mlsanityData to 14 Oct 2026

Deep Image Style Transfer from Freeform Text

arXiv 2212.06868 ↗First posted 13 Dec 2022cs.CV cs.CL cs.LGcited by 0

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Abstract (arXiv)

This paper creates a novel method of deep neural style transfer by generating style images from freeform user text input. The language model and style transfer model form a seamless pipeline that can create output images with similar losses and improved quality when compared to baseline style transfer methods. The language model returns a closely matching image given a style text and description input, which is then passed to the style transfer model with an input content image to create a final output. A proof-of-concept tool is also developed to integrate the models and demonstrate the effectiveness of deep image style transfer from freeform text.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 15 references that are in the map. First posted on arXiv 13 Dec 2022.

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9 of 15 references.

References · 9

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  4. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
  5. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,060
  6. 2015A Neural Algorithm of Artistic Stylecited by 483
  7. 2016Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesiscited by 102
  8. 2016Controlling Perceptual Factors in Neural Style Transfercited by 100
  9. 2018How to Read Paintings: Semantic Art Understanding with Multi-Modal Retrievalcited by 74

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