Search Results for author:
Found 23 papers, 19 papers with code
Active 3D Shape Reconstruction from Vision and Touch
In this paper, we focus on this problem and introduce a system composed of: 1) a haptic simulator leveraging high spatial resolution vision-based tactile sensors for active touching of 3D objects; 2)a mesh-based 3D shape reconstruction model that relies on tactile or visuotactile signals; and 3) a set of data-driven solutions with either tactile or visuotactile priors to guide the shape exploration.
Active MR k-space Sampling with Reinforcement Learning
Deep learning approaches have recently shown great promise in accelerating magnetic resonance image (MRI) acquisition.
3D Shape Reconstruction from Vision and Touch
When a toddler is presented a new toy, their instinctual behaviour is to pick it upand inspect it with their hand and eyes in tandem, clearly searching over its surface to properly understand what they are playing with.
Post-Workshop Report on Science meets Engineering in Deep Learning, NeurIPS 2019, Vancouver
Science meets Engineering in Deep Learning took place in Vancouver as part of the Workshop section of NeurIPS 2019.
Learning to adapt class-specific features across domains for semantic segmentation
Recent advances in unsupervised domain adaptation have shown the effectiveness of adversarial training to adapt features across domains, endowing neural networks with the capability of being tested on a target domain without requiring any training annotations in this domain.
On the Evaluation of Conditional GANs
We show that FJD can be used as a promising single metric for cGAN benchmarking and model selection.
Elucidating image-to-set prediction: An analysis of models, losses and datasets
In this paper, we identify an important reproducibility challenge in the image-to-set prediction literature that impedes proper comparisons among published methods, namely, researchers use different evaluation protocols to assess their contributions.
Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition
The goal of MRI reconstruction is to restore a high fidelity image from partially observed measurements.
GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects
Mesh models are a promising approach for encoding the structure of 3D objects.
Ranked #1 on
3D Object Reconstruction
on Data3D−R2N2
(Avg F1 metric)
Inverse Cooking: Recipe Generation from Food Images
Our system predicts ingredients as sets by means of a novel architecture, modeling their dependencies without imposing any order, and then generates cooking instructions by attending to both image and its inferred ingredients simultaneously.
Ranked #1 on
Recipe Generation
on Recipe1M
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive.
On the iterative refinement of densely connected representation levels for semantic segmentation
State-of-the-art semantic segmentation approaches increase the receptive field of their models by using either a downsampling path composed of poolings/strided convolutions or successive dilated convolutions.
Graph Attention Networks
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Image Segmentation by Iterative Inference from Conditional Score Estimation
We experimentally find that the proposed iterative inference from conditional score estimation by conditional denoising autoencoders performs better than comparable models based on CRFs or those not using any explicit modeling of the conditional joint distribution of outputs.
Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation
Moreover, when applying our 2D pipeline on a challenging 3D MRI prostate segmentation challenge we reach results that are competitive even when compared to 3D methods.
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images
Colorectal cancer (CRC) is the third cause of cancer death worldwide.
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs).
Ranked #8 on
Semantic Segmentation
on CamVid
Diet Networks: Thin Parameters for Fat Genomics
It is based on the idea that we can first learn or provide a distributed representation for each input feature (e. g. for each position in the genome where variations are observed), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units).
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification
This paper introduces the use of single layer and deep convolutional networks for remote sensing data analysis.
ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation
Moreover, ReNet layers are stacked on top of pre-trained convolutional layers, benefiting from generic local features.
Ranked #17 on
Semantic Segmentation
on CamVid
FitNets: Hints for Thin Deep Nets
In this paper, we extend this idea to allow the training of a student that is deeper and thinner than the teacher, using not only the outputs but also the intermediate representations learned by the teacher as hints to improve the training process and final performance of the student.
No more meta-parameter tuning in unsupervised sparse feature learning
We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity.
Ranked #102 on
Image Classification
on STL-10
