Benchmarking Cross-Domain Few-Shot Object Detection in Aerial Imagery
Bibliographic record
Abstract
This paper addresses the challenges of Cross-Domain Few-Shot Object Detection (CD-FSOD), particularly in the context of aerial imagery. CD-FSOD aims to transfer knowledge from data-rich source domains to target domains with sparse labeled data, while confronting the difficulty of adapting to new classes that may differ significantly. To overcome these obstacles, we explore a variety of fine-tuning strategies for object detection models trained on large source domains, with the goal of improving their performance in target domains with few annotations. Additionally, we provide a comprehensive comparison of state-of-the-art object detection models across several cross-domain scenarios. To further facilitate this research, we introduce a novel tool that automates training and testing across diverse configurations, enhancing both reproducibility and efficiency in CD-FSOD studies. Finally, we underscore the importance of selecting appropriate source datasets and fine-tuning strategies to optimize performance in real-world applications. Our code is available here: https://github.com/HichTala/Benchmark_CD-FS-OD_using_HF
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".