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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Image-Specific Information Suppression and Implicit Local Alignment for Text-Based Person Search.

Shuanglin Yan, Hao Tang, Liyan Zhang

    IEEE Transactions on Neural Networks and Learning Systems
    |September 15, 2023
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    Summary
    This summary is machine-generated.

    This study introduces MANet, a novel network for text-based person search (TBPS). MANet effectively aligns image and text features at multiple levels, improving person identification accuracy.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Text-based person search (TBPS) aims to identify individuals in images using textual descriptions.
    • Current state-of-the-art methods often rely on explicit local part generation, which can be unreliable due to noise and lack of context.
    • Existing approaches frequently overlook the information inequality between image and text modalities.

    Purpose of the Study:

    • To propose an efficient joint multilevel alignment network (MANet) for improved text-based person search.
    • To address limitations of existing methods, including unreliable fine-grained correspondence and information inequality between modalities.
    • To achieve fast and effective person identification through enhanced cross-modal feature alignment.

    Main Methods:

    • Developed an image-specific information suppression (ISS) module using relation-guided localization (RGL) and channel attention filtration (CAF) to mitigate background noise and environmental factors.
    • Introduced an implicit local alignment (ILA) module for adaptive aggregation of pixel/word features to modality-shared semantic centers, enabling implicit fine-grained correspondence learning.
    • Incorporated a global alignment (GA) module to complement local alignment, enhancing overall semantic alignment between image and text modalities.

    Main Results:

    • The ISS module effectively suppresses image-specific information, reducing information inequality and aligning information volume between modalities.
    • The ILA module implicitly learns local fine-grained correspondences without requiring additional supervision or cross-modal interactions.
    • The combined local and global alignment strategies in MANet lead to superior semantic alignment and improved TBPS performance.

    Conclusions:

    • MANet offers an efficient and effective solution for text-based person search by addressing key limitations of existing methods.
    • The proposed ISS and ILA modules, along with global alignment, significantly enhance cross-modal feature representation and alignment.
    • Experimental results across multiple datasets validate the superiority and effectiveness of MANet for TBPS tasks.