Combining Foundation Model Confidence and Monocular Depth for Training-Free Out-of-Distribution Segmentation

arXiv:2609.22896 · cs.CV · Submitted 2026-09-19 · Read on arXiv

cs.CV

Submitted: 2026-09-19

Updated: 2026-09-19

Code: https://github.com/serin-varghese/OODTrack

Project page: https://rrow2024.github.io/challenge

Terminology

• Benchmark — 19× in this paper · explained in 2 episodes
• Foundation Model — 9× in this paper · explained in 3 episodes
• Verification — 7× in this paper · explained in 2 episodes
• Foundation Models — 6× in this paper · explained in 7 episodes
• International Conference on Computer Vision — 5× in this paper
• Adaptation — 4× in this paper · explained in 1 episode
• Adversarial Attacks — 4× in this paper · explained in 1 episode
• Robustness — 4× in this paper · explained in 5 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Accuracy — 3× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Normalizing Flow — 3× in this paper · explained in 3 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Asian Conference on Computer Vision — 2× in this paper
• F1 Score — 2× in this paper · explained in 3 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Abstention — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Decoupling — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Modularity — 1× in this paper · explained in 9 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• State Management — 1× in this paper · explained in 3 episodes
• Training-Free Framework — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Winter Conference on Applications of Computer Vision — 1× in this paper
• Wuppertal Obstacle Sequences — 1× in this paper

Sources

• Evaluating Bayesian Deep Learning Methods for Semantic Segmentation
• A Likelihood Ratio-Based Approach to Segmenting Unknown Objects
• Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data
• Out-of-Distribution Segmentation via Wasserstein-Based Evidential Uncertainty

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