and inter-class variance via our contrastive proposal encoding loss (CPE loss). Our design outperforms current state-of-the-art works in any shot and all data splits, with up to +8.8% on standard benchmark PASCAL VOC and +2.7% on challenging COCO benchmark. Code is available at: https: //github.com/MegviiDetection/FSCE
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Cross-Domain Few-Shot Object Detection | Artaxor | FSCE | mAP | 15.9 | #10 of 16 | Archive leaderboard | report |
| Cross-Domain Few-Shot Object Detection | DIOR | FSCE | mAP | 21.9 | #8 of 15 | Archive leaderboard | report |
| Cross-Domain Few-Shot Object Detection | UODD | FSCE | mAP | 12.0 | #10 of 16 | Archive leaderboard | report |
| Few-Shot Object Detection | MS-COCO (10-shot) | FSCE | AP | 11.1 | #25 of 33 | Archive leaderboard | report |
| Few-Shot Object Detection | MS-COCO (30-shot) | FSCE | AP | 15.3 | #16 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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