DHANet uses multi-scale spatial and channel aggregation with a probabilistic bank to mitigate over-alignment and reports state-of-the-art results on four target datasets for cross-domain few-shot segmentation.
Self-disentanglement and re-composition for cross-domain few-shot segmentation
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HERA is a select-regularize-calibrate framework adapting frozen vision foundation models for cross-domain few-shot semantic segmentation via hierarchical layer selection with ETR, prior-guided regularization, and pixelwise adaptive calibration, reporting over 4.1 mIoU gains.
citing papers explorer
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Hierarchical Spatial and Channel Aggregation for Cross-domain Few-shot Segmentation
DHANet uses multi-scale spatial and channel aggregation with a probabilistic bank to mitigate over-alignment and reports state-of-the-art results on four target datasets for cross-domain few-shot segmentation.
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Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation
HERA is a select-regularize-calibrate framework adapting frozen vision foundation models for cross-domain few-shot semantic segmentation via hierarchical layer selection with ETR, prior-guided regularization, and pixelwise adaptive calibration, reporting over 4.1 mIoU gains.