SwinTExCo: Exemplar-based video colorization using Swin Transformer
Published in Expert Systems with Applications, 2025 IF 7.5
Abstract (excerpt). Video colorization represents a compelling domain within the field of Computer Vision. The traditional approach in this field relies on Convolutional Neural Networks (CNNs) to extract features from each video frame and employs a recurrent network to learn information between video frames. While demonstrating considerable success in colorization, most traditional CNNs suffer from a limited receptive field size, capturing local information within a fixed-sized window. Consequently, they struggle to directly grasp long-range dependencies or pixel relationships that span large image or video frame areas. To address this limitation, recent advancements in the field have leveraged Vision Transformer (ViT) and their variants to enhance performance. This article introduces Swin Transformer Exemplar-based Video Colorization (SwinTExCo), an end-to-end model for the video colorization process that incorporates the …
Recommended citation: Duong Thanh Tran, Nguyen Doan Hieu Nguyen, Trung Thanh Pham, Phuong-Nam Tran, Thuy-Duong Thi Vu, Cuong Tuan Nguyen, Hanh Dang-Ngoc, Duc Ngoc Minh Dang. (2025). "SwinTExCo: Exemplar-based video colorization using Swin Transformer." Expert Systems with Applications, 260, 125437.
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