A dual-branch adapter module called LCA with contrast maps and pairwise training on a Unity synthetic dataset improves SAM's instance segmentation performance across lighting variations.
arXiv preprint arXiv:2505.09274 (2025)
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A new reliability score computed from the IoU difference between class-specific and class-agnostic heatmaps, boosted by adversarial enhancement, detects false negatives in binary industrial defect detectors with up to 100% recall.
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Lighting-aware Unified Model for Instance Segmentation
A dual-branch adapter module called LCA with contrast maps and pairwise training on a Unity synthetic dataset improves SAM's instance segmentation performance across lighting variations.
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When Can We Trust Deep Neural Networks? Towards Reliable Industrial Deployment with an Interpretability Guide
A new reliability score computed from the IoU difference between class-specific and class-agnostic heatmaps, boosted by adversarial enhancement, detects false negatives in binary industrial defect detectors with up to 100% recall.