Search Results for author:
Found 32 papers, 10 papers with code
Unimodal and Crossmodal Refinement Network for Multimodal Sequence Fusion
Specifically, to improve unimodal representations, a unimodal refinement module is designed to refine modality-specific learning via iteratively updating the distribution with transformer-based attention layers.
HierAttn: Effectively Learn Representations from Stage Attention and Branch Attention for Skin Lesions Diagnosis
The new light HierAttn network has the potential in promoting the use of deep learning in clinics and allowing patients for early diagnosis of skin disorders with personal devices.
Rethinking Efficient Lane Detection via Curve Modeling
This paper presents a novel parametric curve-based method for lane detection in RGB images.
Ranked #4 on
Lane Detection
on LLAMAS
Diverse facial inpainting guided by exemplars
Facial image inpainting is a task of filling visually realistic and semantically meaningful contents for missing or masked pixels in a face image.
Rethinking Feature Uncertainty in Stochastic Neural Networks for Adversarial Robustness
Extensive experiments on well-known white- and black-box attacks show that MFDV-SNN achieves a significant improvement over existing methods, which indicates that it is a simple but effective method to improve model robustness.
Learning Token-based Representation for Image Retrieval
Next, a refinement block is introduced to enhance the visual tokens with self-attention and cross-attention.
Exploring Versatile Prior for Human Motion via Motion Frequency Guidance
Prior plays an important role in providing the plausible constraint on human motion.
Meta-Auto-Decoder for Solving Parametric Partial Differential Equations
In these applications, our goal is to solve parametric PDEs rather than one instance of them.
Solving Partial Differential Equations with Point Source Based on Physics-Informed Neural Networks
In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs) emerges to be a promising method for solving both forward and inverse PDE problems.
Contextual Similarity Aggregation with Self-attention for Visual Re-ranking
Since our re-ranking model is not directly involved with the visual feature used in the initial retrieval, it is ready to be applied to retrieval result lists obtained from various retrieval algorithms.
DynSTGAT: Dynamic Spatial-Temporal Graph Attention Network for Traffic Signal Control
Then, to efficiently utilize the historical state information of the intersection, we design a sequence model with the temporal convolutional network (TCN) to capture the historical information and further merge it with the spatial information to improve its performance.
The 2nd Anti-UAV Workshop & Challenge: Methods and Results
The 2nd Anti-UAV Workshop \& Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking.
SKFAC: Training Neural Networks With Faster Kronecker-Factored Approximate Curvature
For the fully connected layers, by utilizing the low-rank property of Kronecker factors of Fisher information matrix, our method only requires inverting a small matrix to approximate the curvature with desirable accuracy.
The Probability Evolution of Corporate Control Power
How does the control power of corporate top1 shareholder arise?
SKFAC:Training Neural Networks with Faster Kronecker-Factored Approximate Curvature
For the fully connected layers, by utilizing the low-rank property of Kronecker factors of Fisher information matrix, our method only requires inverting a small matrix to approximate the curvature with desirable accuracy.
THOR, Trace-based Hardware-adaptive layer-ORiented Natural Gradient Descent Computation
It is well-known that second-order optimizer can accelerate the training of deep neural networks, however, the huge computation cost of second-order optimization makes it impractical to apply in real practice.
IUP: An Intelligent Utility Prediction Scheme for Solid-State Fermentation in 5G IoT
In addition, we design a utility prediction model for SSF based on the Generative Adversarial Networks (GAN) and Fully Connected Neural Network (FCNN).
A Priori Generalization Analysis of the Deep Ritz Method for Solving High Dimensional Elliptic Equations
This paper concerns the a priori generalization analysis of the Deep Ritz Method (DRM) [W. E and B. Yu, 2017], a popular neural-network-based method for solving high dimensional partial differential equations.
Learning Deep Local Features With Multiple Dynamic Attentions for Large-Scale Image Retrieval
To this end, we propose a novel deep local feature learning architecture to simultaneously focus on multiple discriminative local patterns in an image.
AsymptoticNG: A regularized natural gradient optimization algorithm with look-ahead strategy
Optimizers that further adjust the scale of gradient, such as Adam, Natural Gradient (NG), etc., despite widely concerned and used by the community, are often found poor generalization performance, compared with Stochastic Gradient Descent (SGD).
Eigenvalue-corrected Natural Gradient Based on a New Approximation
Using second-order optimization methods for training deep neural networks (DNNs) has attracted many researchers.
Automatic Detection of Cardiac Chambers Using an Attention-based YOLOv4 Framework from Four-chamber View of Fetal Echocardiography
The first step to automatically analyze fetal FC views is locating the fetal four crucial chambers of heart in a US image.
A Trace-restricted Kronecker-Factored Approximation to Natural Gradient
There have been many attempts to use second-order optimization methods for training deep neural networks.
Monocular Human Pose and Shape Reconstruction using Part Differentiable Rendering
In this paper, we introduce body part segmentation as critical supervision.
Empirical distributions of the robustified $t$-test statistics
Based on the median and the median absolute deviation estimators, and the Hodges-Lehmann and Shamos estimators, robustified analogues of the conventional $t$-test statistic are proposed.
Applications
Weight-importance sparse training in keyword spotting
Besides this, at most of time, ASR system is used to deal with real-time problem such as keyword spotting (KWS).
Deep Multiscale Model Learning
Numerical results show that using deep learning and multiscale models, we can improve the forward models, which are conditioned to the available data.
Yuan at SemEval-2018 Task 1: Tweets Emotion Intensity Prediction using Ensemble Recurrent Neural Network
We perform the LSTM and BiLSTM model for the emotion intensity prediction.
DRPose3D: Depth Ranking in 3D Human Pose Estimation
In this paper, we propose a two-stage depth ranking based method (DRPose3D) to tackle the problem of 3D human pose estimation.
YNUDLG at IJCNLP-2017 Task 5: A CNN-LSTM Model with Attention for Multi-choice Question Answering in Examinations
In this paper, we perform convolutional neural networks (CNN) to learn the joint representations of question-answer pairs first, then use the joint representations as the inputs of the long short-term memory (LSTM) with attention to learn the answer sequence of a question for labeling the matching quality of each answer.
Design of Efficient Convolutional Layers using Single Intra-channel Convolution, Topological Subdivisioning and Spatial "Bottleneck" Structure
A topological subdivisioning is adopted to reduce the connection between the input channels and output channels.
Sparse Convolutional Neural Networks
Deep neural networks have achieved remarkable performance in both image classification and object detection problems, at the cost of a large number of parameters and computational complexity.
