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Related Concept Videos

Retrieval01:12

Retrieval

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...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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ER Retrieval Pathway

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Related Experiment Videos

Achieving Text-based Person Retrieval with Any Granularity.

Jialong Zuo, Hanyu Zhou, Dongyue Wu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |July 1, 2026
    PubMed
    Summary

    This study introduces a new method for text-based person retrieval that handles varying levels of detail in search queries. The proposed Cross-modal Multi-grained Aligning and Matching (CMAM) framework significantly improves retrieval accuracy across all granularities.

    Related Experiment Videos

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Information Retrieval

    Background:

    • Text-based person retrieval is challenged by uncertain query granularity.
    • Existing methods struggle with the ambiguity of real-world search queries.

    Purpose of the Study:

    • To introduce a new paradigm for text-based person retrieval that accommodates any query granularity.
    • To develop a systematic solution addressing the challenge of uncertain query granularity in person retrieval.

    Main Methods:

    • Formalized a five-level granularity spectrum and created the UFine6926-MG multi-grained dataset.
    • Developed MG-Eval, a holistic evaluation benchmark with tailored metrics and protocols.
    • Proposed the Cross-modal Multi-grained Aligning and Matching (CMAM) framework with orthogonal-expert perception, probabilistic alignment, and granularity-consistent reasoning.

    Main Results:

    • CMAM framework significantly outperforms state-of-the-art methods across all granularity levels.
    • Experiments demonstrate the effectiveness of the proposed CMAM framework in handling multi-grained queries.
    • The UFine6926-MG dataset and MG-Eval benchmark provide robust resources for future research.

    Conclusions:

    • The proposed CMAM framework offers a robust solution for granularity-aware text-based person retrieval.
    • This work establishes a foundational benchmark and baseline for more practical person retrieval systems.
    • The developed methods and datasets pave the way for enhanced real-world applications of person retrieval technology.