One day workshop on Computer Vision

The workshop delved into a diverse spectrum of cutting-edge topics in machine learning and computer vision. It featured presentations on equivariant convolution and attention within radiance fields, the application of diffusion models in medical imaging, and the intricacies of self-supervised learning, providing a comprehensive understanding of its potential applications. The exploration expanded to meta-learning's pivotal role in few-shot learning scenarios and the integration of causally enabled machine learning for both vision and decision-making processes. The use of generative models for crafting digital humans was also unveiled, showcasing interesting applications and demos. The workshop concluded with insights into neural graph function approximation, seamlessly bridging the gap from graph signals to general graph spaces.

One day workshop on Computer Vision

Costas Daniilidis

Equivariant Convolution and Attention in Radiance Fields

Sotirios Tsaftaris

Diffusion models in medical imaging / analysis

Nikolaos Komodakis

An introduction to self-supervised learning

Eleni Triantafyllou

The role of meta-learning in few-shot learning

Athanasios Vlontzos

Causally enabled ML for vision and decision making

Stefanos Zafeiriou

Generative Models for Digital Humans

Giorgos Bouritsas

Neural graph function approximation: from graph signals to general graph spaces


The project “ARCHIMEDES Unit: Research in Artificial Intelligence, Data Science and Algorithms” with code OPS 5154714 is implemented by the National Recovery and Resilience Plan “Greece 2.0” and is funded by the European Union – NextGenerationEU.

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