RAS is a plug-and-play OOD detection technique that applies ranked activation shifts to a fixed reference profile for stable discrimination without hyperparameters.
In: International Conference on Machine Learning
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
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Ranked Activation Shift for Post-Hoc Out-of-Distribution Detection
RAS is a plug-and-play OOD detection technique that applies ranked activation shifts to a fixed reference profile for stable discrimination without hyperparameters.
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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.