Papers › Balanced ID-OOD tradeoff transfer makes query based detectors good few shot learners
Balanced ID-OOD tradeoff transfer makes query based detectors good few shot learners
Yuantao Yin, Ping Yin, Xue Xiao, Liang Yan, Siqing Sun, Xiaobo An
Fine-tuning is a popular approach to solve the few-shot object detection problem. In this paper, we attempt to introduce a new perspective on it. We formulate the few-shot novel tasks as a type of distribution shifted from its ground-truth distribution. We introduce the concept of imaginary placeholder masks to show that this distribution shift is essentially a composite of in-distribution(ID) and out-of-distribution(OOD) shifts. Our empirical investigation results show that it is significant to balance the trade-off between adapting to the available few-shot distribution and keeping the distribution-shift robustness of the pre-trained model. We explore improvements in the few-shot fine-tuning transfer in the few-shot object detection(FSOD) settings from three aspects. First, we explore the LinearProbe-Finetuning(LP-FT) technique to balance this trade-off to mitigate the feature distortion problem. Second, we explore the effectiveness of utilizing the protection freezing strategy for query-based object detectors to keep their OOD robustness. Third, we try to utilize ensembling methods to circumvent the feature distortion. All these techniques are integrated into a whole method called BIOT(Balanced ID-OOD Transfer). Evaluation results show that our method is simple yet effective and general to tap the FSOD potential of query-based object detectors. It outperforms the current SOTA method in many FSOD settings and has a promising scaling capability.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
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 | BIOT(10-Shot) | mAP | 58.4 | #4 of 16 | Archive leaderboard | report |
| Cross-Domain Few-Shot Object Detection | Artaxor | BIOT(5-Shot) | mAP | 53.3 | #5 of 16 | Archive leaderboard | report |
| Cross-Domain Few-Shot Object Detection | DIOR | BIOT | mAP | 31.1 | #3 of 15 | Archive leaderboard | report |
| Cross-Domain Few-Shot Object Detection | UODD | BIOT(10-shot) | mAP | 20.4 | #3 of 16 | Archive leaderboard | report |
| Cross-Domain Few-Shot Object Detection | UODD | BIOT(5-shot) | mAP | 18.0 | #4 of 16 | Archive leaderboard | report |
| Few-Shot Object Detection | MS-COCO (10-shot) | BIOT | AP | 26.3 | #4 of 33 | Archive leaderboard | report |
| Few-Shot Object Detection | MS-COCO (30-shot) | BIOT | AP | 33.8 | #4 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.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections