RetCompletion:High-Speed Inference Image Completion with Retentive Network
<|reference_start|>RetCompletion:High-Speed Inference Image Completion with Retentive Network: Time cost is a major challenge in achieving high-quality pluralistic image completion. Recently, the Retentive Network (RetNet) in natural language processing offers a novel approach to this problem with its low-cost inference capabilities. Inspired by this, we apply RetNet to the pluralistic image completion task in computer vision. We present RetCompletion, a two-stage framework. In the first stage, we introduce Bi-RetNet, a bidirectional sequence information fusion model that integrates contextual information from images. During inference, we employ a unidirectional pixel-wise update strategy to restore consistent image structures, achieving both high reconstruction quality and fast inference speed. In the second stage, we use a CNN for low-resolution upsampling to enhance texture details. Experiments on ImageNet and CelebA-HQ demonstrate that our inference speed is 10$\times$ faster than ICT and 15$\times$ faster than RePaint. The proposed RetCompletion significantly improves inference speed and delivers strong performance, especially when masks cover large areas of the image.<|reference_end|>
@article{cang2024retcompletion:high-speed,
    title={RetCompletion:High-Speed Inference Image Completion with Retentive
  Network},
    author={Yueyang Cang, Pingge Hu, Xiaoteng Zhang, Xingtong Wang, Yuhang Liu},
    journal={arXiv preprint arXiv:2410.04056},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04056},
    primaryClass={cs.CV}
}