%0 Book Section
%T ChaLearn Looking at People: Inpainting and Denoising Challenges
%A Sergio Escalera
%A Marti Soler
%A Stephane Ayache
%A Umut Guçlu
%A Jun Wan
%A Meysam Madadi
%A Xavier Baro
%A Hugo Jair Escalante
%A Isabelle Guyon
%B The Springer Series on Challenges in Machine Learning
%D 2019
%F Sergio Escalera2019
%O HuPBA; no proj
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3327), last updated on Tue, 11 Feb 2020 14:19:52 +0100
%X Dealing with incomplete information is a well studied problem in the context of machine learning and computational intelligence. However, in the context of computer vision, the problem has only been studied in specific scenarios (e.g., certain types of occlusions in specific types of images), although it is common to have incomplete information in visual data. This chapter describes the design of an academic competition focusing on inpainting of images and video sequences that was part of the competition program of WCCI2018 and had a satellite event collocated with ECCV2018. The ChaLearn Looking at People Inpainting Challenge aimed at advancing the state of the art on visual inpainting by promoting the development of methods for recovering missing and occluded information from images and video. Three tracks were proposed in which visual inpainting might be helpful but still challenging: human body pose estimation, text overlays removal and fingerprint denoising. This chapter describes the design of the challenge, which includes the release of three novel datasets, and the description of evaluation metrics, baselines and evaluation protocol. The results of the challenge are analyzed and discussed in detail and conclusions derived from this event are outlined.
%U https://link.springer.com/chapter/10.1007/978-3-030-25614-2_2
%P 23-44