Unpaired image-to-image translation aims to translate images from the source class to target one by providing sufficient data for these classes.
Therefore, high-quality correspondence matching is critical.
We also present a maintenance planning model using a risk-based decision-making method, which identifies the maintenance decision with minimal economic risk of the truck company.
We only participated in the first subtask, and a neural sequence model was used to perform the sequence tagging task.
Motivated by these findings, we propose a temporal multi-correspondence aggregation strategy to leverage similar patches across frames, and a cross-scale nonlocal-correspondence aggregation scheme to explore self-similarity of images across scales.
Blind inpainting is a task to automatically complete visual contents without specifying masks for missing areas in an image.
Conditional generative adversarial networks have shown exceptional generation performance over the past few years.
In this paper, we are interested in generating an cartoon face of a person by using unpaired training data between real faces and cartoon ones.
Current image translation methods, albeit effective to produce high-quality results in various applications, still do not consider much geometric transform.
In this paper, we propose a generative multi-column network for image inpainting.
In single image deblurring, the "coarse-to-fine" scheme, i. e. gradually restoring the sharp image on different resolutions in a pyramid, is very successful in both traditional optimization-based methods and recent neural-network-based approaches.
Ranked #2 on Deblurring on RealBlur-J
Estimating correspondence between two images and extracting the foreground object are two challenges in computer vision.
We propose a new direction for fast video super-resolution (VideoSR) via a SR draft ensemble, which is defined as the set of high-resolution patch candidates before final image deconvolution.