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Related Concept Videos

Concepts and Prototypes01:24

Concepts and Prototypes

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The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
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Natural and Artificial Concepts01:24

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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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Related Experiment Video

Updated: Sep 19, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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$C^{2}D$C2D: Context-Aware Concept Decomposition for Personalized Text-to-Image Synthesis.

Jiang Xin, Xiaonan Fang, Xueling Zhu

    IEEE Transactions on Visualization and Computer Graphics
    |June 16, 2025
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    Summary

    We introduce Context-aware Concept Decomposition (C2D) to improve personalized text-to-image synthesis by resolving contextual conflicts. Our method enhances subconcept recombination for better image quality and consistency.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Machine Learning

    Background:

    • Concept decomposition enables personalized text-to-image synthesis by learning subconcept embeddings from images.
    • Existing methods struggle with contextual conflicts when combining subconcepts from different sources due to encapsulated contextual information.

    Purpose of the Study:

    • To propose a novel Context-aware Concept Decomposition (C2D) framework to address contextual conflicts in text-to-image synthesis.
    • To enable more controllable image generation through subconcept recombination by disentangling context.

    Main Methods:

    • Developed a Similarity-Guided Divergent Embedding (SGDE) method for obtaining subconcept embeddings.
    • Eliminated latent contextual dependence within subconcept embeddings.
    • Reconstructed contextual information using an independent contextual embedding for flexible combination with subconcepts.

    Main Results:

    • The C2D framework successfully resolves contextual conflicts in text-to-image synthesis.
    • Experimental results show superior performance compared to existing methods in terms of image quality.
    • The proposed method achieves enhanced contextual consistency in generated images.

    Conclusions:

    • The C2D framework offers a robust solution for controllable text-to-image synthesis by effectively managing subconcept recombination.
    • Disentangling context from subconcept embeddings allows for greater flexibility and improved generation quality.