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Found 41 papers, 16 papers with code
Sub-center ArcFace: Boosting Face Recognition by Large-scale Noisy Web Faces
In this paper, we relax the intra-class constraint of ArcFace to improve the robustness to label noise.
3D Masked Autoencoding and Pseudo-labeling for Domain Adaptive Segmentation of Heterogeneous Infant Brain MRI
Robust segmentation of infant brain MRI across multiple ages, modalities, and sites remains challenging due to the intrinsic heterogeneity caused by different MRI scanners, vendors, or acquisition sequences, as well as varying stages of neurodevelopment.
Multidimensional Graph Neural Networks for Wireless Communications
Based on the observation that the mismatched permutation property from the policies and the information loss during the update of hidden representations have large impact on the learning performance and efficiency, in this paper we propose a unified framework to learn permutable wireless policies with multidimensional GNNs.
SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation
To this end, we propose a sequence-aware diffusion model (SADM) for the generation of longitudinal medical images.
Weakly-Supervised Gaze Estimation from Synthetic Views
3D gaze estimation is most often tackled as learning a direct mapping between input images and the gaze vector or its spherical coordinates.
A Model-based GNN for Learning Precoding
Simulation results show that the proposed GNN can well learn spectral efficient and energy efficient precoding policies in single- and multi-cell multi-user multi-antenna systems with low training complexity, and can be well generalized to the numbers of users.
Understanding the Performance of Learning Precoding Policy with GNN and CNNs
Learning-based precoding has been shown able to be implemented in real-time, jointly optimized with channel acquisition, and robust to imperfect channels.
Brain MRI-to-PET Synthesis using 3D Convolutional Attention Networks
Such methods may enable more widespread and accurate CBF evaluation in larger cohorts who cannot undergo PET imaging due to radiation concerns, lack of access, or logistic challenges.
A Novel Adaptive Causal Sampling Method for Physics-Informed Neural Networks
We point out that it is not sufficient to only consider the residual loss in adaptive sampling and sampling should obey temporal causality.
Perspective Reconstruction of Human Faces by Joint Mesh and Landmark Regression
Even though 3D face reconstruction has achieved impressive progress, most orthogonal projection-based face reconstruction methods can not achieve accurate and consistent reconstruction results when the face is very close to the camera due to the distortion under the perspective projection.
Detecting Schizophrenia with 3D Structural Brain MRI Using Deep Learning
Our finding corroborates that schizophrenia is associated with widespread alterations in subcortical brain structure and the subcortical structural information provides prominent features in diagnostic classification.
WT-MVSNet: Window-based Transformers for Multi-view Stereo
In this work, we propose Window-based Transformers (WT) for local feature matching and global feature aggregation in multi-view stereo.
Killing Two Birds with One Stone:Efficient and Robust Training of Face Recognition CNNs by Partial FC
In each iteration, positive class centers and a random subset of negative class centers are selected to compute the margin-based softmax loss.
Ranked #1 on
Face Recognition
on MFR
Koopman Methods for Estimation of Animal Motions over Unknown Submanifolds
It is assumed that motions are supported on a low-dimensional, unknown configuration manifold $Q$ that is regularly embedded in high dimensional Euclidean space $X:=\mathbb{R}^d$.
Multi-task Deep Learning for Cerebrovascular Disease Classification and MRI-to-PET Translation
Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of cerebrovascular diseases such as Moyamoya, carotid stenosis, aneurysms, and stroke.
Improving Across-Dataset Brain Tissue Segmentation Using Transformer
Transformers have demonstrated success in natural image segmentation and have recently been applied to 3D medical image segmentation tasks due to their ability to capture long-distance relationships in the input where the local receptive fields of CNNs struggle.
Killing Two Birds With One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC
In each iteration, positive class centers and a random subset of negative class centers are selected to compute the margin-based softmax loss.
BiQUE: Biquaternionic Embeddings of Knowledge Graphs
In contrast, hyperbolic models are effective at modeling hierarchical relations, but do not perform as well on patterns on which circular rotation excels.
Reducing the Teacher-Student Gap via Adaptive Temperatures
Based on this observation, we found that the sharpness gap between the teacher and student output may cause this degradation problem.
Masked Face Recognition Challenge: The InsightFace Track Report
In this workshop, we organize Masked Face Recognition (MFR) challenge and focus on bench-marking deep face recognition methods under the existence of facial masks.
Masked Face Recognition Challenge: The WebFace260M Track Report
There are second phase of the challenge till October 1, 2021 and on-going leaderboard.
Variational Prototype Learning for Deep Face Recognition
Deep face recognition has achieved remarkable improvements due to the introduction of margin-based softmax loss, in which the prototype stored in the last linear layer represents the center of each class.
Recursive Refinement Network for Deformable Lung Registration between Exhale and Inhale CT Scans
We introduce a recursive refinement network (RRN) for unsupervised medical image registration, to extract multi-scale features, construct normalized local cost correlation volume and recursively refine volumetric deformation vector fields.
Ranked #1 on
Image Registration
on DIR-LAB COPDgene
PTNet: A High-Resolution Infant MRI Synthesizer Based on Transformer
Magnetic resonance imaging (MRI) noninvasively provides critical information about how human brain structures develop across stages of life.
Sample and Computation Redistribution for Efficient Face Detection
Although tremendous strides have been made in uncontrolled face detection, efficient face detection with a low computation cost as well as high precision remains an open challenge.
Deep Learning Identifies Neuroimaging Signatures of Alzheimer's Disease Using Structural and Synthesized Functional MRI Data
Here we propose a potential solution by first learning a structural-to-functional transformation in brain MRI, and further synthesizing spatially matched functional images from large-scale structural scans.
Strictly Decentralized Adaptive Estimation of External Fields using Reproducing Kernels
Approximations of the evolution of the ideal local estimate $\hat{g}^i_t$ of agent $i$ is constructed solely using observations made by agent $i$ on a fine time scale.
Learning Power Control for Cellular Systems with Heterogeneous Graph Neural Network
In this paper, we show that the power control policy has a combination of different PI and PE properties, and existing HetGNN does not satisfy these properties.
Reducing the Teacher-Student Gap via Spherical Knowledge Disitllation
We investigate this problem by study the gap of confidence between teacher and student.
MusiCoder: A Universal Music-Acoustic Encoder Based on Transformers
The performance of MusiCoder is evaluated in two downstream music annotation tasks.
Darwin's Neural Network: AI-based Strategies for Rapid and Scalable Cell and Coronavirus Screening
Recent advances in the interdisciplinary scientific field of machine perception, computer vision, and biomedical engineering underpin a collection of machine learning algorithms with a remarkable ability to decipher the contents of microscope and nanoscope images.
Constructing Deep Neural Networks with a Priori Knowledge of Wireless Tasks
In this paper, we show that two kinds of permutation invariant properties widely existed in wireless tasks can be harnessed to reduce the number of model parameters and hence the sample and computational complexity for training.
Segmentation with Residual Attention U-Net and an Edge-Enhancement Approach Preserves Cell Shape Features
The ability to extrapolate gene expression dynamics in living single cells requires robust cell segmentation, and one of the challenges is the amorphous or irregularly shaped cell boundaries.
Substituting Gadolinium in Brain MRI Using DeepContrast
Together, these studies validate our hypothesis that a deep learning approach can potentially replace the need for GBCAs in brain MRI.
Automatic Analysis System of Calcaneus Radiograph: Rotation-Invariant Landmark Detection for Calcaneal Angle Measurement, Fracture Identification and Fracture Region Segmentation
This study aims to develop an analysis system that can automatically locate four anatomic landmarks, measure BA and CAG for fracture assessment, identify fractured calcaneus, and segment fractured regions.
Structure of Deep Neural Networks with a Priori Information in Wireless Tasks
Deep neural networks (DNNs) have been employed for designing wireless networks in many aspects, such as transceiver optimization, resource allocation, and information prediction.
Evolutionary Trigger Set Generation for DNN Black-Box Watermarking
The commercialization of deep learning creates a compelling need for intellectual property (IP) protection.
RetinaFace: Single-stage Dense Face Localisation in the Wild
Face Analysis Project on MXNet
Ranked #1 on
Face Detection
on WIDER Face (Medium)
Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment
Face Analysis Project on MXNet
Ranked #1 on
Face Alignment
on IBUG
Global Norm-Aware Pooling for Pose-Robust Face Recognition at Low False Positive Rate
In this paper, we propose a novel Global Norm-Aware Pooling (GNAP) block, which reweights local features in a convolutional neural network (CNN) adaptively according to their L2 norms and outputs a global feature vector with a global average pooling layer.
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability.
Ranked #1 on
Face Verification
on Labeled Faces in the Wild
