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Open World Entity Segmentation.

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    We introduce Entity Segmentation (ES), a new task for segmenting all visual entities in images without predicting labels. This approach enhances segmentation quality for applications like image editing, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Image Processing

    Background:

    • Traditional image segmentation often requires semantic label prediction, which can be computationally intensive and less critical for certain applications.
    • Existing methods like panoptic segmentation focus on class-specific segmentation, potentially limiting generalization and mask quality.

    Purpose of the Study:

    • Introduce and investigate Entity Segmentation (ES), a novel task focused solely on high-quality segmentation of all visual entities (objects and stuffs) in an image.
    • Explore the efficacy of convolutional center-based representations for unified, class-agnostic segmentation within the ES framework.
    • Develop and evaluate a new architecture tailored for the unique demands of ES, prioritizing segmentation mask quality.

    Main Methods:

    • Propose a class-agnostic, fully-convolutional architecture inspired by CondInst, incorporating novel modules for ES.
    • Utilize a convolutional center-based representation to unify the segmentation of 'things' and 'stuffs'.
    • Train and evaluate models on diverse datasets, assessing performance against class-specific panoptic segmentation benchmarks.

    Main Results:

    • Entity Segmentation models significantly outperform popular class-specific panoptic segmentation models in terms of segmentation quality.
    • The proposed convolutional center-based representation is highly effective for ES, handling both objects and stuffs cohesively.
    • ES models demonstrate strong generalization capabilities across different datasets and domains, even when trained on combined datasets without label conflict resolution.

    Conclusions:

    • Entity Segmentation is a viable and effective task for improving segmentation quality, particularly for applications where semantic labels are secondary.
    • The proposed class-agnostic, center-based approach offers a promising direction for unified and high-performance image segmentation.
    • ES models exhibit robustness and adaptability, simplifying multi-dataset training and enhancing cross-domain generalization.