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Self-Presentation: Self-Monitoring and Self-Handicapping02:05

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People can go to great lengths to protect their self-image and present themselves in ways that they want others to see them. Sociologist Erving Goffman presented the idea that a person is like an actor on a stage. Calling his theory dramaturgy, Goffman believed that we use “impression management” to present ourselves to others as we hope to be perceived. Each situation is a new scene, and individuals perform different roles depending on who is present (Goffman, 1959). Think about the way you...
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Surface-SOS: Self-Supervised Object Segmentation via Neural Surface Representation.

Xiaoyun Zheng, Liwei Liao, Jianbo Jiao

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 12, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Surface representation based Self-supervised Object Segmentation (Surface-SOS) enables object segmentation without annotations by using 3D surface representations from multi-view images. This novel approach refines single-view segmentation using unlabeled multi-view data, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • 3D Scene Reconstruction

    Background:

    • Object segmentation typically requires extensive annotated data.
    • Leveraging multi-view consistency offers potential for unsupervised segmentation.
    • Existing self-supervised methods often rely on strong constraints like static backgrounds or temporal information.

    Purpose of the Study:

    • To develop a novel self-supervised object segmentation framework using 3D surface representations.
    • To effectively utilize structural, textural, and geometrical consistency from multi-view images.
    • To reduce reliance on annotated data and strong supervision constraints.

    Main Methods:

    • Proposed Surface representation based Self-supervised Object Segmentation (Surface-SOS) framework.
    • Introduced a novel scene representation scheme decomposing scenes into two complementary neural modules using Signed Distance Functions (SDF).
    • Refined single-view segmentation by incorporating coarse masks and multi-view unlabeled images.

    Main Results:

    • Surface-SOS generates finer object masks compared to NeRF-based methods.
    • Achieved significant improvements over supervised single-view segmentation baselines.
    • Demonstrated effectiveness on diverse benchmarks (LLFF, CO3D, BlendedMVS, TUM) and real-world scenes.

    Conclusions:

    • Surface-SOS is the first self-supervised approach utilizing neural surface representation for object segmentation.
    • Effectively segments objects from multi-view images without manual annotations.
    • Offers a promising direction for unsupervised segmentation by leveraging geometric consistency.