Search Results for author:
Found 21 papers, 2 papers with code
Physically-admissible polarimetric data augmentation for road-scene analysis
Polarimetric imaging, along with deep learning, has shown improved performances on different tasks including scene analysis.
A formal approach to good practices in Pseudo-Labeling for Unsupervised Domain Adaptive Re-Identification
(ii) General good practices for Pseudo-Labeling, directly deduced from the interpretation of the proposed theoretical framework, in order to improve the target re-ID performance.
Similarity Contrastive Estimation for Self-Supervised Soft Contrastive Learning
To circumvent this issue, we propose a novel formulation of contrastive learning using semantic similarity between instances called Similarity Contrastive Estimation (SCE).
Ranked #70 on
Self-Supervised Image Classification
on ImageNet
Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities
Based on the fact that there is a strong correlation between MR modalities of the same patient, in this work, we propose a novel brain tumor segmentation network in the case of missing one or more modalities.
A Tri-attention Fusion Guided Multi-modal Segmentation Network
Considering the correlation between different MR modalities, in this paper, we propose a multi-modality segmentation network guided by a novel tri-attention fusion.
Improving Unsupervised Domain Adaptive Re-Identification via Source-Guided Selection of Pseudo-Labeling Hyperparameters
However, the effectiveness of these approaches heavily depends on the choice of some hyperparameters (HP) that affect the generation of pseudo-labels by clustering.
Conditional generator and multi-sourcecorrelation guided brain tumor segmentation with missing MR modalities
The proposed network consists of a conditional generator, a correlation constraint network and a segmentation network.
Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities
In addition, multi-modal MR images can provide complementary information for accurate brain tumor segmentation.
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint
Our network includes N model-independent encoding paths with N image sources, a correlation constraint block, a feature fusion block, and a decoding path.
A review: Deep learning for medical image segmentation using multi-modality fusion
Due to their self-learning and generalization ability over large amounts of data, deep learning recently has also gained great interest in multi-modal medical image segmentation.
An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism
The coronavirus disease (COVID-19) pandemic has led to a devastating effect on the global public health.
Brain tumor segmentation with missing modalities via latent multi-source correlation representation
Multimodal MR images can provide complementary information for accurate brain tumor segmentation.
Doc2Vec on the PubMed corpus: study of a new approach to generate related articles
The terminological indexing, words and stems contents of linked documents are highly similar between pmra and Doc2Vec PV-DBOW architecture.
Road scenes analysis in adverse weather conditions by polarization-encoded images and adapted deep learning
The efficiency of the proposed method is mostly due to the high power of the polarimetry to discriminate any object by its reflective properties and on the use of deep neural networks for object detection.
Kernels on fuzzy sets: an overview
This paper introduces the concept of kernels on fuzzy sets as a similarity measure for $[0, 1]$-valued functions, a. k. a.
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning
The second agent acts as the interaction protocol, interpreting and translating to 3D operations the 2D finger trajectories from the first agent.
Learning to recognize touch gestures: recurrent vs. convolutional features and dynamic sampling
We propose a fully automatic method for learning gestures on big touch devices in a potentially multi-user context.
A First Derivative Potts Model for Segmentation and Denoising Using ILP
Unsupervised image segmentation and denoising are two fundamental tasks in image processing.
Une véritable approche $\ell_0$ pour l'apprentissage de dictionnaire
Sparse representation learning has recently gained a great success in signal and image processing, thanks to recent advances in dictionary learning.
Operator-valued Kernels for Learning from Functional Response Data
In this paper we consider the problems of supervised classification and regression in the case where attributes and labels are functions: a data is represented by a set of functions, and the label is also a function.
Support Vector Machines with a Reject Option
We consider the problem of binary classification where the classifier may abstain instead of classifying each observation.
