# Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval

arXiv: https://arxiv.org/abs/2203.14817 · HTML: https://mlsanity.com/paper/2203.14817/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 61 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 30 of 41)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Stacked Cross Attention for Image-Text Matching](https://mlsanity.com/paper/1803.08024/) (2018)
- [A Neural Representation of Sketch Drawings](https://mlsanity.com/paper/1704.03477/) (2017)
- [Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation](https://mlsanity.com/paper/1811.10092/) (2018)
- [Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion](https://mlsanity.com/paper/1902.03680/) (2019)
- [SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color](https://mlsanity.com/paper/1902.06838/) (2019)
- [Masking: A New Perspective of Noisy Supervision](https://mlsanity.com/paper/1805.08193/) (2018)
- [SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis](https://mlsanity.com/paper/1801.02753/) (2018)
- [Doodle to Search: Practical Zero-Shot Sketch-based Image Retrieval](https://mlsanity.com/paper/1904.03451/) (2019)
- [Sketchformer: Transformer-based Representation for Sketched Structure](https://mlsanity.com/paper/2002.10381/) (2020)
- [A Zero-Shot Framework for Sketch-based Image Retrieval](https://mlsanity.com/paper/1807.11724/) (2018)
- [Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval](https://mlsanity.com/paper/1703.05605/) (2017)
- [Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval](https://mlsanity.com/paper/2002.10310/) (2020)
- [Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval](https://mlsanity.com/paper/1903.03372/) (2019)
- [Interactive Sketch & Fill: Multiclass Sketch-to-Image Translation](https://mlsanity.com/paper/1909.11081/) (2019)
- [StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval](https://mlsanity.com/paper/2103.15706/) (2021)
- [Zero-Shot Sketch-Image Hashing](https://mlsanity.com/paper/1803.02284/) (2018)
- [Unsupervised Sketch-to-Photo Synthesis](https://mlsanity.com/paper/1909.08313/) (2019)
- [Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting](https://mlsanity.com/paper/2103.13716/) (2021)
- [More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval](https://mlsanity.com/paper/2103.13990/) (2021)
- [Sketch Your Own GAN](https://mlsanity.com/paper/2108.02774/) (2021)

## Built on by (most cited first, 27 of 27)

- [CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not](https://mlsanity.com/paper/2303.13440/) (2023)
- [Picture that Sketch: Photorealistic Image Generation from Abstract Sketches](https://mlsanity.com/paper/2303.11162/) (2023)
- [SceneTrilogy: On Human Scene-Sketch and its Complementarity with Photo and Text](https://mlsanity.com/paper/2204.11964/) (2022)
- [It's All About Your Sketch: Democratising Sketch Control in Diffusion Models](https://mlsanity.com/paper/2403.07234/) (2024)
- [What Can Human Sketches Do for Object Detection?](https://mlsanity.com/paper/2303.15149/) (2023)
- [Adaptive Fine-Grained Sketch-Based Image Retrieval](https://mlsanity.com/paper/2207.01723/) (2022)
- [Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR](https://mlsanity.com/paper/2303.13779/) (2023)
- [You'll Never Walk Alone: A Sketch and Text Duet for Fine-Grained Image Retrieval](https://mlsanity.com/paper/2403.07222/) (2024)
- [How to Handle Sketch-Abstraction in Sketch-Based Image Retrieval?](https://mlsanity.com/paper/2403.07203/) (2024)
- [Text-to-Image Diffusion Models are Great Sketch-Photo Matchmakers](https://mlsanity.com/paper/2403.07214/) (2024)
- [Data-Free Sketch-Based Image Retrieval](https://mlsanity.com/paper/2303.07775/) (2023)
- [Sketch-based Medical Image Retrieval](https://mlsanity.com/paper/2303.03633/) (2023)
- [Cross-Modal Fusion Distillation for Fine-Grained Sketch-Based Image Retrieval](https://mlsanity.com/paper/2210.10486/) (2022)
- [What Sketch Explainability Really Means for Downstream Tasks](https://mlsanity.com/paper/2403.09480/) (2024)
- [SketchFusion: Learning Universal Sketch Features through Fusing Foundation Models](https://mlsanity.com/paper/2503.14129/) (2025)
- [Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities](https://mlsanity.com/paper/2508.01290/) (2025)
- [Beyond Domain Gap: Exploiting Subjectivity in Sketch-Based Person Retrieval](https://mlsanity.com/paper/2309.08372/) (2023)
- [Sketch Beautification: Learning Part Beautification and Structure Refinement for Sketches of Man-made Objects](https://mlsanity.com/paper/2306.05832/) (2023)
- [Freeview Sketching: View-Aware Fine-Grained Sketch-Based Image Retrieval](https://mlsanity.com/paper/2407.01810/) (2024)
- [Sketch Down the FLOPs: Towards Efficient Networks for Human Sketch](https://mlsanity.com/paper/2505.23763/) (2025)
- [Learning with Multi-modal Gradient Attention for Explainable Composed Image Retrieval](https://mlsanity.com/paper/2308.16649/) (2023)
- [Sketch-based Video Object Segmentation: Benchmark and Analysis](https://mlsanity.com/paper/2311.07261/) (2023)
- [Multimodal Query-guided Object Localization](https://mlsanity.com/paper/2212.00749/) (2022)
- [Symmetrical Bidirectional Knowledge Alignment for Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2312.10320/) (2023)
- [Interactive Drawing Guidance for Anime Illustrations with Diffusion Model](https://mlsanity.com/paper/2507.09140/) (2025)
- [Annotation-Free Human Sketch Quality Assessment](https://mlsanity.com/paper/2507.20548/) (2025)
- [Sketch and Text Synergy: Fusing Structural Contours and Descriptive Attributes for Fine-Grained Image Retrieval](https://mlsanity.com/paper/2604.15735/) (2026)

## Abstract (arXiv)

Sketching enables many exciting applications, notably, image retrieval. The fear-to-sketch problem (i.e., "I can't sketch") has however proven to be fatal for its widespread adoption. This paper tackles this "fear" head on, and for the first time, proposes an auxiliary module for existing retrieval models that predominantly lets the users sketch without having to worry. We first conducted a pilot study that revealed the secret lies in the existence of noisy strokes, but not so much of the "I can't sketch". We consequently design a stroke subset selector that {detects noisy strokes, leaving only those} which make a positive contribution towards successful retrieval. Our Reinforcement Learning based formulation quantifies the importance of each stroke present in a given subset, based on the extent to which that stroke contributes to retrieval. When combined with pre-trained retrieval models as a pre-processing module, we achieve a significant gain of 8%-10% over standard baselines and in turn report new state-of-the-art performance. Last but not least, we demonstrate the selector once trained, can also be used in a plug-and-play manner to empower various sketch applications in ways that were not previously possible.