23rd – 27th October 2022, Tel Aviv, Israel
European Conference on Computer Vision (ECCV) is one of the top conferences in computer vision.
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European Conference on Computer Vision (ECCV) 2022
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Granularity-aware Adaptation for Image Retrieval over Multiple Tasks
Jon Almazán, Byungsoo Ko, Geonmo Gu, Diane Larlus, Yannis Kalantidis - PoseScript: 3D Human Poses from Natural Language
Ginger Delmas, Philippe Weinzaepfel, Thomas Lucas, Francesc Moreno-Noguer, Grégory Rogez - PoseGPT: Quantization-Based 3D Human Motion Generation and Forecasting
Thomas Lucas, Fabien Baradel, Philippe Weinzaepfel, Grégory Rogez
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Workshops
23rd October 2022, David Intercontinental Hotel, Salon G (190), Tel Aviv, Israel
Workshop: Self Supervised Learning: What is Next?
Diane Larlus is an organizer of this workshop on potential research direction seeds, from radically new self-supervision tasks, data sources and paradigms to surprising counter-intuitive results.
Workshop: Instance-Level Recognition Workshop (ILR2022)
This year, the ILR workshop proposes the first Universal Image Embedding Challenge, where the goal is to develop image representations that work well across several domains combined. The workshop is a follow-up of four previous editions — the first two having focused only on landmark recognition (, ), and the latest two expanded to two extra domains (artworks and products) (, ).
Oct. 24th, 11:30am-11:40am (GMT+3)
Invited paper talk: “Granularity-aware Adaptation for Image Retrieval over Multiple Tasks”
Jon Almazan, Byungsoo Ko, Geonmo Gu, Diane Larlus and Yannis Kalantidis
(Hosted by Torsten Sattler)
Full Schedule
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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