Vitexco: Exemplar-based video colorization using vision transformer
Published in 2023 14th International Conference on Information and Communication Technology Convergence (ICTC), 2023
Abstract. In the field of image and video colorization, the existing research employs a CNN to extract information from each video frame. However, due to the local nature of a kernel, it is challenging for CNN to capture the relationships between each pixel and others in an image, leading to inaccurate colorization. To solve this issue, we introduce an end-to-end network called Vitexco for colorizing videos. Vitexco utilizes the power of the Vision Transformer (ViT) to capture the relationships among all pixels in a frame with each other, providing a more effective method for colorizing video frames. We evaluate our approach on DAVIS datasets and demonstrate that it outperforms the state-of-the-art methods regarding color accuracy and visual quality. Our findings suggest that using a ViT can significantly enhance the performance of video colorization.
Recommended citation: Duong Thanh Tran, Nguyen Doan Hieu Nguyen, Trung Thanh Pham, Phuong-Nam Tran, Thuy-Duong Thi Vu, Duc Ngoc Minh Dang. (2023). "Vitexco: Exemplar-based video colorization using vision transformer." 2023 14th International Conference on Information and Communication Technology Convergence (ICTC), 59-64.
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