Search Results for author:
Found 34 papers, 19 papers with code
BEVContrast: Self-Supervision in BEV Space for Automotive Lidar Point Clouds
We present a surprisingly simple and efficient method for self-supervision of 3D backbone on automotive Lidar point clouds.
Three Pillars improving Vision Foundation Model Distillation for Lidar
In particular, thanks to our scalable distillation method named ScaLR, we show that scaling the 2D and 3D backbones and pretraining on diverse datasets leads to a substantial improvement of the feature quality.
Unsupervised Object Localization in the Era of Self-Supervised ViTs: A Survey
We propose here a survey of unsupervised object localization methods that discover objects in images without requiring any manual annotation in the era of self-supervised ViTs.
You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic Segmentation
Assuming that images of the point clouds are available, which is common, our method relies on powerful unsupervised image features to measure the diversity of the point clouds.
SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation
Learning models on one labeled dataset that generalize well on another domain is a difficult task, as several shifts might happen between the data domains.
Using a Waffle Iron for Automotive Point Cloud Semantic Segmentation
Semantic segmentation of point clouds in autonomous driving datasets requires techniques that can process large numbers of points efficiently.
Ranked #4 on
LIDAR Semantic Segmentation
on nuScenes
(val mIoU metric)
RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving
(c) We refine pixel-wise predictions with a convolutional decoder and a skip connection from the convolutional stem to combine low-level but fine-grained features of the the convolutional stem with the high-level but coarse predictions of the ViT encoder.
Unsupervised Object Localization: Observing the Background to Discover Objects
This way, the salient objects emerge as a by-product without any strong assumption on what an object should be.
ALSO: Automotive Lidar Self-supervision by Occupancy estimation
The core idea is to train the model on a pretext task which is the reconstruction of the surface on which the 3D points are sampled, and to use the underlying latent vectors as input to the perception head.
Take One Gram of Neural Features, Get Enhanced Group Robustness
Predictive performance of machine learning models trained with empirical risk minimization (ERM) can degrade considerably under distribution shifts.
Self-supervised learning with rotation-invariant kernels
We introduce a regularization loss based on kernel mean embeddings with rotation-invariant kernels on the hypersphere (also known as dot-product kernels) for self-supervised learning of image representations.
Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data
In this context, we propose a self-supervised pre-training method for 3D perception models that is tailored to autonomous driving data.
NeeDrop: Self-supervised Shape Representation from Sparse Point Clouds using Needle Dropping
There has been recently a growing interest for implicit shape representations.
FacialFilmroll: High-resolution multi-shot video editing
We present FacialFilmroll, a solution for spatially and temporally consistent editing of faces in one or multiple shots.
PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds
Rigid registration of point clouds with partial overlaps is a longstanding problem usually solved in two steps: (a) finding correspondences between the point clouds; (b) filtering these correspondences to keep only the most reliable ones to estimate the transformation.
Localizing Objects with Self-Supervised Transformers and no Labels
We also show that training a class-agnostic detector on the discovered objects boosts results by another 7 points.
Ranked #4 on
Weakly-Supervised Object Localization
on CUB-200-2011
(Top-1 Localization Accuracy metric)
Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds
While there has been a number of studies on Zero-Shot Learning (ZSL) for 2D images, its application to 3D data is still recent and scarce, with just a few methods limited to classification.
Ranked #1 on
Generalized Zero-Shot Learning
on ScanNet
OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning
With this in mind, we propose a teacher-student scheme to learn representations by training a convolutional net to reconstruct a bag-of-visual-words (BoW) representation of an image, given as input a perturbed version of that same image.
Ranked #18 on
Semi-Supervised Image Classification
on ImageNet - 1% labeled data
(Top 5 Accuracy metric)
FLOT: Scene Flow on Point Clouds Guided by Optimal Transport
Our main finding is that FLOT can perform as well as the best existing methods on synthetic and real-world datasets while requiring much less parameters and without using multiscale analysis.
Photo style transfer with consistency losses
We address the problem of style transfer between two photos and propose a new way to preserve photorealism.
FKAConv: Feature-Kernel Alignment for Point Cloud Convolution
Recent state-of-the-art methods for point cloud processing are based on the notion of point convolution, for which several approaches have been proposed.
Ranked #1 on
LIDAR Semantic Segmentation
on Paris-Lille-3D
Scattering Features for Multimodal Gait Recognition
We consider the problem of identifying people on the basis of their walk (gait) pattern.
A Flexible Convolutional Solver for Fast Style Transfers
We propose a new flexible deep convolutional neural network (convnet) to perform fast neural style transfers.
A Flexible Convolutional Solver with Application to Photorealistic Style Transfer
In contrast to existing convnets that address the same task, our architecture derives directly from the structure of the gradient descent originally used to solve the style transfer problem [Gatys et al., 2016].
Unifying local and non-local signal processing with graph CNNs
This paper deals with the unification of local and non-local signal processing on graphs within a single convolutional neural network (CNN) framework.
Compressive Spectral Clustering
Spectral clustering has become a popular technique due to its high performance in many contexts.
Compressive PCA for Low-Rank Matrices on Graphs
We introduce a novel framework for an approxi- mate recovery of data matrices which are low-rank on graphs, from sampled measurements.
Random sampling of bandlimited signals on graphs
On the contrary, the second strategy is adaptive but yields optimal results.
Accelerated Spectral Clustering Using Graph Filtering Of Random Signals
We build upon recent advances in graph signal processing to propose a faster spectral clustering algorithm.
Social and Information Networks Numerical Analysis
Fast Robust PCA on Graphs
Clustering experiments on 7 benchmark datasets with different types of corruptions and background separation experiments on 3 video datasets show that our proposed model outperforms 10 state-of-the-art dimensionality reduction models.
Balancing Sparsity and Rank Constraints in Quadratic Basis Pursuit
We investigate the methods that simultaneously enforce sparsity and low-rank structure in a matrix as often employed for sparse phase retrieval problems or phase calibration problems in compressive sensing.
A Compressed Sensing Framework for Magnetic Resonance Fingerprinting
Inspired by the recently proposed Magnetic Resonance Fingerprinting (MRF) technique, we develop a principled compressed sensing framework for quantitative MRI.
Information Theory Information Theory
Robust image reconstruction from multi-view measurements
The background image is common to all observed images but undergoes geometric transformations, as the scene is observed from different viewpoints.
