Xingyi Yang

National University of Singapore | NUS · Department of Electrical & Computer Engineering

Coronavirus disease 2019 (COVID-19) has infected more than 1.3 million individuals all over the world and caused more than 106,000 deaths. One major hurdle in controlling the spreading of this disease is the inefficiency and shortage of medical tests. There have been increasing efforts on developing deep learning methods to diagnose COVID-19 based...

div>Under the pandemic of COVID-19, people experiencing COVID19-related symptoms or exposed to risk factors have a pressing need to consult doctors. Due to hospital closure, a lot of consulting services have been moved online. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this...

To effectively train medical students to become qualified radiologists, a large number of X-ray images collected from patients with diverse medical conditions are needed. However, due to data privacy concerns, such images are typically difficult to obtain. To address this problem, we develop methods to generate view-consistent, high-fidelity, and h...

Pretraining has become a standard technique in computer vision and natural language processing, which usually helps to improve performance substantially. Previously , the most dominant pretraining method is transfer learning (TL), which uses labeled data to learn a good representation network. Recently, a new pretraining approach-self-supervised le...

Most 3D reconstruction methods may only recover scene properties up to a global scale ambiguity. We present a novel approach to single view metrology that can recover the absolute scale of a scene represented by 3D heights of objects or camera height above the ground as well as camera parameters of orientation and field of view, using just a monocu...

Learning the dynamics of spatiotemporal events is a fundamental problem. Neural point processes enhance the expressivity of point process models with deep neural networks. However, most existing methods only consider temporal dynamics without spatial modeling. We propose Deep Spatiotemporal Point Process (DeepSTPP), a deep dynamics model that integ...

In deep learning applications, the architectures of deep neural networks are crucial in achieving high accuracy. Many methods have been proposed to search for high-performance neural architectures automatically. However, these searched architectures are prone to adversarial attacks. A small perturbation of the input data can render the architecture...

In deep learning applications, the architectures of deep neural networks are crucial in achieving high accuracy. Many methods have been proposed to search for high-performance neural architectures automatically. However, these searched architectures are prone to adversarial attacks. A small perturbation of the input data can render the architecture...

DSRNA: Differentiable Search of Robust Neural Architectures

Deep neural networks (DNN) are typically optimized using stochastic gradient descent (SGD). However, the estimation of the gradient using stochastic samples tends to be noisy and unreliable, resulting in large gradient variance and bad convergence. In this paper, we propose Kalman Optimi-zor (KO), an efficient stochastic optimization algorithm that...

Medical report generation is one of the most challenging tasks in medical image analysis. Although existing approaches have achieved promising results, they either require a predefined template database in order to retrieve sentences or ignore the hierarchical nature of medical report generation. To address these issues, we propose MedWriter that i...

In deep learning applications, the architectures of deep neural networks are crucial in achieving high accuracy. Many methods have been proposed to search for high-performance neural architectures automatically. However, these searched architectures are prone to adversarial attacks. A small perturbation of the input data can render the architecture...

Deep neural networks (DNN) are typically optimized using stochastic gradient descent (SGD). However, the estimation of the gradient using stochastic samples tends to be noisy and unreliable, resulting in large gradient variance and bad convergence. In this paper, we propose \textbf{Filter Gradient Decent}~(FGD), an efficient stochastic optimization...

Pedestrian detection is considered one of the most challenging problems in computer vision, as it involves the combination of classification and localization within a scene. Recently, convolutional neural networks (CNNs) have been demonstrated to achieve superior detection results compared to traditional approaches. Although YOLOv3 (an improved You...

Most 3D reconstruction methods may only recover scene properties up to a global scale ambiguity. We present a novel approach to single view metrology that can recover the absolute scale of a scene represented by 3D heights of objects or camera height above the ground as well as camera parameters of orientation and field of view, using just a monocu...

div>Pedestrian detection is considered one of the most challenging problems in computer vision, as it involves the combination of classification and localization within a scene. Recently, convolutional neural networks (CNNs) have been demonstrated to achieve superior detection results compared to traditional approaches. Although YOLOv3 (an improved...

div>Pedestrian detection is considered one of the most challenging problems in computer vision, as it involves the combination of classification and localization within a scene. Recently, convolutional neural networks (CNNs) have been demonstrated to achieve superior detection results compared to traditional approaches. Although YOLOv3 (an improved...

div>Pedestrian detection is considered one of the most challenging problems in computer vision, as it involves the combination of classification and localization within a scene. Recently, convolutional neural networks (CNNs) have been demonstrated to achieve superior detection results compared to traditional approaches. Although YOLOv3 (an improved...

Pedestrian detection is considered one of the most challenging problems in computer vision, as it involves the combination of classification and localization within a scene. Recently, convolutional neural networks (CNNs) have been demonstrated to achieve superior detection results compared to traditional approaches. Although YOLOv3 (an improved You...

Most 3D reconstruction methods may only recover scene properties up to a global scale ambiguity. We present a novel approach to single view metrology that can recover the \emph{absolute} scale of a scene represented by 3D heights of objects or camera height above the ground as well as camera parameters of orientation and field of view, using just a...

Curcumin is a safe, cost-effective natural agent with multiple targets that displays therapeutic potential in cancer. Recently, we reported a novel curcumin analog, Da0324, which exhibited significantly improved stability and anti-cancer activity. However, the molecular mechanism underlying the anti-cancer activity of Da0324 remains largely unknown...

Pretraining has become a standard technique in computer vision and natural language processing, which usually helps to improve performance substantially. Previously, the most dominant pretraining method is transfer learning (TL), which uses labeled data to learn a good representation network. Recently, a new pretraining approach -- self-supervised...

Pretraining has become a standard technique in computer vision and natural language processing, which usually helps to improve performance substantially. Previously, the most dominant pretraining method is transfer learning (TL), which uses labeled data to learn a good representation network. Recently, a new pretraining approach -- self-supervised...

Under the pandemic of COVID-19, people experiencing COVID19-related symptoms or exposed to risk factors have a pressing need to consult doctors. Due to hospital closure, a lot of consulting services have been moved online. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this probl...

div>Under the pandemic of COVID-19, people experiencing COVID19-related symptoms or exposed to risk factors have a pressing need to consult doctors. Due to hospital closure, a lot of consulting services have been moved online. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this...