Objectomaly: Objectness-Aware Refinement for OoD Segmentation with Structural Consistency and Boundary Precision
Journal:
arXiv
Published Date:
Jul 10, 2025
Abstract
Out-of-Distribution (OoD) segmentation is critical for safety-sensitive
applications like autonomous driving. However, existing mask-based methods
often suffer from boundary imprecision, inconsistent anomaly scores within
objects, and false positives from background noise. We propose
\textbf{\textit{Objectomaly}}, an objectness-aware refinement framework that
incorporates object-level priors. Objectomaly consists of three stages: (1)
Coarse Anomaly Scoring (CAS) using an existing OoD backbone, (2)
Objectness-Aware Score Calibration (OASC) leveraging SAM-generated instance
masks for object-level score normalization, and (3) Meticulous Boundary
Precision (MBP) applying Laplacian filtering and Gaussian smoothing for contour
refinement. Objectomaly achieves state-of-the-art performance on key OoD
segmentation benchmarks, including SMIYC AnomalyTrack/ObstacleTrack and
RoadAnomaly, improving both pixel-level (AuPRC up to 96.99, FPR$_{95}$ down to
0.07) and component-level (F1$-$score up to 83.44) metrics. Ablation studies
and qualitative results on real-world driving videos further validate the
robustness and generalizability of our method. Code will be released upon
publication.
