Linear video coding
Conventional and learning-based video compression — codecs, rate–distortion, and the boundary between hand-designed and neural pipelines.
Active research group · LTCI
Multimedia team - at LTCI, Télécom Paris, Institut Polytechnique de Paris
The Multimedia (MM) team conducts research on the full life-cycle of multimedia data: from compression and transport to learning, explainability, and generation. We work where signal processing meets modern deep learning — with an emphasis on frugal, geometric, and multimodal approaches.
89+
Publications
2021–2026, peer-reviewed
6
Faculty
+ PhD students & postdocs
7
Research themes
from coding to learning
A*
Top venues
CVPR · ICCV · NeurIPS · ICLR
CVPRICCVNeurIPSICLRAAAIWACVECCVICMLACM MultimediaIEEE TPAMIIEEE TMMIEEE TIPIEEE TCSVTPattern Recognition
CVPRICCVNeurIPSICLRAAAIWACVECCVICMLACM MultimediaIEEE TPAMIIEEE TMMIEEE TIPIEEE TCSVTPattern Recognition
What we work on
All themes →Rich-media scene representation
Representing complex scenes with multiple media, layouts, and interactions — beyond a single video stream.
Multimedia content adaptation
Adapting multimedia content to networks, devices, and users — quality of experience under constraints.
Multimedia distribution networks
Transport, orchestration, and protocols for delivering multimedia at scale.
Frugal and efficient AI
Pruning, quantization, low-rank methods, and other tools to make deep models small enough to deploy.
Geometric deep learning
Learning on graphs, manifolds, and structured domains — where the geometry of the data shapes the architecture.
Latest news
All news →- CVPR 2026
Three papers accepted at CVPR 2026
The MM team has three accepted contributions at CVPR 2026, spanning compression, geometric deep learning, and multimodal learning.
- NeurIPS 2025
Three papers accepted at NeurIPS 2025
Three NeurIPS 2025 acceptances on neural network compression and geometric deep learning.
- ICCV 2025
Four papers accepted at ICCV 2025
Four ICCV 2025 acceptances covering compression, generative models, and multimodal learning.
Recent work
All publications →Capsule networks do not need to model everything
Compression in 3D Gaussian Splatting: A Survey of Methods, Trends, and Future Directions
INSTANT: COMPRESSING GRADIENTS AND ACTIVATIONS FOR RESOURCE-EFFICIENT TRAINING
RAVE: Rate-Adaptive Visual Encoding for 3D Gaussian Splatting
A Fused Gromov-Wasserstein Approach to Subgraph Contrastive Learning
A Transductive and Inductive GNNs for Physical Moving Objects Detection in Surface Scenes for Digital Twins
Join us.
We're recruiting PhD students, postdocs, and interns who want to push the boundaries of frugal and structured deep learning, multimedia coding, and multimodal AI. If you like working on problems that matter — and shipping code that proves it — get in touch.