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Found 52 papers, 3 papers with code
BodyPrint: Pose Invariant 3D Shape Matching of Human Bodies
3D human body shape matching has large potential on many real world applications, especially with the recent advances in the 3D range sensing technology.
Deep Decision Network for Multi-Class Image Classification
During the learning phase, starting from the root network node, DDN automatically builds a network that splits the data into disjoint clusters of classes which would be handled by the subsequent expert networks.
DepthSynth: Real-Time Realistic Synthetic Data Generation from CAD Models for 2.5D Recognition
Recent progress in computer vision has been dominated by deep neural networks trained over large amounts of labeled data.
Generating Synthetic X-ray Images of a Person from the Surface Geometry
Furthermore, the synthetic X-ray image is parametrized and can be manipulated by adjusting a set of body markers which are also generated during the X-ray image prediction.
Structure-Aware Shape Synthesis
We propose a new procedure to guide training of a data-driven shape generative model using a structure-aware loss function.
Visual Similarity Attention
While there has been substantial progress in learning suitable distance metrics, these techniques in general lack transparency and decision reasoning, i. e., explaining why the input set of images is similar or dissimilar.
Pyramid Convolutional RNN for MRI Image Reconstruction
We evaluate our model on the fastMRI knee and brain datasets and the results show that the proposed model outperforms other methods and can recover more details.
Hierarchical Kinematic Human Mesh Recovery
In this work, we address this gap by proposing a new technique for regression of human parametric model that is explicitly informed by the known hierarchical structure, including joint interdependencies of the model.
FOAL: Fast Online Adaptive Learning for Cardiac Motion Estimation
In clinical deployment, however, they suffer dramatic performance drops due to mismatched distributions between training and testing datasets, commonly encountered in the clinical environment.
MRI Image Reconstruction via Learning Optimization Using Neural ODEs
We investigate several models based on three ODE solvers and compare models with fixed solvers and learned solvers.
Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation
In this paper, we propose Motion Pyramid Networks, a novel deep learning-based approach for accurate and efficient cardiac motion estimation.
Real-Time Cardiac Cine MRI with Residual Convolutional Recurrent Neural Network
Real-time cardiac cine MRI does not require ECG gating in the data acquisition and is more useful for patients who can not hold their breaths or have abnormal heart rhythms.
Towards Visually Explaining Similarity Models
We show that the resulting similarity models perform, and can be visually explained, better than the corresponding baseline models trained without these constraints.
Anatomy-Aware Cardiac Motion Estimation
A baseline dense motion tracker is trained to approximate the motion fields and then refined to estimate anatomy-aware motion fields under the weak supervision from the VAE.
Measure Anatomical Thickness from Cardiac MRI with Deep Neural Networks
We also analyze thickness patterns on different cardiac pathologies with a standard clinical model and the results demonstrate the potential clinical value of our method for thickness based cardiac disease diagnosis.
Ensemble Attention Distillation for Privacy-Preserving Federated Learning
Such decentralized training naturally leads to issues of imbalanced or differing data distributions among the local models and challenges in fusing them into a central model.
A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts
Despite substantial progress in applying neural networks (NN) to a wide variety of areas, they still largely suffer from a lack of transparency and interpretability.
Cardiac Functional Analysis with Cine MRI via Deep Learning Reconstruction
To the best of our knowledge, this is the first work to evaluate the cine MRI with deep learning reconstruction for cardiac function analysis and compare it with other conventional methods.
Accelerating 3D MULTIPLEX MRI Reconstruction with Deep Learning
We propose a deep learning framework for undersampled 3D MRI data reconstruction and apply it to MULTIPLEX MRI.
Multi-scale Neural ODEs for 3D Medical Image Registration
The inference consists of iterative gradient updates similar to a conventional gradient descent optimizer but in a much faster way, because the neural ODE learns from the training data to adapt the gradient efficiently at each iteration.
Everybody Is Unique: Towards Unbiased Human Mesh Recovery
Next, we present a simple baseline to address this problem that is scalable and can be easily used in conjunction with existing algorithms to improve their performance.
Ranked #1 on
3D Human Shape Estimation
on SSP-3D
(PVE-T metric)
Spatio-Temporal Representation Factorization for Video-based Person Re-Identification
To alleviate these problems, we propose Spatio-Temporal Representation Factorization (STRF), a flexible new computational unit that can be used in conjunction with most existing 3D convolutional neural network architectures for re-ID.
Ranked #2 on
Person Re-Identification
on DukeMTMC-VideoReID
Learning Local Recurrent Models for Human Mesh Recovery
We conduct a variety of experiments on standard video mesh recovery benchmark datasets such as Human3. 6M, MPI-INF-3DHP, and 3DPW, demonstrating the efficacy of our design of modeling local dynamics as well as establishing state-of-the-art results based on standard evaluation metrics.
Ranked #45 on
3D Human Pose Estimation
on 3DPW
Learning Hierarchical Attention for Weakly-supervised Chest X-Ray Abnormality Localization and Diagnosis
However, doing this accurately will require a large amount of disease localization annotations by clinical experts, a task that is prohibitively expensive to accomplish for most applications.
SMPL-A: Modeling Person-Specific Deformable Anatomy
In order to obtain accurate target location information, clinicians have to either conduct frequent intraoperative scans, resulting in higher exposition of patients to radiations, or adopt proxy procedures (e. g., creating and using custom molds to keep patients in the exact same pose during both preoperative organ scanning and subsequent treatment.
Invertible Sharpening Network for MRI Reconstruction Enhancement
During inference, the learned blurring transform can be inverted to a sharpening transform leveraging the network's invertibility.
PseudoClick: Interactive Image Segmentation with Click Imitation
The goal of click-based interactive image segmentation is to obtain precise object segmentation masks with limited user interaction, i. e., by a minimal number of user clicks.
Deep Statistic Shape Model for Myocardium Segmentation
Additionally, the predicted point cloud guarantees boundary correspondence for sequential images, which contributes to the downstream tasks, such as the motion estimation of myocardium.
Self-supervised Human Mesh Recovery with Cross-Representation Alignment
However, on synthetic dense correspondence maps (i. e., IUV) few have been explored since the domain gap between synthetic training data and real testing data is hard to address for 2D dense representation.
Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation
Federated Learning (FL) is a machine learning paradigm where local nodes collaboratively train a central model while the training data remains decentralized.
PREF: Predictability Regularized Neural Motion Fields
We propose to regularize the estimated motion to be predictable.
Federated Learning with Privacy-Preserving Ensemble Attention Distillation
We propose a privacy-preserving FL framework leveraging unlabeled public data for one-way offline knowledge distillation in this work.
Forecasting Human Trajectory from Scene History
We categorize scene history information into two types: historical group trajectory and individual-surroundings interaction.
JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network for Multi-contrast MRI
The proposed framework consists of a sampling mask generator for each image contrast and a reconstructor exploiting the inter-contrast correlations with a recurrent structure which enables the information sharing in a holistic way.
Progressive Multi-view Human Mesh Recovery with Self-Supervision
To date, little attention has been given to multi-view 3D human mesh estimation, despite real-life applicability (e. g., motion capture, sport analysis) and robustness to single-view ambiguities.
Holistic Multi-Slice Framework for Dynamic Simultaneous Multi-Slice MRI Reconstruction
Deep learning (DL)-based methods have shown promising results for single-slice MR reconstruction, but the addition of SMS acceleration raises unique challenges due to the composite k-space signals and the resulting images with strong inter-slice artifacts.
Computationally Efficient 3D MRI Reconstruction with Adaptive MLP
Recon3DMLP improves HR 3D reconstruction and outperforms several existing CNN-based models under similar GPU memory consumption, which demonstrates that Recon3DMLP is a practical solution for HR 3D MRI reconstruction.
An Unsupervised Framework for Joint MRI Super Resolution and Gibbs Artifact Removal
Previous studies tackled these two problems separately, where super resolution methods tend to enhance Gibbs artifacts, whereas Gibbs ringing removal methods tend to blur the images.
Exploring Cycle Consistency Learning in Interactive Volume Segmentation
Given a medical volume, a user first segments a slice (or several slices) via the interaction module and then propagates the segmentation(s) to the remaining slices.
Disguise without Disruption: Utility-Preserving Face De-Identification
We extensively evaluate our method using multiple datasets, demonstrating a higher de-identification rate and superior consistency compared to prior approaches in various downstream tasks.
Enhancing Cardiac MRI Segmentation via Classifier-Guided Two-Stage Network and All-Slice Information Fusion Transformer
Cardiac Magnetic Resonance imaging (CMR) is the gold standard for assessing cardiac function.
Implicit Modeling of Non-rigid Objects with Cross-Category Signals
To effectively capture the interrelation between these entities and ensure precise, collision-free representations, our approach facilitates signaling between category-specific fields to adequately rectify shapes.
PBADet: A One-Stage Anchor-Free Approach for Part-Body Association
The detection of human parts (e. g., hands, face) and their correct association with individuals is an essential task, e. g., for ubiquitous human-machine interfaces and action recognition.
DaReNeRF: Direction-aware Representation for Dynamic Scenes
However, the straightforward decomposition of 4D dynamic scenes into multiple 2D plane-based representations proves insufficient for re-rendering high-fidelity scenes with complex motions.
Self-supervised 3D Patient Modeling with Multi-modal Attentive Fusion
3D patient body modeling is critical to the success of automated patient positioning for smart medical scanning and operating rooms.
Automating Catheterization Labs with Real-Time Perception
For decades, three-dimensional C-arm Cone-Beam Computed Tomography (CBCT) imaging system has been a critical component for complex vascular and nonvascular interventional procedures.
Federated Data Model
In artificial intelligence (AI), especially deep learning, data diversity and volume play a pivotal role in model development.
Spatiotemporal Diffusion Model with Paired Sampling for Accelerated Cardiac Cine MRI
Current deep learning reconstruction for accelerated cardiac cine MRI suffers from spatial and temporal blurring.
Clinically Feasible Diffusion Reconstruction for Highly-Accelerated Cardiac Cine MRI
The currently limited quality of accelerated cardiac cine reconstruction may potentially be improved by the emerging diffusion models, but the clinically unacceptable long processing time poses a challenge.
Human Mesh Recovery from Arbitrary Multi-view Images
Human mesh recovery from arbitrary multi-view images involves two characteristics: the arbitrary camera poses and arbitrary number of camera views.
Self-learning Canonical Space for Multi-view 3D Human Pose Estimation
To facilitate the aggregation of the intra- and inter-view, we define a canonical parameter space, depicted by per-view camera pose and human pose and shape parameters ($\theta$ and $\beta$) of SMPL model, and propose a two-stage learning procedure.
