28.August Glasgow, UK

AIM 2020

Advances in Image Manipulation workshop

and challenges on image and video manipulation

in conjunction with ECCV 2020

Sponsors (TBD)

Call for papers

Image manipulation is a key computer vision tasks, aiming at the restoration of degraded image content, the filling in of missing information, or the needed transformation and/or manipulation to achieve a desired target (with respect to perceptual quality, contents, or performance of apps working on such images). Recent years have witnessed an increased interest from the vision and graphics communities in these fundamental topics of research. Not only has there been a constantly growing flow of related papers, but also substantial progress has been achieved.

Each step forward eases the use of images by people or computers for the fulfillment of further tasks, as image manipulation serves as an important frontend. Not surprisingly then, there is an ever growing range of applications in fields such as surveillance, the automotive industry, electronics, remote sensing, or medical image analysis etc. The emergence and ubiquitous use of mobile and wearable devices offer another fertile ground for additional applications and faster methods.

This workshop aims to provide an overview of the new trends and advances in those areas. Moreover, it will offer an opportunity for academic and industrial attendees to interact and explore collaborations.

This workshop builds upon the success of Advances in Image Manipulation (AIM) workshop at ICCV 2019, Perceptual Image Restoration and Manipulation (PIRM) workshop at ECCV 2018 , the workshop and Challenge on Learned Image Compression (CLIC) editions at CVPR 2018, CVPR 2019, CVPR 2020 and the New Trends in Image Restoration and Enhancement (NTIRE) editions: at CVPR 2017 , 2018, 2019 and 2020 and at ACCV 2016. Moreover, it relies on the people associated with the PIRM, CLIC, and NTIRE events such as organizers, PC members, distinguished speakers, authors of published papers, challenge participants and winning teams.

Papers addressing topics related to image/video manipulation, restoration and enhancement are invited. The topics include, but are not limited to:

  • Image-to-image translation
  • Video-to-video translation
  • Image/video manipulation
  • Perceptual manipulation
  • Image/video generation and hallucination
  • Image/video quality assessment
  • Image/video semantic segmentation
  • Perceptual enhancement
  • Multimodal translation
  • Depth estimation
  • Image/video inpainting
  • Image/video deblurring
  • Image/video denoising
  • Image/video upsampling and super-resolution
  • Image/video filtering
  • Image/video de-hazing, de-raining, de-snowing, etc.
  • Demosaicing
  • Image/video compression
  • Removal of artifacts, shadows, glare and reflections, etc.
  • Image/video enhancement: brightening, color adjustment, sharpening, etc.
  • Style transfer
  • Hyperspectral imaging
  • Underwater imaging
  • Aerial and satellite imaging
  • Methods robust to changing weather conditions / adverse outdoor conditions
  • Image/video manipulation on mobile devices
  • Image/video restoration and enhancement on mobile devices
  • Studies and applications of the above.

AIM 2020 has the following associated groups of challenges:

  • image challenges
  • video challenges

The authors of the top methods in each category will be invited to submit papers to AIM 2020 workshop.

The authors of the top methods will co-author the challenge reports.

The accepted AIM workshop papers will be published under the book title "ECCV Workshops" by

Computer Vision Foundation Open Access and IEEE Xplore Digital Library


Radu Timofte, radu.timofte@vision.ee.ethz.ch

Computer Vision Laboratory

ETH Zurich, Switzerland

AIM 2020 video challenges

Important dates

Challenges Event Date (always 5PM Pacific Time)
Site online February 15, 2020
Release of train data and validation data May 05, 2020
Validation server online May 15, 2020
Final test data release, validation server closed July 3, 2020
Test restoration results submission deadline July 10, 2020
Fact sheets submission deadline July 10, 2020
Code/executable submission deadline July 10, 2020
Preliminary test results release to the participants July 12, 2020
Paper submission deadline for entries from the challenges July 22, 2020
Workshop Event Date (always 5PM Pacific Time)
Paper submission server online May 08, 2020
Paper submission deadline July 10, 2020
Paper submission deadline (only for methods from AIM challenges or ECCV 2020 rejected papers!) July 22, 2020
Regular papers decision notification July 20, 2020
Camera ready deadline July 30, 2020
Workshop day August 28, 2020


Instructions and Policies
Format and paper length

A paper submission has to be in English, in pdf format, and at most 14 pages (excluding references) in single column. The paper format must follow the same guidelines as for all ECCV 2020 submissions.

Double-blind review policy

The review process is double blind. Authors do not know the names of the chair/reviewers of their papers. Reviewers do not know the names of the authors.

Dual submission policy

Dual submission is allowed with ECCV2020 main conference only. If a paper is submitted also to ECCV and accepted, the paper cannot be published both at the ECCV and the workshop.

Submission site (online!)



Accepted and presented papers will be published after the conference in ECCV Workshops proceedings together with the ECCV2020 main conference papers.

Author Kit

The author kit provides a LaTeX2e template for paper submissions. Please refer to the example eccv2020submission.pdf for detailed formatting instructions.



Radu Timofte

Radu Timofte is lecturer and group leader in the Computer Vision Laboratory, at ETH Zurich, Switzerland. He obtained a PhD degree in Electrical Engineering at the KU Leuven, Belgium in 2013, the MSc at the Univ. of Eastern Finland in 2007, and the Dipl. Eng. at the Technical Univ. of Iasi, Romania in 2006. He serves as a reviewer for top journals (such as TPAMI, TIP, IJCV, TNNLS, TCSVT, CVIU, PR) and conferences (ICCV, CVPR, ECCV, NeurIPS) and is associate editor for Elsevier CVIU journal and, starting 2020, for IEEE Trans. PAMI and for SIAM Journal on Imaging Sciences. He serves(d) as area chair (senior PC) for ACCV 2018, ICCV 2019, IJCAI 2019, IJCAI 2020, ECCV 2020, CVPR 2021, He received a NIPS 2017 best reviewer award. His work received the best student paper award at BMVC 2019, a best scientific paper award at ICPR 2012, the best paper award at CVVT workshop (ECCV 2012), the best paper award at ChaLearn LAP workshop (ICCV 2015), the best scientific poster award at EOS 2017, the honorable mention award at FG 2017, and his team won a number of challenges including traffic sign detection (IJCNN 2013) and apparent age estimation (ICCV 2015). He is co-founder of Merantix and co-organizer of NTIRE, CLIC, AIM and PIRM events. His current research interests include sparse and collaborative representations, deep learning, image/video compression, restoration and enhancement.

Wangmeng Zuo

Wangmeng Zuo is currently a Professor in the School of Computer Science and Technology, Harbin Institute of Technology. His research interests include image/video enhancement, image/video generation, visual tracking, and image classification. He has published over 90 papers in top-tier academic journals and conferences. He has served as area chairs of CVPR/ICCV, a Tutorial Organizer in ECCV 2016. He is also the co-authors of several promising deep image denoising models including DnCNN, FFDNet and CBDNet, and is interested in developing deep models for more general and practical image restoration tasks via a self-supervised learning manner.

Ruofan Zhou

Roey Mechrez

Roey Mechrez is the CTO and a Co-founder at BeyondMinds where he leads a group of 30 AI researchers and scientists. Prior to joining BeyondMinds, he did his PhD at the Technion-Israel, where he worked with Prof. Lihi Zelnik-Manor. His work lies in the intersection of, computer vision, and deep learning. Specifically, his research interests are in realistic image generation, manipulation and transformation, focussing on tools, algorithms, and new paradigms for photo editing and synthesis. During his PhD research, he has worked on Saliency Manipulation, Image Generation, Super-resolution, Photorealistic synthesis, and Template Matching.

Shuhang Gu

Shuhang Gu received the B.E. degree from the School of Astronautics, Beijing University of Aeronautics and Astronautics, China, in 2010, the M.E. degree from the Institute of Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, China, in 2013, and Ph.D. degree from the Department of Computing, The Hong Kong Polytechnic University, in 2017. He currently holds an assitant professor position at University of Sydney. Before that he was post-doc in Computer Vision Lab, at ETH Zurich, Switzerland. His research interests include image restoration, enhancement and compression.

Ming-Hsuan Yang

Ming-Hsuan Yang received the PhD degree in Computer Science from University of Illinois at Urbana-Champaign. He is a full professor in Electrical Engineering and Computer Science at University of California at Merced. He has published more than 120 papers in the field of computer vision. Yang serves as a program co-chair of ACCV 2014, general co-chair of ACCV 2016, and program co-chair of ICCV 2019. He serves as an editor for PAMI, IJCV, CVIU, IVC and JAIR. His research interests include object detection, tracking, recognition, image deblurring, super resolution, saliency detection, and image/video segmentation.

Luc Van Gool

Luc Van Gool received a degree in electro-mechanical engineering at the Katholieke Universiteit Leuven in 1981. Currently, he is a full professor for Computer Vision at the ETH in Zurich and the Katholieke Universiteit Leuven in Belgium. He leads research and teaches at both places. He has authored over 300 papers. Luc Van Gool has been a program committee member of several, major computer vision conferences (e.g. Program Chair ICCV'05, Beijing, General Chair of ICCV'11, Barcelona, and of ECCV'14, Zurich). His main interests include 3D reconstruction and modeling, object recognition, and tracking and gesture analysis. He received several Best Paper awards (eg. David Marr Prize '98, Best Paper CVPR'07, Tsuji Outstanding Paper Award ACCV'09, Best Vision Paper ICRA'09). In 2015 he received the 5-yearly Excellence Award in Applied Sciences by the Flemish Fund for Scientific Research, in 2016 a Koenderink Prize and in 2017 a PAMI Distinguished Researcher award. He is a co-founder of more than 10 spin-off companies and was the holder of an ERC Advanced Grant (VarCity). Currently, he leads computer vision research for autonomous driving in the context of the Toyota TRACE labs in Leuven and at ETH, as well as image and video enhancement research for Huawei.

Eli Shechtman

Eli Shechtman is a Principal Scientist at the Creative Intelligence Lab at Adobe Research. He received the B.Sc. degree in Electrical Engineering (magna cum laude) from Tel-Aviv University in 1996. Between 2001 and 2007 he attended the Weizmann Institute of Science where he received with honors his M.Sc. and Ph.D. degrees in Applied Mathematics and Computer Science. In 2007 he joined Adobe and started sharing his time as a post-doc with the University of Washington in Seattle. He published over 60 academic publications and holds over 20 issued patents. He served as a Technical Paper Committee member at SIGGRAPH 2013 and 2014, as an Area Chair at CVPR'15, ICCV'15 and CVPR'17 and serves an Associate Editor at TPAMI. He received several honors and awards, including the Best Paper prize at ECCV 2002, a Best Poster Award at CVPR 2004, a Best Reviewer Award at ECCV 2014 and published two Research Highlights papers in the Communication of the ACM journal.

Zhiwu Huang

Zhiwu Huang is currently a postdoctoral researcher in the Computer Vision Lab, ETH Zurich, Switzerland. He received the PhD degree from Institute of Computing Technology, Chinese Academy of Sciences in 2015. His main research interest is in human-focussed video analysis with Riemannian manifold networks and Wasserstein generative models.

Martin Danelljan

Martin Danelljan received his Ph.D. degree from Linköping University, Sweden in 2018. He is currently a postdoctoral researcher at ETH Zurich, Switzerland. His main research interests are online machine learning methods for visual tracking and video object segmentation, probabilistic models for point cloud registration, and machine learning with no or limited supervision. His research in the field of visual tracking in particular has attracted much attention. In 2014, he won the Visual Object Tracking (VOT) Challenge and the OpenCV State-ofthe-Art Vision Challenge. Furthermore, he achieved top ranks in VOT2016 and VOT2017 challenges. He received the best paper award in the computer vision track in ICPR 2016.

Ming-Yu Liu

Ming-Yu Liu is a principal research scientist at NVIDIA Research. Before joining NVIDIA in 2016, he was a principal research scientist at Mitsubishi Electric Research Labs (MERL). He received his Ph.D. from the Department of Electrical and Computer Engineering at the University of Maryland College Park in 2012. His object pose estimation system was awarded one of hundred most innovative technology products by the R&D magazine in 2014. His street scene understanding paper was selected in the best paper finalist in the 2015 Robotics Science and System (RSS) conference. In CVPR 2018, he won the 1st place in both the Domain Adaptation for Semantic Segmentation Competition in the WAD challenge and the Optical Flow Competition in the Robust Vision Challenge. His research focus is on generative models for image generation and understanding. His goal is to enable machines superhuman-like imagination capabilities.

Andrey Ignatov

Andrey Ignatov is a PhD student at ETH Zurich supervised by Prof. Luc Van Gool and Dr. Radu Timofte. He obtained his master degree from ETH Zurich in 2017. His current research interests include computational imaging, deep learning, wearable devices, and benchmarking.

Invited Talks (TBA)

Schedule (TBA)

The accepted AIM workshop papers will be published under the book title "ECCV Workshops" by

Computer Vision Foundation Open Access and IEEE Xplore Digital Library

AIM 2020 Awards

Best Paper Awards
Challenge Winners
Challenge Awards