mlsanityData to 14 Oct 2026

Towards Practicality of Sketch-Based Visual Understanding

arXiv 2210.15146 ↗First posted 27 Oct 2022cs.CVcited by 1

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Abstract (arXiv)

Sketches have been used to conceptualise and depict visual objects from pre-historic times. Sketch research has flourished in the past decade, particularly with the proliferation of touchscreen devices. Much of the utilisation of sketch has been anchored around the fact that it can be used to delineate visual concepts universally irrespective of age, race, language, or demography. The fine-grained interactive nature of sketches facilitates the application of sketches to various visual understanding tasks, like image retrieval, image-generation or editing, segmentation, 3D-shape modelling etc. However, sketches are highly abstract and subjective based on the perception of individuals. Although most agree that sketches provide fine-grained control to the user to depict a visual object, many consider sketching a tedious process due to their limited sketching skills compared to other query/support modalities like text/tags. Furthermore, collecting fine-grained sketch-photo association is a significant bottleneck to commercialising sketch applications. Therefore, this thesis aims to progress sketch-based visual understanding towards more practicality.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 85 of its 210 references that are in the map. First posted on arXiv 27 Oct 2022.

Builds on

85 of 210 references.

References · 85

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2017Proximal Policy Optimization Algorithmscited by 15,215
  4. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  5. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  6. 2016Layer Normalizationcited by 5,669
  7. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  8. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  9. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  10. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  11. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,591
  12. 2016Context Encoders: Feature Learning by Inpaintingcited by 1,428
38 more
  1. 2016Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzlescited by 1,382
  2. 2019Contrastive Multiview Codingcited by 1,301
  3. 2020Gradient Surgery for Multi-Task Learningcited by 1,256
  4. 2016Colorful Image Colorizationcited by 1,114
  5. 2015Unsupervised Visual Representation Learning by Context Predictioncited by 1,066
  6. 2018Deep Clustering for Unsupervised Learning of Visual Featurescited by 1,028
  7. 2019Self-supervised Visual Feature Learning with Deep Neural Networks: A Surveycited by 657
  8. 2020Prototypical Contrastive Learning of Unsupervised Representationscited by 640
  9. 2020Learning from Noisy Labels with Deep Neural Networks: A Surveycited by 582
  10. 2018Generative Image Inpainting with Contextual Attentioncited by 580
  11. 2019Text Summarization with Pretrained Encoderscited by 556
  12. 2016Self-critical Sequence Training for Image Captioningcited by 547
  13. 2019Generalizing from a Few Examples: A Survey on Few-Shot Learningcited by 463
  14. 2018Dynamic Few-Shot Visual Learning without Forgettingcited by 385
  15. 2017A Neural Representation of Sketch Drawingscited by 313
  16. 2018Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigationcited by 305
  17. 2019Revisiting Self-Supervised Visual Representation Learningcited by 251
  18. 2020Few-Shot Class-Incremental Learningcited by 251
  19. 2019Billion-scale semi-supervised learning for image classificationcited by 232
  20. 2021Few-Shot Incremental Learning with Continually Evolved Classifierscited by 187
  21. 2021Im2Vec: Synthesizing Vector Graphics without Vector Supervisioncited by 116
  22. 2021Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learningcited by 115
  23. 2019CAMP: Cross-Modal Adaptive Message Passing for Text-Image Retrievalcited by 110
  24. 2017Incremental Learning Through Deep Adaptationcited by 108
  25. 2019SCOPS: Self-Supervised Co-Part Segmentationcited by 100
  26. 2019Self-Supervised Learning of 3D Human Pose using Multi-view Geometrycited by 98
  27. 2018SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesiscited by 81
  28. 2018A Zero-Shot Framework for Sketch-based Image Retrievalcited by 71
  29. 2020Incremental Learning In Online Scenariocited by 71
  30. 2017Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrievalcited by 68
  31. 2019Photo-Sketching: Inferring Contour Drawings from Imagescited by 68
  32. 2019Multimodal Self-Supervised Learning for Medical Image Analysiscited by 57
  33. 2017Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Modelscited by 53
  34. 2021StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrievalcited by 50
  35. 2022FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Contextcited by 49
  36. 2018Zero-Shot Sketch-Image Hashingcited by 43
  37. 2020Sketch-BERT: Learning Sketch Bidirectional Encoder Representation from Transformers by Self-supervised Learning of Sketch Gestaltcited by 42
  38. 2019Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrievalcited by 40

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  1. 2025Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detectioncited by 0