An approach based on performer-attention-guided few-shot learning model for plant disease classification

Boulila, Wadii

doi:10.1007/s12145-024-01339-x

Earth Sci Inform

2024

DOI: 10.1007/s12145-024-01339-x

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An approach based on performer-attention-guided few-shot learning model for plant disease classification

Wadii Boulila

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GaitSTAR: Spatial–Temporal Attention-Based Feature-Reweighting Architecture for Human Gait Recognition

Bilal,

Jianbiao,

Mushtaq

et al. 2024

Mathematics

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Human gait recognition (HGR) leverages unique gait patterns to identify individuals, but the effectiveness of this technique can be hindered due to various factors such as carrying conditions, foot shadows, clothing variations, and changes in viewing angles. Traditional silhouette-based systems often neglect the critical role of instantaneous gait motion, which is essential for distinguishing individuals with similar features. We introduce the ”Enhanced Gait Feature Extraction Framework (GaitSTAR)”, a novel method that incorporates dynamic feature weighting through the discriminant analysis of temporal and spatial features within a channel-wise architecture. Key innovations in GaitSTAR include dynamic stride flow representation (DSFR) to address silhouette distortion, a transformer-based feature set transformation (FST) for integrating image-level features into set-level features, and dynamic feature reweighting (DFR) for capturing long-range interactions. DFR enhances contextual understanding and improves detection accuracy by computing attention distributions across channel dimensions. Empirical evaluations show that GaitSTAR achieves impressive accuracies of 98.5%, 98.0%, and 92.7% under NM, BG, and CL conditions, respectively, with the CASIA-B dataset; 67.3% with the CASIA-C dataset; and 54.21% with the Gait3D dataset. Despite its complexity, GaitSTAR demonstrates a favorable balance between accuracy and computational efficiency, making it a powerful tool for biometric identification based on gait patterns.

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GaitSTAR: Spatial–Temporal Attention-Based Feature-Reweighting Architecture for Human Gait Recognition

Bilal,

Jianbiao,

Mushtaq

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

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An approach based on performer-attention-guided few-shot learning model for plant disease classification

Cited by 1 publication

References 35 publications

GaitSTAR: Spatial–Temporal Attention-Based Feature-Reweighting Architecture for Human Gait Recognition

GaitSTAR: Spatial–Temporal Attention-Based Feature-Reweighting Architecture for Human Gait Recognition

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