Papers › Re-Scoring Using Image-Language Similarity for Few-Shot Object Detection
Re-Scoring Using Image-Language Similarity for Few-Shot Object Detection
Min Jae Jung, Seung Dae Han, Joohee Kim
Few-shot object detection, which focuses on detecting novel objects with few labels, is an emerging challenge in the community. Recent studies show that adapting a pre-trained model or modified loss function can improve performance. In this paper, we explore leveraging the power of Contrastive Language-Image Pre-training (CLIP) and hard negative classification loss in low data setting. Specifically, we propose Re-scoring using Image-language Similarity for Few-shot object detection (RISF) which extends Faster R-CNN by introducing Calibration Module using CLIP (CM-CLIP) and Background Negative Re-scale Loss (BNRL). The former adapts CLIP, which performs zero-shot classification, to re-score the classification scores of a detector using image-class similarities, the latter is modified classification loss considering the punishment for fake backgrounds as well as confusing categories on a generalized few-shot object detection dataset. Extensive experiments on MS-COCO and PASCAL VOC show that the proposed RISF substantially outperforms the state-of-the-art approaches. The code will be available.
Code
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Tasks
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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) | RISF | AP | 11.7 | #4 of 7 | Archive leaderboard | report |
| Few-Shot Object Detection | MS-COCO (10-shot) | RISF (SWIN-Large) | AP | 25.5 | #5 of 33 | Archive leaderboard | report |
| Few-Shot Object Detection | MS-COCO (10-shot) | RISF (Resnet-101) | AP | 21.9 | #9 of 33 | Archive leaderboard | report |
| Few-Shot Object Detection | MS-COCO (30-shot) | RISF (SWIN-Large) | AP | 31.9 | #5 of 25 | Archive leaderboard | report |
| Few-Shot Object Detection | MS-COCO (30-shot) | RISF (Resnet-101) | AP | 24.4 | #9 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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