valeo.ai
@valeoai.bsky.social
We are a research team on artificial intelligence for automotive applications working toward assisted and autonomous driving.
--> https://valeoai.github.io/ <--
--> https://valeoai.github.io/ <--
📢 NAF is fully open-source!
The repo contains:
✅ Pretrained model
✅ Example notebooks
✅ Evaluation and training codes
Check it out & ⭐ the repo: github.com/valeoai/NAF
The repo contains:
✅ Pretrained model
✅ Example notebooks
✅ Evaluation and training codes
Check it out & ⭐ the repo: github.com/valeoai/NAF
November 25, 2025 at 10:44 AM
📢 NAF is fully open-source!
The repo contains:
✅ Pretrained model
✅ Example notebooks
✅ Evaluation and training codes
Check it out & ⭐ the repo: github.com/valeoai/NAF
The repo contains:
✅ Pretrained model
✅ Example notebooks
✅ Evaluation and training codes
Check it out & ⭐ the repo: github.com/valeoai/NAF
🛠️ Already have a complex, pre-trained pipeline?
If you are using bilinear interpolation anywhere, NAF acts as a strict drop-in replacement.
Just swap it in. No retraining required. It’s literally free points for your metrics.📈
If you are using bilinear interpolation anywhere, NAF acts as a strict drop-in replacement.
Just swap it in. No retraining required. It’s literally free points for your metrics.📈
November 25, 2025 at 10:44 AM
🛠️ Already have a complex, pre-trained pipeline?
If you are using bilinear interpolation anywhere, NAF acts as a strict drop-in replacement.
Just swap it in. No retraining required. It’s literally free points for your metrics.📈
If you are using bilinear interpolation anywhere, NAF acts as a strict drop-in replacement.
Just swap it in. No retraining required. It’s literally free points for your metrics.📈
🎯 NAF is versatile!
Not just zero-shot feature upsampling: it shines on image restoration too, delivering sharp, high-quality results across multiple applications. 🖼️
Not just zero-shot feature upsampling: it shines on image restoration too, delivering sharp, high-quality results across multiple applications. 🖼️
November 25, 2025 at 10:44 AM
🎯 NAF is versatile!
Not just zero-shot feature upsampling: it shines on image restoration too, delivering sharp, high-quality results across multiple applications. 🖼️
Not just zero-shot feature upsampling: it shines on image restoration too, delivering sharp, high-quality results across multiple applications. 🖼️
🔬 NAF meets theory.
Under the hood, NAF learns an Inverse Discrete Fourier Transform: revealing a link between feature upsampling, classical filtering, and Fourier theory.
✨ Feature upsampling is no longer a black box
Under the hood, NAF learns an Inverse Discrete Fourier Transform: revealing a link between feature upsampling, classical filtering, and Fourier theory.
✨ Feature upsampling is no longer a black box
November 25, 2025 at 10:44 AM
🔬 NAF meets theory.
Under the hood, NAF learns an Inverse Discrete Fourier Transform: revealing a link between feature upsampling, classical filtering, and Fourier theory.
✨ Feature upsampling is no longer a black box
Under the hood, NAF learns an Inverse Discrete Fourier Transform: revealing a link between feature upsampling, classical filtering, and Fourier theory.
✨ Feature upsampling is no longer a black box
💡 NAF is a super simple, universal architecture that reweights any features using only the high-resolution image:
🧬 Lightweight image encoder (600k params)
🔁 Rotary Position Embeddings (RoPE)
🔍 Cross-Scale Neighborhood Attention
First fully learnable VFM-agnostic reweighting!✅
🧬 Lightweight image encoder (600k params)
🔁 Rotary Position Embeddings (RoPE)
🔍 Cross-Scale Neighborhood Attention
First fully learnable VFM-agnostic reweighting!✅
November 25, 2025 at 10:44 AM
💡 NAF is a super simple, universal architecture that reweights any features using only the high-resolution image:
🧬 Lightweight image encoder (600k params)
🔁 Rotary Position Embeddings (RoPE)
🔍 Cross-Scale Neighborhood Attention
First fully learnable VFM-agnostic reweighting!✅
🧬 Lightweight image encoder (600k params)
🔁 Rotary Position Embeddings (RoPE)
🔍 Cross-Scale Neighborhood Attention
First fully learnable VFM-agnostic reweighting!✅
🔥 NAF sets a new SoTA!
It beats both VFM-specific upsamplers (FeatUp, JAFAR) and VFM-agnostic methods (JBU, AnyUp) across downstream tasks:
- 🥇Semantic Segmentation
- 🥇Depth Estimation
- 🥇Open-Vocabulary
- 🥇Video Propagation, etc.
Even for massive models like: DINOv3-7B !
It beats both VFM-specific upsamplers (FeatUp, JAFAR) and VFM-agnostic methods (JBU, AnyUp) across downstream tasks:
- 🥇Semantic Segmentation
- 🥇Depth Estimation
- 🥇Open-Vocabulary
- 🥇Video Propagation, etc.
Even for massive models like: DINOv3-7B !
November 25, 2025 at 10:44 AM
🔥 NAF sets a new SoTA!
It beats both VFM-specific upsamplers (FeatUp, JAFAR) and VFM-agnostic methods (JBU, AnyUp) across downstream tasks:
- 🥇Semantic Segmentation
- 🥇Depth Estimation
- 🥇Open-Vocabulary
- 🥇Video Propagation, etc.
Even for massive models like: DINOv3-7B !
It beats both VFM-specific upsamplers (FeatUp, JAFAR) and VFM-agnostic methods (JBU, AnyUp) across downstream tasks:
- 🥇Semantic Segmentation
- 🥇Depth Estimation
- 🥇Open-Vocabulary
- 🥇Video Propagation, etc.
Even for massive models like: DINOv3-7B !
Need pixel-level features from your backbone (DINOv3, CLIP, RADIO, FRANCA...)?
🚀Introducing NAF: A universal, zero-shot feature upsampler.
It turns low-res ViT features into pixel-perfect maps.
-⚡ Model-agnostic
-🥇 SoTA results
-🚀 4× faster than SoTA
-📈 Scales up to 2K res
🚀Introducing NAF: A universal, zero-shot feature upsampler.
It turns low-res ViT features into pixel-perfect maps.
-⚡ Model-agnostic
-🥇 SoTA results
-🚀 4× faster than SoTA
-📈 Scales up to 2K res
November 25, 2025 at 10:44 AM
Need pixel-level features from your backbone (DINOv3, CLIP, RADIO, FRANCA...)?
🚀Introducing NAF: A universal, zero-shot feature upsampler.
It turns low-res ViT features into pixel-perfect maps.
-⚡ Model-agnostic
-🥇 SoTA results
-🚀 4× faster than SoTA
-📈 Scales up to 2K res
🚀Introducing NAF: A universal, zero-shot feature upsampler.
It turns low-res ViT features into pixel-perfect maps.
-⚡ Model-agnostic
-🥇 SoTA results
-🚀 4× faster than SoTA
-📈 Scales up to 2K res
LED: Light Enhanced Depth Estimation at Night
by: S de Moreau, Y. Almehio, @abursuc.bsky.social, H. El-Idrissi, B. Stanciulescu, @fabienmoutarde
tl;dr: a light enhancement method for better depth estimation in low-light conditions
📄 arxiv.org/abs/2409.08031
Code: ✅
by: S de Moreau, Y. Almehio, @abursuc.bsky.social, H. El-Idrissi, B. Stanciulescu, @fabienmoutarde
tl;dr: a light enhancement method for better depth estimation in low-light conditions
📄 arxiv.org/abs/2409.08031
Code: ✅
November 24, 2025 at 9:00 AM
LED: Light Enhanced Depth Estimation at Night
by: S de Moreau, Y. Almehio, @abursuc.bsky.social, H. El-Idrissi, B. Stanciulescu, @fabienmoutarde
tl;dr: a light enhancement method for better depth estimation in low-light conditions
📄 arxiv.org/abs/2409.08031
Code: ✅
by: S de Moreau, Y. Almehio, @abursuc.bsky.social, H. El-Idrissi, B. Stanciulescu, @fabienmoutarde
tl;dr: a light enhancement method for better depth estimation in low-light conditions
📄 arxiv.org/abs/2409.08031
Code: ✅
LOGen: Toward Lidar Object Generation by Point Diffusion
by: E. Kirby, @mickaelchen.bsky.social, R. Marlet, N. Samet
tl;dr: a diffusion-based method producing lidar point clouds of dataset objects, with an extensive control of the generation
📄 arxiv.org/abs/2412.07385
Code: ✅
by: E. Kirby, @mickaelchen.bsky.social, R. Marlet, N. Samet
tl;dr: a diffusion-based method producing lidar point clouds of dataset objects, with an extensive control of the generation
📄 arxiv.org/abs/2412.07385
Code: ✅
November 24, 2025 at 9:00 AM
LOGen: Toward Lidar Object Generation by Point Diffusion
by: E. Kirby, @mickaelchen.bsky.social, R. Marlet, N. Samet
tl;dr: a diffusion-based method producing lidar point clouds of dataset objects, with an extensive control of the generation
📄 arxiv.org/abs/2412.07385
Code: ✅
by: E. Kirby, @mickaelchen.bsky.social, R. Marlet, N. Samet
tl;dr: a diffusion-based method producing lidar point clouds of dataset objects, with an extensive control of the generation
📄 arxiv.org/abs/2412.07385
Code: ✅
The team is at BMVC'25 @bmvcconf.bsky.social
to present three papers tackling challenges in 3D vision!
We are presenting new works on:
✨ Diffusion for LiDAR point-clouds
🌙 Depth estimation with light enhancement
🔄 Multimodal distillation for 3D semantic segmentation
👇 #BMVC2025
to present three papers tackling challenges in 3D vision!
We are presenting new works on:
✨ Diffusion for LiDAR point-clouds
🌙 Depth estimation with light enhancement
🔄 Multimodal distillation for 3D semantic segmentation
👇 #BMVC2025
November 24, 2025 at 9:00 AM
The team is at BMVC'25 @bmvcconf.bsky.social
to present three papers tackling challenges in 3D vision!
We are presenting new works on:
✨ Diffusion for LiDAR point-clouds
🌙 Depth estimation with light enhancement
🔄 Multimodal distillation for 3D semantic segmentation
👇 #BMVC2025
to present three papers tackling challenges in 3D vision!
We are presenting new works on:
✨ Diffusion for LiDAR point-clouds
🌙 Depth estimation with light enhancement
🔄 Multimodal distillation for 3D semantic segmentation
👇 #BMVC2025
Honored to host a very special guest today: Huy V. Vo, PhD alumnus of @valeoai.bsky.social
He presented his work on automatic data-curation strategies for self-supervised representation learning (DINOv2, DINOv3). Find out more about his research here: huyvvo.github.io
He presented his work on automatic data-curation strategies for self-supervised representation learning (DINOv2, DINOv3). Find out more about his research here: huyvvo.github.io
November 20, 2025 at 6:02 PM
Honored to host a very special guest today: Huy V. Vo, PhD alumnus of @valeoai.bsky.social
He presented his work on automatic data-curation strategies for self-supervised representation learning (DINOv2, DINOv3). Find out more about his research here: huyvvo.github.io
He presented his work on automatic data-curation strategies for self-supervised representation learning (DINOv2, DINOv3). Find out more about his research here: huyvvo.github.io
Thrilled to have Jayneel Parekh visiting us over a break in the CVPR sprint to talk about his exciting works in analyzing MLLMs representations. Check out his great work jayneelparekh.github.io
November 20, 2025 at 8:31 AM
Thrilled to have Jayneel Parekh visiting us over a break in the CVPR sprint to talk about his exciting works in analyzing MLLMs representations. Check out his great work jayneelparekh.github.io
Privileged to the diffusion master @nicolasdufour.bsky.social give to our team (full house) a tour of his excellent works in data and compute efficient diffusion models and a sneak preview of his latest MIRO work.
Check it out 👌
Check it out 👌
October 31, 2025 at 7:28 PM
Privileged to the diffusion master @nicolasdufour.bsky.social give to our team (full house) a tour of his excellent works in data and compute efficient diffusion models and a sneak preview of his latest MIRO work.
Check it out 👌
Check it out 👌
Analyzing Fine-tuning Representation Shift for Multimodal LLMs Steering Alignment
tl;dr: a new method for understanding and controlling how MLLMs adapt during fine-tuning
by: P. Khayatan, M. Shukor, J. Parekh, A. Dapogny, @matthieucord.bsky.social
📄: arxiv.org/abs/2501.03012
tl;dr: a new method for understanding and controlling how MLLMs adapt during fine-tuning
by: P. Khayatan, M. Shukor, J. Parekh, A. Dapogny, @matthieucord.bsky.social
📄: arxiv.org/abs/2501.03012
October 17, 2025 at 10:31 PM
Analyzing Fine-tuning Representation Shift for Multimodal LLMs Steering Alignment
tl;dr: a new method for understanding and controlling how MLLMs adapt during fine-tuning
by: P. Khayatan, M. Shukor, J. Parekh, A. Dapogny, @matthieucord.bsky.social
📄: arxiv.org/abs/2501.03012
tl;dr: a new method for understanding and controlling how MLLMs adapt during fine-tuning
by: P. Khayatan, M. Shukor, J. Parekh, A. Dapogny, @matthieucord.bsky.social
📄: arxiv.org/abs/2501.03012
FLOSS: Free Lunch in Open-vocabulary Semantic Segmentation
tl;dr: a simple trick to boost open-vocabulary semantic segmentation by identifying class expert prompt templates
by: Y. Benigmim, M. Fahes, @tuanhungvu.bsky.social, @abursuc.bsky.social, R. de Charette.
📄: arxiv.org/abs/2504.10487
tl;dr: a simple trick to boost open-vocabulary semantic segmentation by identifying class expert prompt templates
by: Y. Benigmim, M. Fahes, @tuanhungvu.bsky.social, @abursuc.bsky.social, R. de Charette.
📄: arxiv.org/abs/2504.10487
October 17, 2025 at 10:30 PM
FLOSS: Free Lunch in Open-vocabulary Semantic Segmentation
tl;dr: a simple trick to boost open-vocabulary semantic segmentation by identifying class expert prompt templates
by: Y. Benigmim, M. Fahes, @tuanhungvu.bsky.social, @abursuc.bsky.social, R. de Charette.
📄: arxiv.org/abs/2504.10487
tl;dr: a simple trick to boost open-vocabulary semantic segmentation by identifying class expert prompt templates
by: Y. Benigmim, M. Fahes, @tuanhungvu.bsky.social, @abursuc.bsky.social, R. de Charette.
📄: arxiv.org/abs/2504.10487
MoSiC: Optimal-Transport Motion Trajectories for Dense Self-Supervised Learning
tl;dr: a self-supervised learning of temporally consistent representations from video w/ motion cues
by: M. Salehi, S. Venkataramanan, I. Simion, E. Gavves, @cgmsnoek.bsky.social, Y. Asano
📄: arxiv.org/abs/2506.08694
tl;dr: a self-supervised learning of temporally consistent representations from video w/ motion cues
by: M. Salehi, S. Venkataramanan, I. Simion, E. Gavves, @cgmsnoek.bsky.social, Y. Asano
📄: arxiv.org/abs/2506.08694
October 17, 2025 at 10:30 PM
MoSiC: Optimal-Transport Motion Trajectories for Dense Self-Supervised Learning
tl;dr: a self-supervised learning of temporally consistent representations from video w/ motion cues
by: M. Salehi, S. Venkataramanan, I. Simion, E. Gavves, @cgmsnoek.bsky.social, Y. Asano
📄: arxiv.org/abs/2506.08694
tl;dr: a self-supervised learning of temporally consistent representations from video w/ motion cues
by: M. Salehi, S. Venkataramanan, I. Simion, E. Gavves, @cgmsnoek.bsky.social, Y. Asano
📄: arxiv.org/abs/2506.08694
GaussRender: Learning 3D Occupancy with Gaussian Rendering
tl;dr: a module for 3D occupancy learning that enforces 2D-3D consistency through differentiable Gaussian rendering
by: L. Chambon, @eloizablocki.bsky.social, @alexandreboulch.bsky.social, M. Chen, M. Cord
📄: arxiv.org/abs/2502.05040
tl;dr: a module for 3D occupancy learning that enforces 2D-3D consistency through differentiable Gaussian rendering
by: L. Chambon, @eloizablocki.bsky.social, @alexandreboulch.bsky.social, M. Chen, M. Cord
📄: arxiv.org/abs/2502.05040
October 17, 2025 at 10:29 PM
GaussRender: Learning 3D Occupancy with Gaussian Rendering
tl;dr: a module for 3D occupancy learning that enforces 2D-3D consistency through differentiable Gaussian rendering
by: L. Chambon, @eloizablocki.bsky.social, @alexandreboulch.bsky.social, M. Chen, M. Cord
📄: arxiv.org/abs/2502.05040
tl;dr: a module for 3D occupancy learning that enforces 2D-3D consistency through differentiable Gaussian rendering
by: L. Chambon, @eloizablocki.bsky.social, @alexandreboulch.bsky.social, M. Chen, M. Cord
📄: arxiv.org/abs/2502.05040
Our recent research will be presented at @iccv.bsky.social! #ICCV2025
We’ll present 5 papers about:
💡 self-supervised & representation learning
🌍 3D occupancy & multi-sensor perception
🧩 open-vocabulary segmentation
🧠 multimodal LLMs & explainability
valeoai.github.io/posts/iccv-2...
We’ll present 5 papers about:
💡 self-supervised & representation learning
🌍 3D occupancy & multi-sensor perception
🧩 open-vocabulary segmentation
🧠 multimodal LLMs & explainability
valeoai.github.io/posts/iccv-2...
October 17, 2025 at 10:10 PM
Our recent research will be presented at @iccv.bsky.social! #ICCV2025
We’ll present 5 papers about:
💡 self-supervised & representation learning
🌍 3D occupancy & multi-sensor perception
🧩 open-vocabulary segmentation
🧠 multimodal LLMs & explainability
valeoai.github.io/posts/iccv-2...
We’ll present 5 papers about:
💡 self-supervised & representation learning
🌍 3D occupancy & multi-sensor perception
🧩 open-vocabulary segmentation
🧠 multimodal LLMs & explainability
valeoai.github.io/posts/iccv-2...
“Has anyone heard about DUSt3R?”
All hands and hearts up in the room.
Honored to welcome @gabrielacsurka.bsky.social today to speak about the amazing work @naverlabseurope.bsky.social towards 3D Foundation Models
All hands and hearts up in the room.
Honored to welcome @gabrielacsurka.bsky.social today to speak about the amazing work @naverlabseurope.bsky.social towards 3D Foundation Models
October 6, 2025 at 12:38 PM
“Has anyone heard about DUSt3R?”
All hands and hearts up in the room.
Honored to welcome @gabrielacsurka.bsky.social today to speak about the amazing work @naverlabseurope.bsky.social towards 3D Foundation Models
All hands and hearts up in the room.
Honored to welcome @gabrielacsurka.bsky.social today to speak about the amazing work @naverlabseurope.bsky.social towards 3D Foundation Models
It’s PhD graduation season in the team!
Today, @bjoernmichele.bsky.social is defending his PhD on "Domain Adaptation for 3D Data"
Best of luck! 🚀
Today, @bjoernmichele.bsky.social is defending his PhD on "Domain Adaptation for 3D Data"
Best of luck! 🚀
October 6, 2025 at 12:09 PM
It’s PhD graduation season in the team!
Today, @bjoernmichele.bsky.social is defending his PhD on "Domain Adaptation for 3D Data"
Best of luck! 🚀
Today, @bjoernmichele.bsky.social is defending his PhD on "Domain Adaptation for 3D Data"
Best of luck! 🚀
PPT: Pretraining with Pseudo-Labeled Trajectories for Motion Forecasting
📄 Paper: arxiv.org/abs/2412.06491
by Y. Xu, @yuanyinnn.bsky.social, @eloizablocki.bsky.social, @tuanhungvu.bsky.social , @alexandreboulch.bsky.social, M. Cord
📄 Paper: arxiv.org/abs/2412.06491
by Y. Xu, @yuanyinnn.bsky.social, @eloizablocki.bsky.social, @tuanhungvu.bsky.social , @alexandreboulch.bsky.social, M. Cord
September 24, 2025 at 5:11 PM
PPT: Pretraining with Pseudo-Labeled Trajectories for Motion Forecasting
📄 Paper: arxiv.org/abs/2412.06491
by Y. Xu, @yuanyinnn.bsky.social, @eloizablocki.bsky.social, @tuanhungvu.bsky.social , @alexandreboulch.bsky.social, M. Cord
📄 Paper: arxiv.org/abs/2412.06491
by Y. Xu, @yuanyinnn.bsky.social, @eloizablocki.bsky.social, @tuanhungvu.bsky.social , @alexandreboulch.bsky.social, M. Cord
VaViM & VaVAM: Autonomous Driving through Video Generative Modeling
🔗 Project page: valeoai.github.io/vavim-vavam/
📄 Paper: arxiv.org/abs/2502.15672
💻 Code: github.com/valeoai/Vide...
by F. Bartoccioni, E. Ramzi, et al.
🔗 Project page: valeoai.github.io/vavim-vavam/
📄 Paper: arxiv.org/abs/2502.15672
💻 Code: github.com/valeoai/Vide...
by F. Bartoccioni, E. Ramzi, et al.
September 24, 2025 at 5:11 PM
VaViM & VaVAM: Autonomous Driving through Video Generative Modeling
🔗 Project page: valeoai.github.io/vavim-vavam/
📄 Paper: arxiv.org/abs/2502.15672
💻 Code: github.com/valeoai/Vide...
by F. Bartoccioni, E. Ramzi, et al.
🔗 Project page: valeoai.github.io/vavim-vavam/
📄 Paper: arxiv.org/abs/2502.15672
💻 Code: github.com/valeoai/Vide...
by F. Bartoccioni, E. Ramzi, et al.
CoRL 2025 is just around the corner in Seoul, Korea!
🤖 🚗
We're excited to present our latest research and connect with the community.
#CoRL2025
🤖 🚗
We're excited to present our latest research and connect with the community.
#CoRL2025
September 24, 2025 at 4:47 PM
CoRL 2025 is just around the corner in Seoul, Korea!
🤖 🚗
We're excited to present our latest research and connect with the community.
#CoRL2025
🤖 🚗
We're excited to present our latest research and connect with the community.
#CoRL2025
Just back from CVPR@Paris 🥐, what a fantastic event!
Great talks, great posters, and great to connect with the French & European vision community.
Kudos to the organizers, hoping that it returns next year! 🤞
#CVPR2025 @cvprconference.bsky.social
Great talks, great posters, and great to connect with the French & European vision community.
Kudos to the organizers, hoping that it returns next year! 🤞
#CVPR2025 @cvprconference.bsky.social
June 6, 2025 at 5:41 PM
Just back from CVPR@Paris 🥐, what a fantastic event!
Great talks, great posters, and great to connect with the French & European vision community.
Kudos to the organizers, hoping that it returns next year! 🤞
#CVPR2025 @cvprconference.bsky.social
Great talks, great posters, and great to connect with the French & European vision community.
Kudos to the organizers, hoping that it returns next year! 🤞
#CVPR2025 @cvprconference.bsky.social
