Image and Visual Representation Lab

Welcome to IVRL

IVRL Logo

 

The Image and Visual Representation Lab (IVRL) performs research that is primarily focused on the capture, analysis, and reproduction of color images. Aiming to improve everyone’s photographic experience, we develop algorithms and systems that help us understand, process, and measure images.

Our research areas are computational photography, color image processing, computer vision, and image quality.

News:

© 2022 EPFL

Best Technical Paper award at SIGGRAPH 2022

— The paper "CLIPasso: Semantically-Aware Object Sketching" by Yael Vinker, Ehsan Pajouheshgar, Jessica Y. Bo, Roman Bachmann, Amit Haim Bermano, Daniel Cohen-Or, Amir Zamir and Ariel Shamir received the Best Technical Paper award at SIGGRAPH 2022. Ehsan is a PhD student of IVRL since 2021. Our work converts an image of an object to a sketch, allowing for varying levels of abstraction, while preserving its key visual features. Even with a very minimal representation (the rightmost flamingo and horse are drawn with only a few strokes), one can recognize both the semantics and the structure of the subject depicted.

The Largo.ai team at Cannes ©  Largo.ai / EPFL 2022

“It's an amazing feeling to be at the 75th Cannes Film Festival!”

— EPFL spin-off Largo.ai ran a unique AI-powered pitch session in Cannes in collaboration with the festival, to deliver content evaluation, casting recommendations, box office predictions and streaming platform results for upcoming, unproduced projects.

Peter Grönquist and Yufan Ren in Singapore © AI Singapore / EPFL 2022

“Deepfake generation and detection is like an arms race”

— Two EPFL computer scientists have taken home a major prize in Singapore’s Trusted Media Challenge, a five-month long competition aimed at cracking the code of deepfakes.

© 2022 CVPR

Our paper on self supervised learning (SSL) accepted to CVPR 2022

— The paper "Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy" by Tong Zhang, Congpei Qiu, Wei Ke, Sabine Süsstrunk, Mathieu Salzmann is accepted to CVPR2022.

© 2022 CVPR

Our paper on multitask transformer will appear in CVPR 2022

— The paper "MulT: An End-to-End Multitask Learning Transformer" by Deblina Bhattacharjee, Tong Zhang, Sabine Süsstrunk and Mathieu Salzmann is accepted to CVPR2022.

© 2022 EPFL

BIGPrior is published in IEEE Transactions on Image Processing

— BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration by Majed El Helou and Sabine Süsstrunk published in IEEE Transactions on Image Processing.

© 2022 WACV

Estimating Image Depth in the Comics Domain

— Estimating Image Depth in the Comics Domain by Deblina Bhattacharjee, Martin Everaert, Mathieu Salzmann, Sabine Süsstrunk accepted into Winter Conference on Applications of Computer Vision (WACV 2022). 

© 2022 IEEE

IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

— Training Provably Robust Models by Polyhedral Envelope Regularization by Chen Liu, Mathieu Salzmann and Sabine Süsstrunk published in IEEE Transactions on Neural Networks and Learning Systems (TNNLS).

© Olivier Stucky, 2021

Transfer, Reconfiguration and Digital Transition in Graphic Narrative

— Bahar Aydemir and Raphaël Baroni presented their work "Beyond the Shadow of the Z: How We Read European Bande Dessinée" in The international conference “Transfer, Reconfiguration and Digital Transition in Graphic Narratives”.

© 2021 EPFL

Video compression patent and licensing

— We released an online demo of our extreme video completion method. Our technology is very energy and time-efficient on the encoder side (we require practically 0 computation and no specialized hardware like typical DCT-based encoders), it can reach extreme compression rates with complete rate flexibility and can achieve better visual reconstruction at these rates compared with the MPEG-4 H264 standard. This makes it ideal for instance for video-transmitting IoT devices, surveillance cameras, drone transmission, or emergency deployment transmission where bandwidth is very limited. Users can visualize different results and test with their own videos too on: adefan.epfl.ch For the more technically curious readers, we have an associated publication in the leading signal processing conference IEEE ICASSP that can be accessed here: https://infoscience.epfl.ch/record/277003/

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