Authors: Poulami Sinhamahapatra, Franziska Schwaiger, Shirsha Bose, Huiyu Wang, Karsten Roscher, Stephan Guennemann

Published on: April 11, 2024

Impact Score: 7.2

Arxiv code: Arxiv:2404.07664

Summary

• What is new: Introducing PROWL, a plug-and-play framework for OOD object detection that works without domain-specific training.
• Why this is important: The challenge of detecting and localizing unknown or OOD objects in vision, crucial for autonomous systems.
• What the research proposes: PROWL framework that leverages self-supervised pre-trained models for inference-based OOD detection without requiring domain dataset training.
• Results: PROWL outperforms supervised methods in detecting OOD objects on benchmark datasets, proving effective across various domains like rail and maritime.

Technical Details

Technological frameworks used: PROWL (PRototype-based zero-shot OOD detection Without Labels)

Models used: Self-supervised pre-trained models

Data used: RoadAnomaly, RoadObstacle datasets from the SMIYC benchmark

Potential Impact

Automotive, autonomous vehicle manufacturers, maritime and rail industries could benefit from or be disrupted by PROWL’s advancements in OOD detection.

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