special session on : Physical Models and AI in Image and in Multimodality at CBMI2023 conference (http://cbmi2023.org/)

Deep neural networks now enable the learning of complex functions from the data to address a variety of difficult problems. However, questions emerge on the relevance and understanding of their learned functions, especially when relating to physical models.

Knowledge on the physical environment can allow for the introduction of constraints on the model in order to reduce the search space and to converge to more relevant and simplified solutions that can contribute to higher model confidence. Image synthesis and computer graphics are typical application domains that make use of the known physical constraints on objects and materials to produce realistic images.

Conversely, when the physical model is not perfectly known, AI-based models can help identify relevant solutions to improve the physicist's knowledge and understanding of a given phenomenon. A typical example is haze removal in images while the physical haze model is still not well identified.

Finally, when addressing AI and physics problems, the different data modalities need to be fused appropriately. Again, multimodality handling can be guided by knowledge on the physical model or can be learnt in order to increase knowledge.  

This special session aims to bring together researchers working on analysis, indexing, and mining of data related to images and multimodality in various fields involving physical models, remote sensing, astrophysics, mechanics, computer graphics, provides them a venue for sharing novel ideas and discuss their most recent works and promotes exchanges between computer scientists and astrophysicists. Topics of interest include (but are not limited to):

• Supervised learning: classification and regression

• Unsupervised learning: clustering and dimensionality reduction

• Real time, on-site or on-board processing

• Learning physical models from data

• From simulated to actual data

• Time-Series Analysis

• Management of data in physics

Organizer :

• Alexandre Benoit, Savoie Mont Blanc University, LISTIC, Annecy, France — alexandre.benoit@univ-smb.fr