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Automatic Processing and Automatic Social Behavior01:28

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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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Egocentric Action Recognition by Automatic Relation Modeling.

Haoxin Li, Wei-Shi Zheng, Jianguo Zhang

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    This study introduces a weakly supervised method for egocentric action recognition by automatically localizing interactors and modeling relations. The approach uses an ego-relational LSTM network to understand complex interactions in first-person videos.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Egocentric videos capture first-person perspectives, driving applications in life logging and health monitoring.
    • Egocentric action recognition is crucial but challenged by complex interactions between wearers and their environment.
    • Existing methods often require prior knowledge or auxiliary data for interactor localization, limiting their applicability.

    Purpose of the Study:

    • To develop a weakly supervised framework for egocentric action recognition.
    • To enable automatic modeling of relations between camera wearers and interacting persons/objects without prior knowledge.
    • To improve the accuracy and efficiency of recognizing actions in egocentric videos.

    Main Methods:

    • A weakly supervised framework unifying automatic interactor localization and explicit relation modeling.
    • Automatic localization of action-relevant regions using keypoints learned from video data.
    • An ego-relational LSTM network with differentiable architecture search to model temporal, interactive, and contextual relations.

    Main Results:

    • Demonstrated effectiveness of the proposed method on egocentric video datasets.
    • Successfully automated interactor localization and relation modeling.
    • Achieved improved egocentric action recognition performance through explicit relation modeling.

    Conclusions:

    • The proposed weakly supervised approach effectively models relations for egocentric action recognition.
    • Automatic interactor localization and ego-relational LSTM networks offer a robust solution.
    • This method reduces reliance on manual annotations and prior knowledge, advancing the field.