DBMF integrates scores from text-image and vision branches to improve out-of-distribution detection on endoscopic datasets by up to 24.84% over prior methods.
Pattern recognition letters27(8), 861–874 (2006)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Compares domain-aware, case-based, and feature attribution explainability methods for gate-level hardware Trojan detection on the Trust-Hub benchmark dataset.
citing papers explorer
-
DBMF: A Dual-Branch Multimodal Framework for Out-of-Distribution Detection
DBMF integrates scores from text-image and vision branches to improve out-of-distribution detection on endoscopic datasets by up to 24.84% over prior methods.
-
Explainability Methods for Hardware Trojan Detection: A Systematic Comparison
Compares domain-aware, case-based, and feature attribution explainability methods for gate-level hardware Trojan detection on the Trust-Hub benchmark dataset.