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Found 9 papers, 4 papers with code
Adversarial Learning for Neural PDE Solvers with Sparse Data
Neural network solvers for partial differential equations (PDEs) have made significant progress, yet they continue to face challenges related to data scarcity and model robustness.
Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking
In the contemporary of deep learning, where models often grapple with the challenge of simultaneously achieving robustness against adversarial attacks and strong generalization capabilities, this study introduces an innovative Local Feature Masking (LFM) strategy aimed at fortifying the performance of Convolutional Neural Networks (CNNs) on both fronts.
Beyond Augmentation: Empowering Model Robustness under Extreme Capture Environments
This method improves the model's generalization under extreme conditions and enables learning diverse features, thus better addressing the challenges in re-ID.
Cross-Task Attack: A Self-Supervision Generative Framework Based on Attention Shift
Studying adversarial attacks on artificial intelligence (AI) systems helps discover model shortcomings, enabling the construction of a more robust system.
Exploring Color Invariance through Image-Level Ensemble Learning
This issue is particularly pronounced in complex wide-area surveillance scenarios, such as person re-identification and industrial dust segmentation, where models often experience a decline in performance due to overfitting on color information during training, given the presence of environmental variations.
Cross-Modality Perturbation Synergy Attack for Person Re-identification
For instance, infrared images are typically grayscale, unlike visible images that contain color information.
Person Re-identification Method Based on Color Attack and Joint Defence
Finally, a series of experimental results show that the proposed joint adversarial defense method is more competitive than a state-of-the-art methods.
A Person Re-identification Data Augmentation Method with Adversarial Defense Effect
This method can not only improve the accuracy of the model, but also help the model defend against adversarial examples; 2) Multi-Modal Defense, it integrates three homogeneous modal images of visible, grayscale and sketch, and further strengthens the defense ability of the model.
Ranked #19 on
Person Re-Identification
on Market-1501-C
Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method
Experiments on several ReID baselines and three common large-scale datasets such as Market1501, DukeMTMC, and MSMT17 have verified the effectiveness of this method.
Ranked #2 on
Person Re-Identification
on Market-1501
(using extra training data)
