This paper reviews the first-ever image demoireing challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ICCV 2019. This paper describes the challenge, and focuses on the proposed solutions and their results. Demoireing is a difficult task of removing moire patterns from an image to reveal an underlying clean image. A new dataset, called LCDMoire was created for this challenge, and consists of 10,200 synthetically generated image pairs (moire and clean ground truth). The challenge was divided into 2 tracks. Track 1 targeted fidelity, measuring the ability of demoire methods to obtain a moire-free image compared with the ground truth, while Track 2 examined the perceptual quality of demoire methods. The tracks had 60 and 39 registered participants, respectively. A total of eight teams competed in the final testing phase. The entries span the current the state-of-the-art in the image demoireing problem.
AIM 2022 Challenge on Instagram Filter Removal: Methods and Results
Özyeğin University（厄兹耶因大学）；University of Würzburg（维尔茨堡大学）；Xidian University（西安电子科技大学）；Chinese Academy of Sciences（中国科学院）；MAICRO；AiRiA；Xiaomi Inc.（小米公司）；Sahmyook University（三育大学）；FS Solution；Sejong University（世宗大学）；Chung-Ang University（中央大学）；Couger Inc.（Couger公司）
EMOPAIN Challenge 2020: Multimodal Pain Evaluation from Facial and Bodily Expressions
School of Computer Science, The University of Nottingham（诺丁汉大学计算机学院）；University College London（伦敦大学学院）；Department of Computer Science, University of East Anglia（东英吉利大学计算机科学系）；Department of Electronic and Computer Engineering, Brunel University London（布鲁内尔大学伦敦电子与计算机工程系）；Department of Computer Science, University of Oxford（牛津大学计算机科学系）
SkelNetOn 2019: Dataset and Challenge on Deep Learning for Geometric Shape Understanding
DeepScale（DeepScale公司）；Bergische Universität Wuppertal（伍珀塔尔大学）；Occidental College（西方学院）；University of Toulouse（图卢兹大学）；University of Basel（巴塞尔大学）；Dartmouth College（达特茅斯学院）；Université Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Princeton University（普林斯顿大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
POLY-SIM: Polyglot Speaker Identification with Missing Modality Grand Challenge 2026 Evaluation Plan
Institute of Computational Perception, Johannes Kepler University Linz, Austria（林茨约翰·开普勒大学计算感知研究所）；University of Michigan-Flint, USA（密歇根大学弗林特分校）；Sapienza University of Rome, Italy（罗马大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Fortemedia Singapore, Singapore（新加坡Fortemedia公司）；IT:U Interdisciplinary Transformation University Austria（奥地利跨学科转型大学）；University of Augsburg, Germany（奥格斯堡大学）；Human-centered AI Group, AI Lab, Linz Institute of Technology, Austria（奥地利林茨理工学院人工智能实验室人本人工智能组）
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
各种深度估计模型现在广泛用于许多移动和IoT设备上，用于图像分割、散景效果渲染、对象跟踪和许多其他移动任务。因此，具有可以在低功率移动芯片组上快速运行的高效且准确的深度估计模型是非常关键的。在这项移动人工智能挑战赛中，目标是开发基于深度学习的单图像深度估计解决方案，该解决方案可以在物联网平台和智能手机上显示实时性能。为此，参与者使用了一个大规模的RGB到深度数据集，该数据集是由ZED立体相机收集的，能够为位于50米以内的物体生成深度图。所有模型的运行时都在Raspberry Pi 4平台上进行了评估，其中开发的解决方案能够以高达27 FPS的速度生成VGA分辨率的深度图，同时实现高保真度的结果。在挑战中开发的所有模型也与任何基于Android或Linux的移动设备兼容，本文提供了它们的详细描述。
AIM 2024 Challenge on Video Super-Resolution Quality Assessment: Methods and Results
Lomonosov Moscow State University（莫斯科罗蒙诺索夫国立大学）；MSU Institute for Artificial Intelligence（莫斯科国立大学人工智能研究所）；University of Würzburg（维尔茨堡大学）；ByteDance（字节跳动）；Tencent（腾讯）；School of Computer Science and Cyber Engineering, Guangzhou University（广州大学计算机科学与网络工程学院）；Shanghai Jiao Tong University（上海交通大学）
