Data, code and models released by NAVER LABS Europe


The first general-purpose massively multilingual HuBERT speech representation model.

A promising compact model for speech processing pipelines, offering an unprecedented balance between high performance and parameter efficiency. Developed within the the EU UTTER project.


A sparse bi-encoder BERT-based model for effective and efficient first-stage ranking.

Several releases: SPLADE V-2, SPLADE V-3, CoSPLADE etc.


Efficient distillation of multi-task speech models via language-specific experts.

A multitask and multilingual speech model covering 99 languages.


Whole-body human mesh recovery of multiple persons from a single image.

A simple yet effective single-shot method to detect multiple people in an image and estimate their pose, body shape and expression.


Benchmarking Object-agnostic Hand-Object 3D Reconstruction

The SHOWMe dataset comprises 96 videos with their associated high-quality textured meshes of a hand holding an object.


A multi-subject 4D dataset of human motion sequences in varying outfits exhibiting large displacements.

Collaboration with INRIA.


Contextualizing SPLADE for conversational information retrieval.

SPLADE is sparse bi-encoder BERT-based model for effective and efficient first-stage ranking.


Correcting 3D human poses with natural language.

The PoseFix dataset consists of several thousand paired 3D poses and corresponding text feedback that describes how the source pose needs to be modified to obtain the target pose.


Stable Learning of Augmentations with Cold-start and KL regularization.

Learning augmentation policies without prior knowledge.

RELIS semantic segmentation

Reliability in semantic segmentation: are we on the right track?

A codebase to evaluate the robustness and uncertainty properties of semantic segmentation models as implemented in the CVPR 2024 paper.


No reason for no supervision: improved generalization in supervised models.

Model for transfer learning.

Synthetic ImageNet clones

Fake it till you make it: learning transferable representations from synthetic ImageNet clones.

Two ResNet50 models pretrained on our synthetic ImageNet clones: ImageNet-100-SD or ImageNet-1K-SD.


DIStributional Control of LLMs

A toolkit for controlling language models and other generative models.


Attention-based Retrieval with Text-Explicit Matching and Implicit Similarity.

An Explicit Matching module for compatibility and an Implicit Similarity module for relevance.


Cross-view Completion for 3D vision

Unsupervised representation learning task trained from pairs of images showing the same scene from different viewpoints.


Efficient pareto-optimal fairness-utility amortizations in repeated rankings.

The expohedron is a polytope whose points represent all achievable exposures of items for a Position Based Model (PBM).

Learning super-features for image retrieval

A novel architecture for deep image retrieval

Code for running our FIRe model , based solely on mid-level features that we call super-features.

Multilingual machine translation

Assessing the impact of compression methods on MNMT.

Code repository for paper: What do compressed multilingual machine translation models forget?

Neural feature fusion fields

3D distillation of self-supervised 2D image representations.

A method that improves dense 2D image feature extractors when the latter are applied to the analysis of multiple images reconstructible as a 3D scene.


A novel, plug and play model for human 3D shape estimation in videos.

Model trained by mimicking the BERT algorithm from the natural language processing community.


Quantization-based 3D human motion generation and forecasting.

An auto-regressive transformer-based approach which internally compresses human motion into quantized latent sequences.


3D human poses from natural language.

A dataset pairing 3D human poses with both automatically generated and human-written descriptions.


A shallow multilingual machine translation model for low-resource languages.

Covers more than 10K language pairs, achieves competitive results with M2M-100 while being much smaller and faster.

Semantic segmentation (OASIS benchmark)

On the road to Online Adaptation for Semantic Image Segmentation (OASIS).

A Pytorch codebase for research to replicate the CVPR22 paper.

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