AIM 2020 Challenge on Image Extreme Inpainting

TL;DR

This paper reviews the AIM 2020 challenge on extreme image inpainting, highlighting solutions, results, and establishing benchmarks for classical and semantically guided inpainting methods.

Contribution

It provides a comprehensive overview of challenge solutions and sets new benchmarks for extreme image inpainting techniques.

Findings

High participation in both tracks

Effective solutions developed for large-scale inpainting

Benchmark results for future research

Abstract

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.