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Benchmarking the Robustness of Instance Segmentation Models.

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    This study evaluates instance segmentation models for real-world use. Group normalization (GN) improves robustness to image corruptions, while batch normalization (BN) aids generalization across datasets.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Image Analysis

    Background:

    • Instance segmentation models are crucial for real-world applications.
    • Evaluating model performance under real-world image corruptions and out-of-domain data is essential for deployment.
    • Domain adaptation is a key area for improving generalization capabilities.

    Purpose of the Study:

    • To comprehensively evaluate instance segmentation models against real-world image corruptions and out-of-domain datasets.
    • To assess the impact of various architectural choices and training strategies on model robustness and generalization.
    • To provide insights for selecting or designing robust instance segmentation models for practical applications.

    Main Methods:

    • Benchmarking state-of-the-art instance segmentation architectures, backbones, and normalization layers.
    • Comparing models trained from scratch versus pretrained networks.
    • Investigating the effect of multitask training on robustness and generalization.
    • Evaluating performance on corrupted and out-of-domain image collections.

    Main Results:

    • Group normalization (GN) enhances robustness against image corruptions.
    • Batch normalization (BN) improves generalization across different datasets with varying feature statistics.
    • Single-stage detectors exhibit poor generalization to larger image resolutions, unlike multi-stage detectors.
    • Pretrained models and multitask training influence robustness and generalization.

    Conclusions:

    • Model design choices significantly impact robustness and generalization in instance segmentation.
    • GN and BN offer distinct advantages for different types of performance degradation.
    • Multi-stage detectors are more adaptable to varying image resolutions.
    • This benchmark guides the development and selection of reliable instance segmentation models for real-world deployment.