Publications de Pascal Frossard

High-Quality Reconstruction of Depth Maps From Graph-Based Non-Uniform Sampling

Depth sensing is essential for intelligent computer vision applications, but it often suffers from low range precision and spatial resolution. To address this problem, we propose a novel framework that combines non-uniform sampling and reconstruction based ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2025

Decoding the Interactions and Functions of Non-coding Rna with Artificial Intelligence

Pascal Frossard, Jacques Fellay, Vincent Jung

In addition to encoding proteins, mRNAs have context-specific regulatory roles that contribute to many cellular processes. However, uncovering new mRNA functions is constrained by limitations of traditional biochemical and computational methods. In this Ro ...

NATURE PORTFOLIO2025

OSLO-IC: On-the-Sphere Learned Omnidirectional Image Compression with Attention Modules and Spatial Context

Pascal Frossard

Developing effective 360-degree (spherical) image compression techniques is crucial for technologies like virtual reality and automated driving. This paper advances the state-of-the-art in on-the-sphere learning (OSLO) for omnidirectional image compression ...

IEEE2025

Hierarchical Spherical CNNs with Lifting-Based Adaptive Wavelets for Pooling and Unpooling

Pascal Frossard

Institute of Electrical and Electronics Engineers (IEEE)2025

Revealing Brain and Spinal Cord Functional Networks by Block-Structured Graph Laplacian Mixture Models

Pascal Frossard, Dimitri Nestor Alice Van De Ville, William Cappelletti, Ekansh Sareen, Ilaria Ricchi

The Graph Laplacian Mixture Model (GLMM) allows to infer multiple underlying graph structures from multivariate time series data. Given its effectiveness in identifying brain states from brain activity measured by functional magnetic resonance imaging (fMR ...

IEEE2025

Sequential Representation Learning via Static-Dynamic Conditional Disentanglement

Pascal Frossard

This paper explores self-supervised disentangled representation learning within sequential data, focusing on separating time-indep- endent and time-varying factors in videos. We propose a new model that breaks the usual independence assumption between thos ...

Springer Science and Business Media Deutschland GmbH2025

Equivariant 3D-conditional diffusion model for molecular linker design

Pascal Frossard, Bruno Emanuel Ferreira De Sousa Correia, Arne Schneuing, Ilia Igashov, Clément Arthur Yvon Vignac

Fragment-based drug discovery has been an effective paradigm in early-stage drug development. An open challenge in this area is designing linkers between disconnected molecular fragments of interest to obtain chemically relevant candidate drug molecules. I ...

2024

360Spred: Saliency Prediction for 360-Degree Videos Based on 3D Separable Graph Convolutional Networks

Pascal Frossard

Predicting the saliency map of a 360-degree video is the key for various downstream tasks, such as saliency-based compression and tile-based adaptive streaming. Besides static salient objects, the moving target will also contribute to the saliency map. The ...

2024

IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection

Pascal Frossard

Bird's eye view (BEV) representation has emerged as a dominant solution for describing 3D space in autonomous driving scenarios. However, objects in the BEV representation typically exhibit small sizes, and the associated point cloud context is inherently ...

IEEE Computer Society2024

Tertiary Lymphoid Structures Generation Through Graph-Based Diffusion

Pascal Frossard, Ntorina Thanou

Graph-based representation approaches have been proven to be successful in the analysis of biomedical data due to their capability of capturing intricate dependencies between biological entities, such as the spatial organization of different cell types in ...

Springer International Publishing Ag2024