# AIM 2020 Challenge on Image Extreme Inpainting

Source: [AIM 2020 Challenge on Image Extreme Inpainting](<https://www.sota2.com/research/paper/68fa686f1e209131cea1c2e6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:44.720Z

Authors: Evangelos Ntavelis, Andr\\'es Romero, Siavash Bigdeli, Radu Timofte

Published: 2020-10-02T17:11:17.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2010.01110>)

[PDF](<https://arxiv.org/pdf/2010.01110.pdf>)