RetCompletion:High-Speed Inference Image Completion with Retentive Network

Αποθηκεύτηκε σε:

Λεπτομέρειες βιβλιογραφικής εγγραφής
Εκδόθηκε σε:	arXiv.org (Dec 4, 2024), p. n/a
Κύριος συγγραφέας:	Cang, Yueyang
Άλλοι συγγραφείς:	Hu, Pingge, Zhang, Xiaoteng, Wang, Xingtong, Liu, Yuhang, Shi, Li
Έκδοση:	Cornell University Library, arXiv.org
Θέματα:	Image restoration Data integration Computer vision Image resolution Image quality Image reconstruction Image enhancement Natural language processing Inference
Διαθέσιμο Online:	Citation/Abstract Full text outside of ProQuest
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Περιγραφή
Περίληψη:	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.
ISSN:	2331-8422
Πηγή:	Engineering Database