AIM 2020 Challenge on Image Extreme Inpainting
It sets a benchmark for future extreme image inpainting methods, addressing a domain-specific problem for researchers in computer vision.
The paper reviews the AIM 2020 challenge on extreme image inpainting, tackling the problem of inpainting large image regions with two tracks: classical inpainting using context only and semantically guided inpainting using segmentation maps, with 88 and 74 participants respectively and 11 and 6 teams in the final phase.
This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint considerably large part of the image using no supervision but the context. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the image to inpaint. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods.