Search Results for author:
Found 22 papers, 5 papers with code
SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation
For each 3D point, the local polar representation block is firstly explored to construct a spatial representation that is invariant to the z-axis rotation, then the dual-distance attentive pooling block is designed to utilize the representations of its neighbors for learning more discriminative local features according to both the geometric and feature distances among them, and finally, the global contextual feature block is designed to learn a global context for each 3D point by utilizing its spatial location and the volume ratio of the neighborhood to the global point cloud.
Ranked #2 on
Semantic Segmentation
on Semantic3D
Drill the Cork of Information Bottleneck by Inputting the Most Important Data
As a promising tool to better understand the learning dynamic of minibatch SGD, the information bottleneck (IB) theory claims that the optimization process consists of an initial fitting phase and the following compression phase.
Deep Q Learning from Dynamic Demonstration with Behavioral Cloning
This study proposes a novel approach integrating deep Q learning from dynamic demonstrations with a behavioral cloning model (DQfDD-BC), which includes a supervised learning technique of instructing a DRL model to enhance its performance.
FCM-RDpA: TSK Fuzzy Regression Model Construction Using Fuzzy C-Means Clustering, Regularization, DropRule, and Powerball AdaBelief
To effectively optimize Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a mini-batch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA) algorithm was recently proposed.
Dynamic Fusion based Federated Learning for COVID-19 Detection
To improve communication efficiency and model performance, in this paper, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections.
Conditional Uncorrelation and Efficient Non-approximate Subset Selection in Sparse Regression
By the proposed method, the computational complexity is reduced from $O(\frac{1}{6}{k^3}+mk^2+mkd)$ to $O(\frac{1}{6}{k^3}+\frac{1}{2}mk^2)$ for each candidate subset in sparse regression.
Convolutional Ordinal Regression Forest for Image Ordinal Estimation
We propose a novel ordinal regression approach, termed Convolutional Ordinal Regression Forest or CORF, for image ordinal estimation, which can integrate ordinal regression and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships.
Defining Digital Quadruplets in the Cyber-Physical-Social Space for Parallel Driving
The objectives of the three virtual digital vehicles are interacting, guiding, simulating and improving with the real vehicles.
Digital Quadruplets for Cyber-Physical-Social Systems based Parallel Driving: From Concept to Applications
The three virtual vehicles (descriptive, predictive, and prescriptive) dynamically interact with the real one in order to enhance the safety and performance of the real vehicle.
GPO: Global Plane Optimization for Fast and Accurate Monocular SLAM Initialization
Initialization is essential to monocular Simultaneous Localization and Mapping (SLAM) problems.
Context-Aware Dynamic Feature Extraction for 3D Object Detection in Point Clouds
Varying density of point clouds increases the difficulty of 3D detection.
Adaptive and Azimuth-Aware Fusion Network of Multimodal Local Features for 3D Object Detection
We adopt different modalities of LiDAR data to generate richer features and present an adaptive and azimuth-aware network to aggregate local features from image, bird's eye view maps and point cloud.
A Heuristic Algorithm for the Fabric Spreading and Cutting Problem in Apparel Factories
Firstly the constructive procedure creates a set of lays in sequence, and then the improving loop tries to pick each lay from the lay set and rearrange the remaining lays into a smaller lay set.
Parallel Medical Imaging for Intelligent Medical Image Analysis: Concepts, Methods, and Applications
Artificial imaging systems are introduced to select and prescriptively generate medical image data in a knowledge-driven way to utilize medical domain knowledge.
Accelerating Minibatch Stochastic Gradient Descent using Typicality Sampling
Machine learning, especially deep neural networks, has been rapidly developed in fields including computer vision, speech recognition and reinforcement learning.
Mutual Clustering on Comparative Texts via Heterogeneous Information Networks
HINT first models multi-sourced texts (e. g. news and tweets) as heterogeneous information networks by introducing the shared ``anchor texts'' to connect the comparative texts.
An Efficient Deep Reinforcement Learning Model for Urban Traffic Control
Urban Traffic Control (UTC) plays an essential role in Intelligent Transportation System (ITS) but remains difficult.
M4CD: A Robust Change Detection Method for Intelligent Visual Surveillance
Secondly, multiple heterogeneous features (including brightness variation, chromaticity variation, and texture variation) are extracted by comparing the input frame with the background model, and a multi-source learning strategy is designed to online estimate the probability distributions for both foreground and background.
Scene-Specific Pedestrian Detection Based on Parallel Vision
In order to make the generic scene pedestrian detectors work well in specific scenes, the labeled data from specific scenes are needed to adapt the models to the specific scenes.
Training and Testing Object Detectors with Virtual Images
In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data.
The ParallelEye Dataset: Constructing Large-Scale Artificial Scenes for Traffic Vision Research
As a result, we present a viable implementation pipeline for constructing large-scale artificial scenes for traffic vision research.
DeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Prediction
Detrending based methods decompose original flow series into trend and residual series, in which trend describes the fixed temporal pattern in traffic flow and residual series is used for prediction.
