NegAS uses negative labels for attention guidance and sigmoid scoring to improve OOD detection in VLM-based object detectors while preserving ID performance.
arXiv:2304.04521 , year =
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
CoOD decomposes inputs into components and applies Component Shift Score plus Compositional Consistency Score to improve detection of both standard and compositional out-of-distribution data.
citing papers explorer
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NegAS: Negative Label Guided Attention and Scoring for Out-of-Distribution Object Detection with Vision-Language Models
NegAS uses negative labels for attention guidance and sigmoid scoring to improve OOD detection in VLM-based object detectors while preserving ID performance.
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Component-Based Out-of-Distribution Detection
CoOD decomposes inputs into components and applies Component Shift Score plus Compositional Consistency Score to improve detection of both standard and compositional out-of-distribution data.