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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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    This study introduces subjective video captioning (SVC) to generate personalized emotional descriptions by considering viewer reactions. The proposed Subjective-Objective Emotion-Correlated Generation Network (SO-ECGN) enhances emotional understanding in video content.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Multimedia Processing

    Background:

    • Emotional Video Captioning (EVC) focuses on objective descriptions from visual cues.
    • Existing EVC methods neglect personalized viewer emotional responses.
    • A gap exists in understanding subjective affective experiences in video content.

    Purpose of the Study:

    • To introduce and address the novel task of Subjective Video Captioning (SVC).
    • To generate emotional captions reflecting personalized viewer reactions.
    • To bridge the gap between objective emotion perception and subjective affective understanding.

    Main Methods:

    • Proposed the Subjective-Objective Emotion-Correlated Generation Network (SO-ECGN).
    • Utilized dynamic mask attention and emotion domain shifting for objective emotion learning.
    • Developed a subjective-objective emotions correlation module for adaptive emotion guidance.
    • Implemented an emotion-correlated decoder for generating subjective captions.

    Main Results:

    • SO-ECGN demonstrated superior performance on challenging datasets.
    • Achieved 79.2% on BLEU-1 and 45.1% on CIDEr metrics on the EmVidCap-L dataset.
    • Validation of the effectiveness of individual modules within the SO-ECGN framework.

    Conclusions:

    • The proposed SO-ECGN effectively generates subjective emotional captions.
    • SVC is a promising direction for personalized video understanding.
    • The approach successfully integrates factual and emotional information for enhanced captioning.