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Related Concept Videos

Retrieval01:12

Retrieval

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Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
503

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Taking Astray Domain Back Home for Single-Source Domain Generalizable Text-to-Image Person Retrieval.

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    Summary
    This summary is machine-generated.

    This study introduces single-source domain generalizable text-to-image person retrieval (SSDG-TIPR) for real-world surveillance. The proposed TIME method effectively adapts models to unseen domains, achieving state-of-the-art performance.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Text-to-image person retrieval (TIPR) typically assumes unified data distributions, limiting real-world applicability.
    • Surveillance data often originates from diverse scenarios, violating the unified domain assumption.
    • Existing TIPR methods struggle with domain shifts due to varied data collection environments.

    Purpose of the Study:

    • Introduce single-source domain generalizable text-to-image person retrieval (SSDG-TIPR) for realistic surveillance.
    • Address the challenge of limited training data in SSDG-TIPR.
    • Propose a novel method, TIME, to adapt models to unseen target domains.

    Main Methods:

    • Propose the single-source domain generalizable text-to-image person retrieval (SSDG-TIPR) task.
    • Introduce the 'to take it home' (TIME) method for domain generalization.
    • TIME comprises Domain Astray Leading (DAL), Domain Invariant Feature Extract (DIFE), and Domain Home Taking (DoT) modules.

    Main Results:

    • TIME demonstrated superior performance on 10 SSDG-TIPR sub-tasks.
    • The method also achieved state-of-the-art results on 3 conventional TIPR sub-tasks.
    • Evaluated on CUHK-PEDES, ICFG-PEDES, and RSTPReid benchmark datasets.

    Conclusions:

    • The proposed TIME method effectively handles domain shifts in text-to-image person retrieval.
    • SSDG-TIPR is a more realistic and challenging task for practical surveillance applications.
    • TIME establishes a new state-of-the-art for both SSDG-TIPR and conventional TIPR settings.