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Found 5 papers, 4 papers with code
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)
