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Found 33 papers, 12 papers with code
Study of the Impact of the Big Data Era on Accounting and Auditing
Keywords: Big Data, Accounting, Audit, Data Privacy, AI, Machine Learning, Transparency.
IGFNet: Illumination-Guided Fusion Network for Semantic Scene Understanding using RGB-Thermal Images
However, current state-of-the-art methods simply use networks to fuse features on multi-modality inexplicably, rather than designing a fusion method based on the intrinsic characteristics of RGB images and thermal images.
Ranked #6 on
Thermal Image Segmentation
on MFN Dataset
A General Implicit Framework for Fast NeRF Composition and Rendering
Mainly, our work introduces a new surface representation known as Neural Depth Fields (NeDF) that quickly determines the spatial relationship between objects by allowing direct intersection computation between rays and implicit surfaces.
Adaptive-Mask Fusion Network for Segmentation of Drivable Road and Negative Obstacle With Untrustworthy Features
To provide a solution to this issue, we propose the Adaptive-Mask Fusion Network (AMFNet) by introducing adaptive-weight masks in the fusion module to fuse features from RGB and depth images with inconsistency.
RNGDet++: Road Network Graph Detection by Transformer with Instance Segmentation and Multi-scale Features Enhancement
To annotate road network graphs effectively and efficiently, automatic algorithms for road network graph detection are demanded.
CenterLineDet: CenterLine Graph Detection for Road Lanes with Vehicle-mounted Sensors by Transformer for HD Map Generation
Due to the use of the DETR-like transformer network, CenterLineDet can handle complicated graph topology, such as lane intersections.
RNGDet: Road Network Graph Detection by Transformer in Aerial Images
To provide a solution to these problems, we propose a novel approach based on transformer and imitation learning in this paper.
Why-So-Deep: Towards Boosting Previously Trained Models for Visual Place Recognition
We propose a novel approach for improving image retrieval based on previously trained models.
csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps
To provide a solution to the aforementioned problems, in this letter, we propose a novel system termed csBoundary to automatically detect road boundaries at the city scale for HD map annotation.
$$Research on fusion algorithm of multi-attribute decision making and reinforcement learning based on intuitionistic fuzzy number in wargame environment$$
It is also the first time in this field that an algorithm design for intelligent wargaming combines multi-attribute decision making with reinforcement learning.
Method for making multi-attribute decisions in wargames by combining intuitionistic fuzzy numbers with reinforcement learning
Researchers are increasingly focusing on intelligent games as a hot research area. The article proposes an algorithm that combines the multi-attribute management and reinforcement learning methods, and that combined their effect on wargaming, it solves the problem of the agent's low rate of winning against specific rules and its inability to quickly converge during intelligent wargame training. At the same time, this paper studied a multi-attribute decision making and reinforcement learning algorithm in a wargame simulation environment, and obtained data on red and blue conflict. Calculate the weight of each attribute based on the intuitionistic fuzzy number weight calculations.
DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks
Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply complicated negotiation skills with them, such as yielding, merging and taking turns, to achieve both safe and efficient driving in various settings.
CP-loss: Connectivity-preserving Loss for Road Curb Detection in Autonomous Driving with Aerial Images
To alleviate this issue, we detect road curbs offline using high-resolution aerial images in this paper.
End-to-End Interactive Prediction and Planning with Optical Flow Distillation for Autonomous Driving
With the recent advancement of deep learning technology, data-driven approaches for autonomous car prediction and planning have achieved extraordinary performance.
Learning Interpretable End-to-End Vision-Based Motion Planning for Autonomous Driving with Optical Flow Distillation
To address this problem, we propose an interpretable end-to-end vision-based motion planning approach for autonomous driving, referred to as IVMP.
iCurb: Imitation Learning-based Detection of Road Curbs using Aerial Images for Autonomous Driving
We find that the visual appearances between road areas and off-road areas are usually different in aerial images, so we propose a novel solution to detect road curbs off-line using aerial images.
Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images for Autonomous Driving
So in this paper, we propose a new benchmark dataset, named \textit{Topo-boundary}, for offline topological road-boundary detection.
Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms
Therefore, in this paper, we first build a drivable area and road anomaly detection benchmark for ground mobile robots, evaluating the existing state-of-the-art single-modal and data-fusion semantic segmentation CNNs using six modalities of visual features.
Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars
To provide a solution to this problem, we propose a novel branched network G-CIL for the navigation policy learning.
DiGNet: Learning Scalable Self-Driving Policies for Generic Traffic Scenarios with Graph Neural Networks
Traditional decision and planning frameworks for self-driving vehicles (SDVs) scale poorly in new scenarios, thus they require tedious hand-tuning of rules and parameters to maintain acceptable performance in all foreseeable cases.
Geometric Structure Aided Visual Inertial Localization
To take the advantages of both, in this work, we present a complete visual inertial localization system based on a hybrid map representation to reduce the computational cost and increase the positioning accuracy.
Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile Robots
Our NIM can be deployed in existing convolutional neural networks (CNNs) to refine the segmentation performance.
Self-Supervised Drivable Area and Road Anomaly Segmentation using RGB-D Data for Robotic Wheelchairs
We develop a pipeline that can automatically generate segmentation labels for drivable areas and road anomalies.
GMMLoc: Structure Consistent Visual Localization with Gaussian Mixture Models
Incorporating prior structure information into the visual state estimation could generally improve the localization performance.
Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments with Multimodal Sensor Fusion
All-day and all-weather navigation is a critical capability for autonomous driving, which requires proper reaction to varied environmental conditions and complex agent behaviors.
VTGNet: A Vision-based Trajectory Generation Network for Autonomous Vehicles in Urban Environments
Traditional methods for autonomous driving are implemented with many building blocks from perception, planning and control, making them difficult to generalize to varied scenarios due to complex assumptions and interdependencies.
The Role of the Hercules Autonomous Vehicle During the COVID-19 Pandemic: An Autonomous Logistic Vehicle for Contactless Goods Transportation
Moreover, many countries have imposed tough lockdown measures to reduce the virus transmission (e. g., retail, catering) during the pandemic, which causes inconveniences for human daily life.
PointTrackNet: An End-to-End Network For 3-D Object Detection and Tracking From Point Clouds
Recent machine learning-based multi-object tracking (MOT) frameworks are becoming popular for 3-D point clouds.
High-speed Autonomous Drifting with Deep Reinforcement Learning
Drifting is a complicated task for autonomous vehicle control.
Real-time Policy Distillation in Deep Reinforcement Learning
Policy distillation in deep reinforcement learning provides an effective way to transfer control policies from a larger network to a smaller untrained network without a significant degradation in performance.
Robust Lane Marking Detection Algorithm Using Drivable Area Segmentation and Extended SLT
In this paper, a robust lane detection algorithm is proposed, where the vertical road profile of the road is estimated using dynamic programming from the v-disparity map and, based on the estimated profile, the road area is segmented.
Movable-Object-Aware Visual SLAM via Weakly Supervised Semantic Segmentation
Moving objects can greatly jeopardize the performance of a visual simultaneous localization and mapping (vSLAM) system which relies on the static-world assumption.
RTFNet: RGB-Thermal Fusion Network for Semantic Segmentation of Urban Scenes
In order to enable robust and accurate semantic segmentation for autonomous vehicles, we take the advantage of thermal images and fuse both the RGB and thermal information in a novel deep neural network.
Ranked #4 on
Thermal Image Segmentation
on KP day-night
