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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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Improving Inconspicuous Attributes Modeling for Person Search by Language.

Kai Niu, Tao Huang, Linjiang Huang

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|June 13, 2023

Summary

This study introduces the Adaptive Salient Attribute Mask Network (ASAMN) for person search by language. ASAMN improves pedestrian retrieval by focusing on both obvious and subtle features, outperforming existing methods.

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Person search by language aims to match natural language descriptions with pedestrian images.
Current methods struggle with cross-modal heterogeneity, often focusing only on salient attributes and failing to distinguish similar pedestrians.

Purpose of the Study:

To propose a novel network, the Adaptive Salient Attribute Mask Network (ASAMN), for improved person retrieval.
To address the limitation of current methods by enabling focus on inconspicuous attributes alongside salient ones.

Main Methods:

Introduced the Adaptive Salient Attribute Mask Network (ASAMN) for adaptive masking of salient attributes.
Developed Uni-modal Salient Attribute Mask (USAM) and Cross-modal Salient Attribute Mask (CSAM) modules to consider uni-modal and cross-modal relations.
Implemented an Attribute Modeling Balance (AMB) module to balance the modeling capacity of salient and inconspicuous attributes during cross-modal alignment.

Main Results:

The ASAMN method demonstrated effectiveness and generalization capacity in extensive experiments.
Achieved state-of-the-art retrieval performance on the CUHK-PEDES and ICFG-PEDES benchmarks.
The proposed approach successfully addresses the challenge of distinguishing similar pedestrians by considering less obvious attributes.

Conclusions:

The ASAMN method offers a significant advancement in person search by language.
Adaptive masking and balanced attribute modeling are crucial for enhancing cross-modal retrieval accuracy.
The findings suggest a new direction for developing more robust and discriminative cross-modal retrieval systems.