Best Paper Awards

Jeremy Klotz, Shree Nayar

Minimalist Vision with Freeform Pixels

Best Paper Awards – Honorable Mention

Vitali Petsiuk, Kate Saenko

Concept Arithmetics for Circumventing Concept Inhibition in Diffusion Models

Stanislav Pidhorskyi, Tomas Simon, Gabriel Schwartz, He Wen, Yaser Sheikh, Jason Saragih

Rasterized Edge Gradients: Handling Discontinuities Differentially

PAMI Everingham Prize

The CelebA Team
Ziwei Liu, Ping Luo, Xiaogang Wang, Xiaoou Tang

for a family of datasets that has accelerated the progress in generative image modeling and many other tasks

David Forsyth

for continual advice and wisdom in overseeing the computer vision community's conferences and journals

Koenderink Prize

Jakob Engel, Thomas Schöps, Daniel Cremers

LSD-SLAM: Large-Scale Direct Monocular SLAM

Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, C. Lawrence Zitnick

Microsoft COCO: Common Objects in Context

Best Paper Awards

Xingjian Zhen, Zihang Meng, Rudrasis Chakraborty, Vikas Singh

On the Versatile Uses of Partial Distance Correlation in Deep Learning

Best Paper Awards – Honorable Mention

Ishit Mehta, Manmohan Chandraker, Ravi Ramamoorthi

A Level Set Theory for Neural Implicit Evolution under Explicit Flows

Garvita Tiwari, Dimitrije Antic, Jan E. Lenssen, Nikolaos Sarafianos, Tony Tung, Gerard Pons-Moll

Pose-NDF: Modelling Human Pose Manifolds with Neural Distance Fields

Everingham Prize

Walter J. Scheirer

Outstanding long-term service to the computer vision community

Khurram Soomro, Amir Roshan Zamir and Mubarak Shah, Hilde Kuehne, Hueihan Jhuang, Estibaliz Garrote, Tomaso A. Poggio, Thomas Serre

The UCF101 and HMD51 dataset teams

Koenderink Prize

Daniel J. Butler, Jonas Wulff, Garrett B. Stanley, Michael J. Black

A naturalistic open source movie for optical flow evaluation

Nathan Silberman, Derek Hoiem, Pushmeet Kohli, Rob Fergus

Indoor Segmentation and Support Inference from RGBD Images

Best Demo

Yair Moshe, Dan-Ilan Ben-David, Eran Mann, Sagie Baboach, Iddo Bar-Haim, Shoval Gerbi, Adam Katav, Technion – Israel Institute of Technology, Israel

Using a Smartphone for Augmented Reality in a Classroom

Best Paper Awards

Zachary Teed, Jia Deng

RAFT: Recurrent All-Pairs Field Transforms for Optical Flow

Best Paper Awards – Honorable Mention

Mengtian Li, Yu-Xiong Wang, Deva Ramanan

Towards Streaming Perception

Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

Everingham Prize

Antonio Torralba

Multiple Datasets

Johannes Schönberger

COLMAP SFM and MVS software library

Koenderink Prize

Florent Perronnin, Jorge Sánchez and Thomas Mensink

Improving the Fisher Kernel for Large-Scale Image Classification

Michael Calonder, Vincent Lepetit, Christoph Strecha, and Pascal Fua

Brief: Binary robust independent elementary features

Best Demo

Rita Cucchiara, Matteo Fabbri, and Simone Calderara, University of Modena and Reggio Emilia, Italy

Inter-Homines

Best Paper Awards

Martin Sundermeyer, Zoltan Marton, Maximilian Durner, Manuel Brucker, Rudolph Triebel

Implicit 3D Orientation Learning for 6D Object Detection from RGB Images

Best Paper Awards – Honorable Mention

Yuxin Wu, Kaiming He

Group Normalization

Albert Pumarola, Antonio Agudo, Aleix M. Martinez, Alberto Sanfeliu, Francesco Moreno-Noguer

GANimation: Anatomically-aware Facial Animation from a Single Image

Everingham Prize

Alan Smeaton, Wessel Kraaij, Paul Over, George Awad

For a series of datasets and workshops since 2003 that have driven progress in large scale Video Retrieval.

Changchang Wu

For providing a well documented software library for Structure from Motion that has been used effortlessly by so many.

Koenderink Prize

Herve Jegou, Matthijs Douze, Cordelia Schmid

Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search

Helmut Grabner, Christian Leistner, Horst Bischof

Semi-supervised On-Line Boosting for Robust Tracking

Best Paper Awards

Hanme Kim, Stefan Leutenegger, and Andrew J. Davison

Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera

Best Paper Awards – Honorable Mention

Jonathan Barron and Ben Poole

The Fast Bilateral Solver

Everingham Prize

Alex Berg, Jia Deng, Fei-Fei Li, Wei, Liu, Olga Russakovsky and team – ImageNet

For a series of datasets and challenges since 2010 that have had such impact on the computer vision field. ImageNet built on the Caltech101/256 datasets, increasing the number of images by orders of magnitude and enabling the development of new algorithms.

Ramin Zabih

For extensive, generous, service to the community: As long-term head of the IEEE PAMI Technical Committee he introduced many reforms, including to the awards process and the relationship to the IEEE. And he has been the driving force in creating and running the Computer Vision Foundation (CVF).

Koenderink Prize

Herbert Bay, Tinne Tuytelaars, and Luc Van Gool

Surf: Speeded up robust features (ECCV 2006)

Edward Rosten and Tom Drummond

Machine learning for high-speed corner detection (ECCV 2006)

Best Student Paper Award

Emma Alexander, Qi Guo, Sanjeev Koppal, Steven Gortler, and Todd Zickler

Focal Flow: Measuring Distance and Velocity with Defocus and Differential Motion

Best Paper Awards

Kevin Matzen and Noah Snavely

Scene Chronology

Jia Deng, Nan Ding, Yangqing Jia, Andrea Frome, Kevin Murphy, Samy Bengio, Yuan Li, Hartmut Neven, Hartwig Adam

Large-Scale Object Classification using Label Relation Graphs

Best Paper Awards – Honorable Mention

Matt Zeiler and Rob Fergus

Visualizing and Understanding Convolutional Neural Networks

PAMI Everingham Prize

Terry and Ginger Boult

For extensive, generous, long-term service to the community in the management of computer vision conferences and workshops.

Koenderink Prize

Thomas Brox, Andrès Bruhn, Nils Papenberg & Joachim Weickert (ECCV 2004)

High Accuracy Optical Flow Estimation Based on a Theory for Warping

Timo Ahonen, Abdenour Hadid & Matti Pietikäinen (ECCV 2004)

Face Recognition with Local Binary Patterns

Best Paper Award

Daniel Kuettel, Matthieu Guillaumin and Vittorio Ferrari

Segmentation Propagation in ImageNet

Best Paper Award – Honorable Mention

Kris Kitani, Brian D. Ziebart, James Bagnell and Martial Hebert

Activity Forecasting

Koenderink Prize

Vladimir Kolmogorov and Ramin Zabih

What Energy Functions Can Be Minimized via Graph Cuts?

Best Student Paper Award

Jianxiong Xiao and Yasutaka Furukawa

Reconstructing the World’s Museums

Best Paper Award

L. Laticky, C. Russell, P. Kohli and P.H.S. Torr

Graph Cut based Inference with Co-occurrence Statistics

Runner-Up Paper Award

A. Gupta, A. Efros and M. Hebert

Blocks World Revisited: Image Understanding Using Qualitative Geometry and Mechanics

Koenderink Prize

H. Sidenbladh, M.J. Black and D.J. Fleet

Stochastic Tracking of 3D Human Figures Using 2D Image Motion (ECCV 2000)

M. Weber, M. Welling and P. Perona

For the ECCV’2000 paper entitled: Unsupervised Learning of Models for Recognition

Best Student Paper Award

T. Pätz and T. Preusser

Ambrosio-Tortorelli Segmentation of Stochastic Images

Best Paper Award

Geremy Heitz and Daphne Koller

Learning Spatial Context: Using Stuff to Find Things

Koenderink Prize

Michael Isard and Andrew Blake

Contour Tracking by Stochastic Propagation of Conditional Density (ECCV 1996)

Olivier Faugeras, Quang-Tuan Luong, and Steve Maybank

Camera self-calibration: theory and experiments (ECCV 1992)

Best Student Paper Award

Matthew B. Blaschko and Christoph H. Lampert

Learning to Localize Objects with Structured Output Regression

Longuet-Higgins Best Paper Award

Anat Levin, Yair Weiss

Learning to Combine Bottom-up and Top-down Segmentation

Best Paper Award – Honorable Mention

Samuel W. Hasinoff, Kiriakos N. Kutulakos

Confocal Stereo

Omar Ait-Aider, Nicolas Andreff, Jean Marc Lavest, and Philippe Martinet

Simultaneous Object Pose and Velocity Computation Using a Single View from a Rolling Shutter Camera

Longuet-Higgins Best Paper Award

T. Brox, A. Bruhn, N. Papenberg, and J. Weickert

High Accuracy Optical Flow Estimation Based on a Theory for Warping

Best Paper Award – Honorable Mention

René Vidal and Yi Ma

A Unified Algebraic Approach to 2-D and 3-D Motion Segmentation

Best Paper – Cognitive Vision

Kenji Okuma, Ali Taleghani, Nando de Freitas, James J. Little, and David G. Lowe

A Boosted Particle Filter: Multitarget Detection and Tracking

With the Young Researcher Award, ECVA recognizes and encourages outstanding research achievements of young researchers in computer vision. Each awardee will receive Euro 5000 prize money. There will be one award every year, which will be awarded at the next ECCV conference.

Deadline

The deadline for nominating a candidate for the years 2024 and 2025 is:
April 30, 2026 (End of Day, Anywhere on Earth)

Eligibility

Young researchers must be based at a European research institution and should not be older than 35. Their main scope of research should be in computer vision or be strongly linked to computer vision.

Selection Criteria

Nominations will be reviewed by a selection committee for the quality and significance of the research contributions of the young researcher. Emphasis will be on the research after their PhD.

How to Nominate

The nomination (self-nominations are not allowed) must include:

• A short summary (approx. 2 pages) of the main research achievements
• 3-5 of the most important publications
• A scan of the dissertation certificate (which includes the date of the defense)
• The candidate’s CV
• A short laudation (40-80 words)

Please use this template (pdf) (latex) to nominate a candidate and send the nomination (preferably as a single pdf file) to yr-award@ecva.net If you should have any questions, please send an email to yr-award@ecva.net.

Angela Dai

Angela stands out as a pioneering researcher in 3D scene reconstruction and semantic scene understanding. Angela’s research has played a pivotal role in establishing modern 3D deep learning as a prominent and influential area of study. Her efforts have significantly broadened the scope of visual perception, transforming the landscape of research in the digitization and understanding of 3D environments.

Johannes Schönberger

Johannes Schönberger has made numerous significant contributions to geometric computer vision and, through his dedication to open and reproducible research, has enabled a large body of work in both academia and industry. Since earning his PhD, he has advanced and influenced the state of the art in topics related to mapping and localization as well as pioneered new research directions in the area of privacy preserving methods.

Zeynep Akata

Zeynep Akata has had a number of significant contributions in multimodal deep learning, in vision and language for low-shot learning and in explainable machine learning. Her research defined the state of the art for zero-shot learning and her natural language explanations provided novel human-understandable justifications both for fine-grained visual classification and autonomous driving. She is a full professor at Tubingen University and leads a research group there. She has received a number of awards.

Amir Zamir

With a remarkable combination of creativity, productivity, and technical depth, Amir Zamir stands out as an emerging superstar in computer vision. Since earning his PhD, Prof. Zamir has pioneered highly influential approaches in multi-task/transfer learning and 3D environment simulation that serve as inspiration not simply for follow-up papers, but for entire research groups – both in academia and in industry. His work points the way forward as our field transitions from the era of “dataset AI” to the embodied AI future.

With the ECVA PhD Award, ECVA recognizes and encourages outstanding research achievements during the dissertation phase, in the field of computer vision. Each awardee will receive Euro 2500 prize money. There will be two awards every year, which will be awarded at the next ECCV conference.

Deadline

The deadline for nominating a dissertation for the years 2024 and 2025 is:
April 30, 2026 (End of Day, Anywhere on Earth)

Eligibility

Any European dissertation in the area of computer vision that has been defended in 2024 / 2025 can be nominated. A dissertation is considered European if the dissertation has been performed primarily at a European research institution.

Selection Criteria

Dissertations will be reviewed for technical depth and significance of the research contribution and potential impact on theory and practice.

How to Nominate

The nomination (self-nominations by the author of the dissertation are not allowed) must include:

• A short summary (approx. 2 pages) of the main research achievements of the dissertation
• 2-3 of the most important publications
• A scan of the dissertation certificate (which includes the date of the defense)
• Final version of the dissertation as pdf (or link to pdf)
• The candidate’s CV
• A short laudation (40-80 words)

Please use this template (pdf) (latex) to nominate a candidate and send the nomination (preferably as a single pdf file) to phd-award@ecva.net. If you should have any questions, please send an email to phd-award@ecva.net.

Yaoyao Liu

Learning from Imperfect Data: Incremental Learning and Few-shot Learning

Yaoyao Liu introduced many creative methods to apply the “learning-to-learn” idea in few-shot learning, continual learning, and their applications. In terms of algorithmic development, his work leveraged online learning, reinforcement learning, bilevel optimization to make a various of prefixed components learnable and adaptive. In terms of applications, his work explored many important real-world tasks, including object detection and medical imaging. His scientific works have become highly influential in the field.

Songyou Peng

Neural Scene Representations for 3D Reconstruction and Scene Understanding

Dr. Peng has significantly advanced the field of 3D computer vision, focusing on the areas of 3D reconstruction and 3D scene understanding. His research in his PhD thesis not only addresses the challenges of large-scale 3D scene reconstruction but also pioneers the use of large vision foundation models for zero-shot 3D scene understanding. His works have inspired extensive follow-up research across various fields of computer vision, robotics, and graphics.

Jan Eric Lenssen

Differentiable Algorithms with Data-driven Parameterization in 3D Vision

In his PhD work, Jan Eric Lenssen made a large number of very original, technically deep, and significant contributions in the area of geometric deep learning, graph neural networks, and 3D representation learning. His contributions to efficient GPU message passing algorithms laid the foundation for Pytorch Geometric, the most used graph neural network library world-wide and for the successful startup Kumo.ai.

Shangzhe Wu

Unsupervised Learning of 3D Objects in the Wild

Shangzhe Wu’s PhD pioneers monocular 3D reconstruction of deformable objects without 3D supervision from images and videos collected in the wild. The thesis advances this field on two fronts simultaneously: (1) demonstrating an unprecedented quality of results and (2) using only minimal assumptions, leading to both significant practical values and scientific insights. The work in this thesis has inspired a wide range of follow-up work by the community and has significantly shaped the field of unsupervised 3D learning.

Iro Laina

Semantics, Language and Geometry: Learning to Understand the Scene

Iro Laina’s dissertation addresses a broad range of challenging problems in computer vision, and specifically in scene understanding. The dissertation makes a series of timely, strong, and innovative scientific contributions by breaking down the overarching scene understanding problem to geometric, semantic, and linguistic components. It advances the state of the art in perceptual tasks and in the intersection of vision and language. The content of the thesis has been adopted in widespread real-world applications and has created significant impact.

Yongqin Xian

Learning from Limited Labeled Data – Zero Shot and Few-Shot Learning

Yongqin Xian made significant contributions in the area of zero-shot and few-shot learning. He proposed novel feature generating frameworks that have defined the state of the art in this area. He introduced the first approach for zero-label semantic segmentation and pushed the boundary of few-shot video classification by leveraging unlabeled videos from a large dataset. In addition, his zero-shot learning benchmark has become highly influential in the field.

Marcella Cornia

Learning to Describe Salient Objects in Images with Vision and Language

Marcella Cornia is a cocktail of excellent technical expertise and great intellectual curiosity; the cherry on the glass is her kindness and willingness to work in both pure research projects, industrial research, and public engagement.

Triantafyllos Afouras

Audio-visual Deep Learning

Triantafyllos Afouras’ outstanding dissertation introduces multiple creative ways to use audio and visual data in machine learning and in applications. It explores cross-modal and self-supervised learning for the important areas of lip reading, audio-visual speaker separation and enhancement, audio-visual object detection, and sign language recognition. Both the scientific outcomes of the dissertation and the datasets released have had a high impact in terms of citations and downloads.