VFM4SDG is a dual-prior framework that distills cross-domain stable relations from VFMs into DETR encoders and injects semantic-contextual priors into decoder queries to reduce missed detections in single-domain generalized object detection.
Detrs with collaborative hybrid assign- ments training. arxiv 2022
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Introduces the XAMI benchmark dataset of 1000 annotated XMM-Newton images for artefact detection together with a hybrid CNN-transformer instance segmentation demonstration.
FDDet is a semi-supervised object detection framework with BBoxMixUp and CGPC that outperforms standard detectors on the new FDD-48 food defect dataset under data-limited real-world conditions.
Hausdorff distance-based matching and adaptive query denoising improve Rotated DETR, yielding +4.18 to +4.99 AP50 gains on DOTA-v2.0, DOTA-v1.5, and DIOR-R with ResNet-50.
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XAMI -- A Benchmark Dataset for Artefact Detection in XMM-Newton Optical Images
Introduces the XAMI benchmark dataset of 1000 annotated XMM-Newton images for artefact detection together with a hybrid CNN-transformer instance segmentation demonstration.