mlsanityData to 7 Oct 2026

A Creative Industry Image Generation Dataset Based on Captions

arXiv 2211.09035 ↗First posted 16 Nov 2022cs.CVcited by 1

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Abstract (arXiv)

Most image generation methods are difficult to precisely control the properties of the generated images, such as structure, scale, shape, etc., which limits its large-scale application in creative industries such as conceptual design and graphic design, and so on. Using the prompt and the sketch is a practical solution for controllability. Existing datasets lack either prompt or sketch and are not designed for the creative industry. Here is the main contribution of our work. a) This is the first dataset that covers the 4 most important areas of creative industry domains and is labeled with prompt and sketch. b) We provide multiple reference images in the test set and fine-grained scores for each reference which are useful for measurement. c) We apply two state-of-the-art models to our dataset and then find some shortcomings, such as the prompt is more highly valued than the sketch.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 28 of its 51 references that are in the map. First posted on arXiv 16 Nov 2022.

Builds on

28 of 51 references.

References · 28

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  4. 2022Generative Adversarial Networkscited by 5,603
  5. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  6. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  7. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  8. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
  9. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  10. 2016Improved Techniques for Training GANscited by 2,938
  11. 2021LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairscited by 1,544
  12. 2021Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Conceptscited by 1,209
16 more
  1. 2017High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANscited by 1,176
  2. 2022Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priorscited by 487
  3. 2021CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoderscited by 186
  4. 2018SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesiscited by 81
  5. 2021Multimodal Conditional Image Synthesis with Product-of-Experts GANscited by 75
  6. 2019Photo-Sketching: Inferring Contour Drawings from Imagescited by 67
  7. 2020Artificial Intelligence in the Creative Industries: A Reviewcited by 61
  8. 2019Interactive Sketch & Fill: Multiclass Sketch-to-Image Translationcited by 51
  9. 2018Learning Deep Sketch Abstractioncited by 36
  10. 2021M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis via Non-Autoregressive Generative Transformerscited by 36
  11. 2018SketchyScene: Richly-Annotated Scene Sketchescited by 32
  12. 2022StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Translationcited by 28
  13. 2015Free-hand Sketch Synthesis with Deformable Stroke Modelscited by 16
  14. 2016CIFAR-10: KNN-based Ensemble of Classifierscited by 16
  15. 2020Synthesizing human-like sketches from natural images using a conditional convolutional decodercited by 13
  16. 2020Sketch-to-Art: Synthesizing Stylized Art Images From Sketchescited by 10

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Citing papers per year: 2023: 1.

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  1. 2023Survey on Controlable Image Synthesis with Deep Learningcited by 0