iColoriT: Towards Propagating Local Hint to the Right Region in Interactive Colorization by Leveraging Vision Transformer
Jooyeol Yun, Sanghyeon Lee, Minho Park, Jaegul Choo

TL;DR
iColoriT introduces a Vision Transformer-based method for point-interactive image colorization that effectively propagates user hints globally, achieving real-time performance and superior colorization quality compared to existing approaches.
Contribution
The paper proposes iColoriT, a novel Transformer-based model that enhances hint propagation in interactive colorization and introduces efficient upsampling with a local stabilizing layer.
Findings
Outperforms existing methods in accuracy and user effort
Achieves real-time colorization with pixel shuffling
Effectively propagates hints to relevant regions using self-attention
Abstract
Point-interactive image colorization aims to colorize grayscale images when a user provides the colors for specific locations. It is essential for point-interactive colorization methods to appropriately propagate user-provided colors (i.e., user hints) in the entire image to obtain a reasonably colorized image with minimal user effort. However, existing approaches often produce partially colorized results due to the inefficient design of stacking convolutional layers to propagate hints to distant relevant regions. To address this problem, we present iColoriT, a novel point-interactive colorization Vision Transformer capable of propagating user hints to relevant regions, leveraging the global receptive field of Transformers. The self-attention mechanism of Transformers enables iColoriT to selectively colorize relevant regions with only a few local hints. Our approach colorizes images in…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Image Enhancement Techniques · Advanced Vision and Imaging
MethodsLinear Layer · Absolute Position Encodings · Position-Wise Feed-Forward Layer · Residual Connection · Label Smoothing · Dropout · Byte Pair Encoding · Adam · Dense Connections · Softmax
