  We propose a novel data augmentation method named 'FenceMask' that exhibits
outstanding performance in various computer vision tasks. It is based on the
'simulation of object occlusion' strategy, which aim to achieve the balance
between object occlusion and information retention of the input data. By
enhancing the sparsity and regularity of the occlusion block, our augmentation
method overcome the difficulty of small object augmentation and notably improve
performance over baselines. Sufficient experiments prove the performance of our
method is better than other simulate object occlusion approaches. We tested it
on CIFAR10, CIFAR100 and ImageNet datasets for Coarse-grained classification,
COCO2017 and VisDrone datasets for detection, Oxford Flowers, Cornel Leaf and
Stanford Dogs datasets for Fine-Grained Visual Categorization. Our method
achieved significant performance improvement on Fine-Grained Visual
Categorization task and VisDrone dataset.
