RADSeg adapts the RADIO model with targeted enhancements to deliver 6-30% higher mIoU in zero-shot OVSS while using 2.5x fewer parameters and running 3.95x faster than prior large-model combinations.
Samrefiner: Taming segment anything model for universal mask refinement
3 Pith papers cite this work. Polarity classification is still indexing.
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Dual use of SAM for broader target pixel learning and DINOv3 for domain-invariant prototypes yields +1.3% and +1.4% mIoU gains over baselines on GTA-to-Cityscapes and SYNTHIA-to-Cityscapes.
A multi-scale weakly supervised framework converts underwater point classifications into coarse masks to train UAV coral segmentation models, then refines them via self-training to reach 86.07% pixel accuracy and 52.23% mIoU without pixel-level labels.
citing papers explorer
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RADSeg: Unleashing Parameter and Compute Efficient Zero-Shot Open-Vocabulary Segmentation Using Agglomerative Models
RADSeg adapts the RADIO model with targeted enhancements to deliver 6-30% higher mIoU in zero-shot OVSS while using 2.5x fewer parameters and running 3.95x faster than prior large-model combinations.
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Dual-Foundation Models for Unsupervised Domain Adaptation
Dual use of SAM for broader target pixel learning and DINOv3 for domain-invariant prototypes yields +1.3% and +1.4% mIoU gains over baselines on GTA-to-Cityscapes and SYNTHIA-to-Cityscapes.
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A drone-based framework for coral habitat mapping via weakly supervised segmentation
A multi-scale weakly supervised framework converts underwater point classifications into coarse masks to train UAV coral segmentation models, then refines them via self-training to reach 86.07% pixel accuracy and 52.23% mIoU without pixel-level labels.