# DeePaste -- Inpainting for Pasting

arXiv: https://arxiv.org/abs/2112.10600 · HTML: https://mlsanity.com/paper/2112.10600/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 53 references that are in the map.
- First posted on arXiv 20 Dec 2021.

## Builds on (most cited first, 27 of 27)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://mlsanity.com/paper/2012.07177/) (2020)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection](https://mlsanity.com/paper/1708.01642/) (2017)
- [Deep Image Matting](https://mlsanity.com/paper/1703.03872/) (2017)
- [DoveNet: Deep Image Harmonization via Domain Verification](https://mlsanity.com/paper/1911.13239/) (2019)
- [Deep Image Harmonization](https://mlsanity.com/paper/1703.00069/) (2017)
- [High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis](https://mlsanity.com/paper/1611.09969/) (2016)
- [Generative Face Completion](https://mlsanity.com/paper/1704.05838/) (2017)
- [GP-GAN: Towards Realistic High-Resolution Image Blending](https://mlsanity.com/paper/1703.07195/) (2017)
- [Deep Image Blending](https://mlsanity.com/paper/1910.11495/) (2019)
- [Synthesizing Training Images for Boosting Human 3D Pose Estimation](https://mlsanity.com/paper/1604.02703/) (2016)
- [Learning to Generate Synthetic Data via Compositing](https://mlsanity.com/paper/1904.05475/) (2019)
- [On Pre-Trained Image Features and Synthetic Images for Deep Learning](https://mlsanity.com/paper/1710.10710/) (2017)
- [Learning Multi-scale Features for Foreground Segmentation](https://mlsanity.com/paper/1808.01477/) (2018)
- [Multiview RGB-D Dataset for Object Instance Detection](https://mlsanity.com/paper/1609.07826/) (2016)
- [On Rendering Synthetic Images for Training an Object Detector](https://mlsanity.com/paper/1411.7911/) (2014)
- [BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction](https://mlsanity.com/paper/2101.09585/) (2021)
- [Towards Benchmarking Scene Background Initialization](https://mlsanity.com/paper/1506.04051/) (2015)
- [Object Detection Using Deep CNNs Trained on Synthetic Images](https://mlsanity.com/paper/1706.06782/) (2017)
- [Pixel Objectness](https://mlsanity.com/paper/1701.05349/) (2017)
- [Using a Supervised Method without supervision for foreground segmentation](https://mlsanity.com/paper/2011.07954/) (2020)

## Built on by (most cited first, 1 of 1)

- [ForAug: Mitigating Biases in Image Classification via Controlled Image Compositions](https://mlsanity.com/paper/2503.09399/) (2025)

## Abstract (arXiv)

One of the challenges of supervised learning training is the need to procure an substantial amount of tagged data. A well-known method of solving this problem is to use synthetic data in a copy-paste fashion, so that we cut objects and paste them onto relevant backgrounds. Pasting the objects naively results in artifacts that cause models to give poor results on real data. We present a new method for cleanly pasting objects on different backgrounds so that the dataset created gives competitive performance on real data. The main emphasis is on the treatment of the border of the pasted object using inpainting. We show state-of-the-art results both on instance detection and foreground segmentation