mlsanityData to 7 Oct 2026

Multi-Modality Image Inpainting using Generative Adversarial Networks

arXiv 2206.09210 ↗First posted 18 Jun 2022eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

Deep learning techniques, especially Generative Adversarial Networks (GANs) have significantly improved image inpainting and image-to-image translation tasks over the past few years. To the best of our knowledge, the problem of combining the image inpainting task with the multi-modality image-to-image translation remains intact. In this paper, we propose a model to address this problem. The model will be evaluated on combined night-to-day image translation and inpainting, along with promising qualitative and quantitative results.

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References · 10

  1. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  2. 2020Encoding in Style: a StyleGAN Encoder for Image-to-Image Translationcited by 727
  3. 2020Contrastive Learning for Unpaired Image-to-Image Translationcited by 637
  4. 2020Contextual Residual Aggregation for Ultra High-Resolution Image Inpaintingcited by 124
  5. 2020Recurrent Feature Reasoning for Image Inpaintingcited by 107
  6. 2021Breaking the Dilemma of Medical Image-to-image Translationcited by 82
  7. 2020Rethinking the Truly Unsupervised Image-to-Image Translationcited by 56
  8. 2019SPA-GAN: Spatial Attention GAN for Image-to-Image Translationcited by 43
  9. 2020Learning Oracle Attention for High-fidelity Face Completioncited by 21
  10. 2018Semi-parametric Image Inpaintingcited by 6

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