Abstract
Leveraging the characteristics of convolutional layers, image classifiers are extremely effective. However, recent works have exposed that in many cases they immoderately rely on global image statistics that are easy to manipulate while preserving image semantics. In text recognition, we reveal that it is rather the local image statistics which the networks overly depend on. Motivated by this, we suggest an approach to regulate the reliance on local statistics that improves overall text recognition performance. Our method, termed TextAdaIN, creates local distortions in the feature map which prevent the network from overfitting to the local statistics. It does so by deliberately mismatching fine-grained feature statistics between samples in a mini-batch. Despite TextAdaIN's simplicity, extensive experiments show its effectiveness compared to other, more complicated methods. TextAdaIN achieves state-of-the-art results on standard handwritten text recognition benchmarks. Additionally, it generalizes to multiple architectures and to the domain of scene text recognition. Furthermore, we demonstrate that integrating TextAdaIN improves robustness towards image corruptions.
Abstract (translated)
URL
https://arxiv.org/abs/2105.03906
