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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".