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FenceMask: A Data Augmentation Approach for Pre-extracted Image Features
We propose a novel data augmentation method named 'FenceMask' that exhib...
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GridMask Data Augmentation
We propose a novel data augmentation method `GridMask' in this paper. It...
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ANDA: A Novel Data Augmentation Technique Applied to Salient Object Detection
In this paper, we propose a novel data augmentation technique (ANDA) app...
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VideoMix: Rethinking Data Augmentation for Video Classification
State-of-the-art video action classifiers often suffer from overfitting....
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BitMix: Data Augmentation for Image Steganalysis
Convolutional neural networks (CNN) for image steganalysis demonstrate b...
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ADA: A Game-Theoretic Perspective on Data Augmentation for Object Detection
The use of random perturbations of ground truth data, such as random tra...
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ResizeMix: Mixing Data with Preserved Object Information and True Labels
Data augmentation is a powerful technique to increase the diversity of d...
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Thumbnail: A Novel Data Augmentation for Convolutional Neural Network
In this paper, we propose a new data augmentation strategy named Thumbnail, which aims to strengthen the network's capture of global features. We get a generated image by reducing an image to a certain size, which is called as the thumbnail, and pasting it in the random position of the original image. The generated image not only retains most of the original image information but also has the global information in the thumbnail. Furthermore, we find that the idea of thumbnail can be perfectly integrated with Mixed Sample Data Augmentation, so we paste the thumbnail in another image where the ground truth labels are also mixed with a certain weight, which makes great achievements on various computer vision tasks. Extensive experiments show that Thumbnail works better than the state-of-the-art augmentation strategies across classification, fine-grained image classification, and object detection. On ImageNet classification, ResNet50 architecture with our method achieves 79.21 which is more than 2.89
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