# Edge Augmentation for Large-Scale Sketch Recognition without Sketches

arXiv: https://arxiv.org/abs/2202.13164 · HTML: https://mlsanity.com/paper/2202.13164/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 71 references that are in the map.
- First posted on arXiv 26 Feb 2022.

## Builds on (most cited first, 30 of 34)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Moment Matching for Multi-Source Domain Adaptation](https://mlsanity.com/paper/1812.01754/) (2018)
- [Deeper, Broader and Artier Domain Generalization](https://mlsanity.com/paper/1710.03077/) (2017)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Domain Generalization via Invariant Feature Representation](https://mlsanity.com/paper/1301.2115/) (2013)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [Generalizing to Unseen Domains via Adversarial Data Augmentation](https://mlsanity.com/paper/1805.12018/) (2018)
- [Domain Generalization by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1903.06864/) (2019)
- [A Neural Representation of Sketch Drawings](https://mlsanity.com/paper/1704.03477/) (2017)
- [Learning to Learn Single Domain Generalization](https://mlsanity.com/paper/2003.13216/) (2020)
- [Learning to Generate Novel Domains for Domain Generalization](https://mlsanity.com/paper/2007.03304/) (2020)
- [Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data](https://mlsanity.com/paper/1909.00889/) (2019)
- [SelfReg: Self-supervised Contrastive Regularization for Domain Generalization](https://mlsanity.com/paper/2104.09841/) (2021)
- [Learning to Diversify for Single Domain Generalization](https://mlsanity.com/paper/2108.11726/) (2021)
- [Domain Adaptation for Visual Applications: A Comprehensive Survey](https://mlsanity.com/paper/1702.05374/) (2017)
- [Doodle to Search: Practical Zero-Shot Sketch-based Image Retrieval](https://mlsanity.com/paper/1904.03451/) (2019)
- [Bi-Directional Cascade Network for Perceptual Edge Detection](https://mlsanity.com/paper/1902.10903/) (2019)
- [Towards Recognizing Unseen Categories in Unseen Domains](https://mlsanity.com/paper/2007.12256/) (2020)
- [Learning to Sketch with Shortcut Cycle Consistency](https://mlsanity.com/paper/1805.00247/) (2018)
- [Sketch-based 3D Shape Retrieval using Convolutional Neural Networks](https://mlsanity.com/paper/1504.03504/) (2015)
- [More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval](https://mlsanity.com/paper/2103.13990/) (2021)
- [Multi-Graph Transformer for Free-Hand Sketch Recognition](https://mlsanity.com/paper/1912.11258/) (2019)
- [One Sketch for All: One-Shot Personalized Sketch Segmentation](https://mlsanity.com/paper/2112.10838/) (2021)
- [Asymmetric Feature Maps with Application to Sketch Based Retrieval](https://mlsanity.com/paper/1704.03946/) (2017)
- [SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks](https://mlsanity.com/paper/2003.00678/) (2020)

## Built on by (most cited first, 4 of 4)

- [Block and Detail: Scaffolding Sketch-to-Image Generation](https://mlsanity.com/paper/2402.18116/) (2024)
- [Crafting Distribution Shifts for Validation and Training in Single Source Domain Generalization](https://mlsanity.com/paper/2409.19774/) (2024)
- [Do Generalised Classifiers really work on Human Drawn Sketches?](https://mlsanity.com/paper/2407.03893/) (2024)
- [Co-Segmentation without any Pixel-level Supervision with Application to Large-Scale Sketch Classification](https://mlsanity.com/paper/2410.13582/) (2024)

## Abstract (arXiv)

This work addresses scaling up the sketch classification task into a large number of categories. Collecting sketches for training is a slow and tedious process that has so far precluded any attempts to large-scale sketch recognition. We overcome the lack of training sketch data by exploiting labeled collections of natural images that are easier to obtain. To bridge the domain gap we present a novel augmentation technique that is tailored to the task of learning sketch recognition from a training set of natural images. Randomization is introduced in the parameters of edge detection and edge selection. Natural images are translated to a pseudo-novel domain called "randomized Binary Thin Edges" (rBTE), which is used as a training domain instead of natural images. The ability to scale up is demonstrated by training CNN-based sketch recognition of more than 2.5 times larger number of categories than used previously. For this purpose, a dataset of natural images from 874 categories is constructed by combining a number of popular computer vision datasets. The categories are selected to be suitable for sketch recognition. To estimate the performance, a subset of 393 categories with sketches is also collected.