Search Results for author:
Found 21 papers, 6 papers with code
Using Motion Cues to Supervise Single-Frame Body Pose and Shape Estimation in Low Data Regimes
When enough annotated training data is available, supervised deep-learning algorithms excel at estimating human body pose and shape using a single camera.
A Phase-Coded Time-Domain Interleaved OTFS Waveform with Improved Ambiguity Function
To enhance the sensing performance of the orthogonal time frequency space (OTFS) waveform, we propose a novel time-domain interleaved cyclic-shifted P4-coded OTFS (TICP4-OTFS) with improved ambiguity function.
Data-Consistent Non-Cartesian Deep Subspace Learning for Efficient Dynamic MR Image Reconstruction
Non-Cartesian sampling with subspace-constrained image reconstruction is a popular approach to dynamic MRI, but slow iterative reconstruction limits its clinical application.
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a highly labor-intensive process.
Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation
Specifically, we show that: (1) our approach improves performance on all tasks when they are complementary and mutually dependent; (2) the CTRL helps to improve both semantic segmentation and depth estimation tasks performance in the challenging UDA setting; (3) the proposed ISL training scheme further improves the semantic segmentation performance.
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation
Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a highly labor-intensive process.
Ranked #4 on
Semi-Supervised Semantic Segmentation
on Cityscapes 100 samples labeled
(using extra training data)
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation
Open compound domain adaptation (OCDA) is a domain adaptation setting, where target domain is modeled as a compound of multiple unknown homogeneous domains, which brings the advantage of improved generalization to unseen domains.
mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets
One challenge of object recognition is to generalize to new domains, to more classes and/or to new modalities.
Consistency Guided Scene Flow Estimation
To handle inherent modeling error in the consistency loss (e. g. Lambertian assumptions) and for better generalization, we further introduce a learned, output refinement network, which takes the initial predictions, the loss, and the gradient as input, and efficiently predicts a correlated output update.
MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better
High-resolution (HR) magnetic resonance imaging (MRI) provides detailed anatomical information that is critical for diagnosis in the clinical application.
Fully Automated Multi-Organ Segmentation in Abdominal Magnetic Resonance Imaging with Deep Neural Networks
The model takes in multi-slice MR images and generates the output of segmentation results.
Deep learning within a priori temporal feature spaces for large-scale dynamic MR image reconstruction: Application to 5-D cardiac MR Multitasking
High spatiotemporal resolution dynamic magnetic resonance imaging (MRI) is a powerful clinical tool for imaging moving structures as well as to reveal and quantify other physical and physiological dynamics.
Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera
We present GLNet, a self-supervised framework for learning depth, optical flow, camera pose and intrinsic parameters from monocular video - addressing the difficulty of acquiring realistic ground-truth for such tasks.
Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach
In this work, we take the advantage of additional geometric information from synthetic data, a powerful yet largely neglected cue, to bridge the domain gap.
Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning
This paper tackles the problem of video object segmentation, given some user annotation which indicates the object of interest.
Ranked #66 on
Semi-Supervised Video Object Segmentation
on DAVIS 2016
Domain Adaptive Faster R-CNN for Object Detection in the Wild
The results demonstrate the effectiveness of our proposed approach for robust object detection in various domain shift scenarios.
Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network
High-resolution (HR) magnetic resonance images (MRI) provide detailed anatomical information important for clinical application and quantitative image analysis.
Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks
Magnetic resonance image (MRI) in high spatial resolution provides detailed anatomical information and is often necessary for accurate quantitative analysis.
ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes
To this end, we propose a new reality oriented adaptation approach for urban scene semantic segmentation by learning from synthetic data.
Scale-Aware Alignment of Hierarchical Image Segmentation
To demonstrate the power of our method, we perform comprehensive experiments, which show that our method, as a post-processing step, can significantly improve the quality of the hierarchical segmentation representations, and ease the usage of hierarchical image segmentation to high-level vision tasks such as object segmentation.
Is Image Super-resolution Helpful for Other Vision Tasks?
In this paper, we present the first comprehensive study and analysis of the usefulness of ISR for other vision applications.
