Search Results for author:
Found 17 papers, 3 papers with code
FastCAD: Real-Time CAD Retrieval and Alignment from Scans and Videos
In contrast to previous works, we directly predict alignment parameters and shape embeddings.
InterroGate: Learning to Share, Specialize, and Prune Representations for Multi-task Learning
In this work, we propose \textit{InterroGate}, a novel multi-task learning (MTL) architecture designed to mitigate task interference while optimizing inference computational efficiency.
PVP: Personalized Video Prior for Editable Dynamic Portraits using StyleGAN
In this work, our goal is to take as input a monocular video of a face, and create an editable dynamic portrait able to handle extreme head poses.
3D Distillation: Improving Self-Supervised Monocular Depth Estimation on Reflective Surfaces
Self-supervised monocular depth estimation (SSMDE) aims at predicting the dense depth maps of monocular images, by learning to minimize a photometric loss using spatially neighboring image pairs during training.
DG-Recon: Depth-Guided Neural 3D Scene Reconstruction
A key challenge in neural 3D scene reconstruction from monocular images is to fuse features back projected from various views without any depth or occlusion information.
Multi-Task Edge Prediction in Temporally-Dynamic Video Graphs
In such temporally-dynamic graphs, a core problem is inferring the future state of spatio-temporal edges, which can constitute multiple types of relations.
Find it if You Can: End-to-End Adversarial Erasing for Weakly-Supervised Semantic Segmentation
Semantic segmentation is a task that traditionally requires a large dataset of pixel-level ground truth labels, which is time-consuming and expensive to obtain.
Ranked #25 on
Semantic Segmentation
on PASCAL VOC 2012 val
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
3D Convolutional Neural Networks Image Registration Based on Efficient Supervised Learning from Artificial Deformations
We propose a supervised nonrigid image registration method, trained using artificial displacement vector fields (DVF), for which we propose and compare three network architectures.
I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation
Adversarial training has been recently employed for realizing structured semantic segmentation, in which the aim is to preserve higher-level scene structural consistencies in dense predictions.
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Segmentation methods had to be containerized and submitted to the challenge organizers.
Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CT
Various approaches for liver segmentation in CT have been proposed: Besides statistical shape models, which played a major role in this research area, novel approaches on the basis of convolutional neural networks have been introduced recently.
EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection
Convolutional neural networks have been successfully applied to semantic segmentation problems.
Ranked #15 on
Lane Detection
on TuSimple
Student Beats the Teacher: Deep Neural Networks for Lateral Ventricles Segmentation in Brain MR
To show this, we use noisy segmentation labels generated by a conventional region growing algorithm to train a deep network for lateral ventricle segmentation.
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation
In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images.
Deep Multi-scale Location-aware 3D Convolutional Neural Networks for Automated Detection of Lacunes of Presumed Vascular Origin
In this paper, we propose an automated two-stage method using deep convolutional neural networks (CNN).
Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities
The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks.
