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Found 7 papers, 1 papers with code
Instruct2Attack: Language-Guided Semantic Adversarial Attacks
We propose Instruct2Attack (I2A), a language-guided semantic attack that generates semantically meaningful perturbations according to free-form language instructions.
GaitContour: Efficient Gait Recognition based on a Contour-Pose Representation
Gait recognition holds the promise to robustly identify subjects based on walking patterns instead of appearance information.
Self-Supervised Learning of Whole and Component-Based Semantic Representations for Person Re-Identification
To address these limitations, we propose a Local Semantic Extraction (LSE) module inspired by Interactive Segmentation Models.
A study on the impact of pre-trained model on Just-In-Time defect prediction
These findings emphasize the effectiveness of transformer-based pre-trained models in JIT defect prediction tasks, especially in scenarios with limited training data.
GADER: GAit DEtection and Recognition in the Wild
Gait recognition holds the promise of robustly identifying subjects based on their walking patterns instead of color information.
Multi-Modal Human Authentication Using Silhouettes, Gait and RGB
In this work, we propose Dual-Modal Ensemble (DME), which combines both RGB and silhouette data to achieve more robust performances for indoor and outdoor whole-body based recognition.
