A confidence-feedback-weighted graph matching network achieves 96.36% F1-score on damage site matching by using matchability confidence to weight edge features and applying geometric consistency and hard-example mining.
Fast explicit diffusion for accelerated features in nonlinear scale spaces
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
method 1polarities
use method 1representative citing papers
OpenPRC provides a schema-driven framework with five modules for GPU physics simulation, experimental vision ingestion, reservoir learning, information analysis, and physics-aware optimization to enable consistent PRC evaluation from simulations and real experiments.
citing papers explorer
-
Confidence-feedback-weighted graph matching network: online-offline laser-induced damage site matching under complex interference
A confidence-feedback-weighted graph matching network achieves 96.36% F1-score on damage site matching by using matchability confidence to weight edge features and applying geometric consistency and hard-example mining.
-
OpenPRC: A Unified Open-Source Framework for Physics-to-Task Evaluation in Physical Reservoir Computing
OpenPRC provides a schema-driven framework with five modules for GPU physics simulation, experimental vision ingestion, reservoir learning, information analysis, and physics-aware optimization to enable consistent PRC evaluation from simulations and real experiments.