GL-MCM: Global and Local Maximum Concept Matching for Zero-Shot Out-of-Distribution Detection

Understand

Zero-shot out-of-distribution (OOD) detection is a task that detects OOD images during inference with only in-distribution (ID) class names.

• Existing methods assume ID images contain a single, centered object, and do not consider the more realistic multi-object scenarios, where both ID and OOD objects are present.
• To meet the needs of many users, the detection method must have the flexibility to adapt the type of ID images.
• To this end, we present Global-Local Maximum Concept Matching (GL-MCM), which incorporates local image scores as an auxiliary score to enhance the separability of global and local visual features.