</> Code with Papers by Syntology

Papers › Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for...

Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object Detection

6 Apr 2025arXiv:2504.04517archive 2025-07-28

Jiancheng Pan, Yanxing Liu, Xiao He, Long Peng, Jiahao Li, Yuze Sun, Xiaomeng Huang

Foundation models pretrained on extensive datasets, such as GroundingDINO and LAE-DINO, have performed remarkably in the cross-domain few-shot object detection (CD-FSOD) task. Through rigorous few-shot training, we found that the integration of image-based data augmentation techniques and grid-based sub-domain search strategy significantly enhances the performance of these foundation models. Building upon GroundingDINO, we employed several widely used image augmentation methods and established optimization objectives to effectively navigate the expansive domain space in search of optimal sub-domains. This approach facilitates efficient few-shot object detection and introduces an approach to solving the CD-FSOD problem by efficiently searching for the optimal parameter configuration from the foundation model. Our findings substantially advance the practical deployment of vision-language models in data-scarce environments, offering critical insights into optimizing their cross-domain generalization capabilities without labor-intensive retraining. Code is available at https://github.com/jaychempan/ETS.

PaperPDFCode

Code

jaychempan/ETS officialmentioned in papermentioned on GitHubpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Cross-Domain Few-ShotCross-Domain Few-Shot Object DetectionData AugmentationDomain GeneralizationFew-Shot Object DetectionImage AugmentationNavigateObject Detectionobject-detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Cross-Domain Few-Shot Object Detection Artaxor ETS mAP 71.2 #1 of 16 Archive leaderboard report
Cross-Domain Few-Shot Object Detection Clipark1k ETS mAP 61.5 #1 of 10 Archive leaderboard report
Cross-Domain Few-Shot Object Detection DIOR ETS mAP 37.5 #1 of 15 Archive leaderboard report
Cross-Domain Few-Shot Object Detection DeepFish ETS mAP 44.1 #1 of 10 Archive leaderboard report
Cross-Domain Few-Shot Object Detection NEU-DET ETS mAP 26.1 #1 of 10 Archive leaderboard report
Cross-Domain Few-Shot Object Detection UODD ETS mAP 29.8 #1 of 16 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