8 citations · 24 across the 9 of their papers we have counts for
9 papers
Escaping Plato's Cave: Towards the Alignment of 3D and Text Latent Spaces
Souhail Hadgi, Luca Moschella, Andrea Santilli +5
Recent works have shown that, when trained at scale, uni-modal 2D vision and text encoders converge to learned features that share remarkable structural properties, despite arising…
Bootstrapping Parallel Anchors for Relative Representations
Irene Cannistraci, Luca Moschella, Valentino Maiorca +3
The use of relative representations for latent embeddings has shown potential in enabling latent space communication and zero-shot model stitching across a wide range of applicatio…
Latent Spectral Regularization for Continual Learning
Emanuele Frascaroli, Riccardo Benaglia, Matteo Boschini +4
While biological intelligence grows organically as new knowledge is gathered throughout life, Artificial Neural Networks forget catastrophically whenever they face a changing train…
ASIF: Coupled Data Turns Unimodal Models to Multimodal Without Training
Antonio Norelli, Marco Fumero, Valentino Maiorca +3
CLIP proved that aligning visual and language spaces is key to solving many vision tasks without explicit training, but required to train image and text encoders from scratch on a…
Relative representations enable zero-shot latent space communication
Luca Moschella, Valentino Maiorca, Marco Fumero +3
Neural networks embed the geometric structure of a data manifold lying in a high-dimensional space into latent representations. Ideally, the distribution of the data points in the…
Metric Based Few-Shot Graph Classification
Donato Crisostomi, Simone Antonelli, Valentino Maiorca +3
Many modern deep-learning techniques do not work without enormous datasets. At the same time, several fields demand methods working in scarcity of data. This problem is even more c…
Explanatory Learning: Beyond Empiricism in Neural Networks
Antonio Norelli, Giorgio Mariani, Luca Moschella +4
We introduce Explanatory Learning (EL), a framework to let machines use existing knowledge buried in symbolic sequences -- e.g. explanations written in hieroglyphic -- by autonomou…
Shape registration in the time of transformers
Giovanni Trappolini, Luca Cosmo, Luca Moschella +3
In this paper, we propose a transformer-based procedure for the efficient registration of non-rigid 3D point clouds. The proposed approach is data-driven and adopts for the first t…
Learning Spectral Unions of Partial Deformable 3D Shapes
Luca Moschella, Simone Melzi, Luca Cosmo +5
Spectral geometric methods have brought revolutionary changes to the field of geometry processing. Of particular interest is the study of the Laplacian spectrum as a compact, isome…