A million-box 159-class remote-sensing dataset plus a GSD- and hierarchy-aware detector yields ~5 mAP average gains over fully supervised baselines on nine external benchmarks with no target training.
Fssd: feature fusion single shot multibox detector,
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
cs.CV 4representative citing papers
Active learning with randomly initialized models achieves comparable results to traditional candidate-model methods, with low-confidence sampling proving most effective.
Develops a multi-task learning based adversarial training approach to improve robustness of object detectors to adversarial attacks, with experiments on PASCAL-VOC and MS-COCO.
Proposes LCD and three other hybrid uncertainty-diversity sampling methods for active learning that outperform prior approaches by selecting uncertain yet diverse samples.
citing papers explorer
-
LEVIRDet: A Million-Scale 159-Category Dataset and Foundation Model for Universal Remote Sensing Object Detection
A million-box 159-class remote-sensing dataset plus a GSD- and hierarchy-aware detector yields ~5 mAP average gains over fully supervised baselines on nine external benchmarks with no target training.
-
Are Candidate Models Really Needed for Active Learning?
Active learning with randomly initialized models achieves comparable results to traditional candidate-model methods, with low-confidence sampling proving most effective.
-
Towards Adversarially Robust Object Detection
Develops a multi-task learning based adversarial training approach to improve robustness of object detectors to adversarial attacks, with experiments on PASCAL-VOC and MS-COCO.
-
Balancing Uncertainty and Diversity of Samples: Leveraging Diversity of Least, High Confidence Samples for Effective Active Learning
Proposes LCD and three other hybrid uncertainty-diversity sampling methods for active learning that outperform prior approaches by selecting uncertain yet diverse samples.