**CutBlur** is a data augmentation method that is specifically designed for the low-level vision tasks. It cuts a low-resolution patch and pastes it to the corresponding high-resolution image region and vice versa. The key intuition of Cutblur is to enable a model to learn not only "how" but also "where" to super-resolve an image. By doing so, the model can understand "how much" instead of blindly learning to apply super-resolution to every given pixel.

SimVLM is a minimalist pretraining framework to reduce training complexity by exploiting large-scale weak supervision. It is trained end-to-end with a single prefix language modeling (PrefixLM) objective. PrefixLM enables bidirectional attention within the prefix sequence, and thus it is applicable for both decoder-only
and encoder-decoder sequence-to-sequence language models.

SimVLM

SimVLM: Simple Visual Language Model Pretraining with Weak Supervision

CutBlur

Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy

Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the
performance of an OCR system. In this paper, we propose an effective end-to-end framework named Document Enhancement
Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs) to restore severely degraded document images.
To the best of our knowledge, this practice has not been studied within the context of generative adversarial deep networks. We
demonstrate that, in different tasks (document clean up, binarization, deblurring and watermark removal), DE-GAN can produce an
enhanced version of the degraded document with a high quality. In addition, our approach provides consistent improvements compared to state-of-the-art methods over the widely used DIBCO 2013, DIBCO 2017 and H-DIBCO 2018 datasets, proving its ability to restore a degraded document image to its ideal condition. The obtained results on a wide variety of degradation reveal the flexibility of the proposed model to be exploited in other document enhancement problems.

Source	Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com