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Related Experiment Video

Updated: May 7, 2026

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
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DGPDL: Domain-Guided Prompt Distribution Learning for Generalizable Face Anti-spoofing.

Ajian Liu, Xun Lin, Ruicong Zhi

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 16, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Domain-Guided Prompt Distribution Learning (DGPDL) enhances face anti-spoofing by using prompts to represent domain signals, improving generalization across different domains without retraining.

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    Last Updated: May 7, 2026

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Overfitting to source domain signals hinders face anti-spoofing generalization.
    • Current methods improving source domain diversity offer limited benefits for unseen target domains.
    • Domain gaps cause understanding bias in face anti-spoofing models.

    Purpose of the Study:

    • To propose a novel Domain-Guided Prompt Distribution Learning (DGPDL) method.
    • To alleviate understanding bias and improve domain generalization in face anti-spoofing.
    • To leverage Vision-Language Models for a unified representation of domain signals.

    Main Methods:

    • Developed a learnable Domain-Specific Distribution (DSD) to connect various domain elements.
    • Constructed optimal Domain-Specific Prompts (DSPs) using Prompt Assemble Attention (PAA) based on style statistics.
    • Utilized assembled DSPs in both vision and language branches of Vision-Language Models.

    Main Results:

    • DGPDL effectively reduces reliance on specific domain appearances by representing domain signals as prompts.
    • The model dynamically adapts to unseen target domains without retraining.
    • Achieved state-of-the-art performance on several cross-domain face anti-spoofing benchmarks.

    Conclusions:

    • DGPDL offers a robust solution for face anti-spoofing domain generalization.
    • Uniform prompt representation of domain signals ensures applicability to target domains.
    • The proposed method significantly improves the robustness and adaptability of face anti-spoofing systems.