text: 465,162,1392057546567991296,915988922831863808,Ron Litman,"Happy to share our new paper named “TextAdaIN: Fine-Grained AdaIN for Robust Text Recognition”.  This is a join work with Oren Nuriel and Sharon Fogel.  See more details in our paper: <LINK> <LINK> In this paper we reveal that state-of-the-art text recognizers are prone to overly rely on local image statistics. We propose a simple normalization-based technique for moderating the reliance on local statistics, which enhances the performance of text recognizers.",https://arxiv.org/abs/2105.03906,"Leveraging the characteristics of convolutional layers, neural networks are extremely effective for pattern recognition tasks. However in some cases, their decisions are based on unintended information leading to high performance on standard benchmarks but also to a lack of generalization to challenging testing conditions and unintuitive failures. Recent work has termed this ""shortcut learning"" and addressed its presence in multiple domains. In text recognition, we reveal another such shortcut, whereby recognizers overly depend on local image statistics. Motivated by this, we suggest an approach to regulate the reliance on local statistics that improves text recognition performance. Our method, termed TextAdaIN, creates local distortions in the feature map which prevent the network from overfitting to local statistics. It does so by viewing each feature map as a sequence of elements and deliberately mismatching fine-grained feature statistics between elements 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 more challenging testing conditions. ",TextAdaIN: Paying Attention to Shortcut Learning in Text Recognizers,2,"['Happy to share our new paper named “TextAdaIN: Fine-Grained AdaIN for Robust Text Recognition”. \n\nThis is a join work with Oren Nuriel and Sharon Fogel. \n\nSee more details in our paper: <LINK> <LINK>', 'In this paper we reveal that state-of-the-art text recognizers are prone to overly rely on local image statistics. We propose a simple normalization-based technique for moderating the reliance on local statistics, which enhances the performance of text recognizers.']",21,05,463