Introduces LCA dual-branch adapter and pairwise loss for lighting-robust SAM instance segmentation, validated on existing benchmarks plus a new Unity synthetic dataset.
arXiv preprint arXiv:2505.09274 (2025)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
-
Lighting-aware Unified Model for Instance Segmentation
Introduces LCA dual-branch adapter and pairwise loss for lighting-robust SAM instance segmentation, validated on existing benchmarks plus a new Unity synthetic dataset.
-
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.