arxiv_cv 97% Match Research Paper Autonomous Driving Engineers,Robotics Researchers,Computer Vision Scientists,AI Safety Researchers 1 month ago

Training-Free Out-Of-Distribution Segmentation With Foundation Models

computer-vision › scene-understanding

📄 Abstract

Abstract: Detecting unknown objects in semantic segmentation is crucial for safety-critical applications such as autonomous driving. Large vision foundation models, including DINOv2, InternImage, and CLIP, have advanced visual representation learning by providing rich features that generalize well across diverse tasks. While their strength in closed-set semantic tasks is established, their capability to detect out-of-distribution (OoD) regions in semantic segmentation remains underexplored. In this work, we investigate whether foundation models fine-tuned on segmentation datasets can inherently distinguish in-distribution (ID) from OoD regions without any outlier supervision. We propose a simple, training-free approach that utilizes features from the InternImage backbone and applies K-Means clustering alongside confidence thresholding on raw decoder logits to identify OoD clusters. Our method achieves 50.02 Average Precision on the RoadAnomaly benchmark and 48.77 on the benchmark of ADE-OoD with InternImage-L, surpassing several supervised and unsupervised baselines. These results suggest a promising direction for generic OoD segmentation methods that require minimal assumptions or additional data.

Key Contributions

This paper proposes a simple, training-free approach for detecting out-of-distribution (OoD) regions in semantic segmentation using features from vision foundation models like InternImage. By applying K-Means clustering and confidence thresholding on raw decoder logits, the method can distinguish in-distribution from OoD regions without requiring any outlier supervision.

Business Value

Enhances the safety and reliability of autonomous driving systems and other safety-critical applications by enabling them to reliably identify and react to unseen or unexpected objects and environments.

Paper Metadata

Innovation Type

Methodology

Deployment Feasibility

High, due to its training-free nature and reliance on pre-trained foundation models, making it easier to integrate.

Limitations Addressed

Difficulty in detecting unknown objects in semantic segmentation,Underexplored capability of foundation models for OoD detection,Need for outlier supervision in existing OoD methods

Technical Tags

out-of-distribution detectionsemantic segmentationvision foundation modelsDINOv2InternImageCLIPtraining-free approachK-Means clusteringconfidence thresholdingautonomous driving

Research Topics

Out-of-Distribution DetectionSemantic SegmentationFoundation ModelsRobustness in AIAutonomous Systems

Methods & Architectures

Feature extraction from foundation models (InternImage)K-Means clusteringConfidence thresholding on decoder logitsTraining-free OoD detection InternImageDINOv2CLIP

Applications & Tasks

Autonomous Driving Robotics Safety-Critical Systems Out-of-Distribution DetectionSemantic SegmentationUnknown Object Detection Out-of-Distribution SegmentationUnknown Object Detection

Datasets & Benchmarks

Datasets

RoadAnomaly, AD

Benchmarks

RoadAnomaly: 50.02 Average Precision • AD: 48.77 Average Precision

Average Precision (AP)

Related Fields

Computer VisionMachine LearningAutonomous SystemsAI Safety

Keywords

out-of-distribution detectionsemantic segmentationfoundation modelsDINOv2InternImageCLIPtraining-freeK-Meansautonomous drivingunknown objectsrobustnesscomputer visiondeep learningclustering

Academic Context

#Out-of-Distribution Detection#Semantic Segmentation#Foundation Models#Robustness in AI#Autonomous Systems

Commercial Potential

Potential Products

Enhanced perception systems for autonomous vehiclesRobust segmentation modules for industrial automation

Target Industries

AutomotiveRoboticsAerospaceSurveillance

Use Case Examples

Detecting unexpected obstacles on the road (e.g., debris, animals)Identifying novel objects in a robot's environmentEnsuring system robustness in diverse and unpredictable conditions

Competitive Edge

Offers a simpler and more efficient training-free alternative for OoD segmentation compared to methods requiring extensive outlier data or complex training procedures.

Market Opportunity

Large, driven by the autonomous vehicle and robotics industries.

Revenue Models

Licensing to automotive OEMs and Tier 1 suppliersintegration into robotics platforms.

Resource Requirements

Compute Needs

Inference requires features from foundation models, which can be computationally intensive. Training is not required for the OoD detection part.

Data Requirements

Requires segmentation datasets for feature extraction and potentially for clustering/thresholding calibration, though no explicit OoD labels are needed.

Deployment Constraints

Real-time performance requirements for autonomous driving,Integration with existing perception pipelines

Scalability

Scalability depends on the efficiency of the chosen foundation model and the clustering algorithm.

Regulatory Considerations

Safety standards for autonomous systems

Production Readiness

Maturity Level

Research/Development

Time to Market

1-3 years for integration into safety-critical systems.

Patent Potential

Low to Moderate, depending on specific algorithmic innovations.

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