Search Results for author:
Found 40 papers, 11 papers with code
RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts
In this paper, we introduce RAAMove, a comprehensive multi-domain corpus dedicated to the annotation of move structures in RA abstracts.
Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control
It is proved that the optimal policy for the augmented state MDP is optimal for the original PC problem with observation delay.
Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part II: Control-Aware Radio Resource Allocation
We focused on the PC sub-problem and proposed the MTCC-PC algorithm to learn an optimal PC policy given an RRA policy.
Learning Real-World Image De-Weathering with Imperfect Supervision
Real-world image de-weathering aims at removing various undesirable weather-related artifacts.
Self-Supervised High Dynamic Range Imaging with Multi-Exposure Images in Dynamic Scenes
The color component is estimated from aligned multi-exposure images, while the structure one is generated through a structure-focused network that is supervised by the color component and an input reference (\eg, medium-exposure) image.
Implicit Sensing in Traffic Optimization: Advanced Deep Reinforcement Learning Techniques
The results unequivocally demonstrate that the DQN agent trained using the {\epsilon}-greedy policy significantly outperforms the one trained with the Boltzmann policy.
Beyond Image Borders: Learning Feature Extrapolation for Unbounded Image Composition
In this paper, we circumvent this issue by presenting a joint framework for both unbounded recommendation of camera view and image composition (i. e., UNIC).
Symbol-Level Precoding for MU-MIMO System with RIRC Receiver
The problem is solved using the alternating optimization (AO) method, and the optimal solution structures for transmit precoding and receive combining matrices are derived by using Lagrangian and Karush-Kuhn-Tucker (KKT) conditions, based on which, the original problem is transformed into an equivalent quadratic programming problem, enabling more efficient solutions.
NTIRE 2023 Quality Assessment of Video Enhancement Challenge
61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions.
Self-supervised Learning to Bring Dual Reversed Rolling Shutter Images Alive
In this paper, we propose a Self-supervised learning framework for Dual reversed RS distortions Correction (SelfDRSC), where a DRSC network can be learned to generate a high framerate GS video only based on dual RS images with reversed distortions.
Optimal Scheduling in IoT-Driven Smart Isolated Microgrids Based on Deep Reinforcement Learning
In this paper, we investigate the scheduling issue of diesel generators (DGs) in an Internet of Things (IoT)-Driven isolated microgrid (MG) by deep reinforcement learning (DRL).
Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising
And we extend the blind-spot network to a blind-neighborhood network (BNN) for providing supervision on flat areas.
SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies
Moreover, unlike most previous reconciliation methods which either rely on strong assumptions or focus on coherent constraints only, we utilize deep neural optimization networks, which not only achieve coherency without any assumptions, but also allow more flexible and realistic constraints to achieve task-based targets, e. g., lower under-estimation penalty and meaningful decision-making loss to facilitate the subsequent downstream tasks.
Physics-Guided ISO-Dependent Sensor Noise Modeling for Extreme Low-Light Photography
Although deep neural networks have achieved astonishing performance in many vision tasks, existing learning-based methods are far inferior to the physical model-based solutions in extreme low-light sensor noise modeling.
Ranked #1 on
Image Denoising
on ELD SonyA7S2 x200
CoSign: Exploring Co-occurrence Signals in Skeleton-based Continuous Sign Language Recognition
The co-occurrence signals (e. g., hand shape, facial expression, and lip pattern) play a critical role in Continuous Sign Language Recognition (CSLR).
End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation
Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconciling all forecasts to ensure coherency, i. e., the forecasts should satisfy the hierarchical aggregation constraints.
Efficient stereo matching on embedded GPUs with zero-means cross correlation
By combining this technique with the domain transformation (DT) algorithm, our system show real-time processing speed of 32 fps, on a Jetson Tx2 GPU for 1, 280x384 pixel images with a maximum disparity of 128.
Deep Radial Embedding for Visual Sequence Learning
The blank class of CTC plays a crucial role in the alignment process and is often considered responsible for the peaky behavior of CTC.
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks.
Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Report
In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based bokeh effect rendering approach that can run on modern smartphone GPUs using TensorFlow Lite.
Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report
The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing.
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP).
AIM 2022 Challenge on Instagram Filter Removal: Methods and Results
This paper introduces the methods and the results of AIM 2022 challenge on Instagram Filter Removal.
Multilayer Fisher extreme learning machine for classification
To address this problem, a novel Fisher extreme learning machine autoencoder (FELM-AE) is proposed and is used as the component for the multilayer Fisher extreme leaning machine (ML-FELM).
AVDDPG: Federated reinforcement learning applied to autonomous platoon control
Both Inter-FRL and Intra-FRL are applied to a custom AV platooning environment using both gradient and weight aggregation to observe the performance effects FRL can have on AV platoons relative to an AV platooning environment trained without FRL.
Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programming
Deep Reinforcement Learning (DRL) is regarded as a potential method for car-following control and has been mostly studied to support a single following vehicle.
Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learning
Nowadays, the application of microgrids (MG) with renewable energy is becoming more and more extensive, which creates a strong need for dynamic energy management.
A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud
Predictive autoscaling (autoscaling with workload forecasting) is an important mechanism that supports autonomous adjustment of computing resources in accordance with fluctuating workload demands in the Cloud.
Deep Reinforcement Learning Aided Platoon Control Relying on V2X Information
In this context, the value of V2X communications for DRL-based platoon controllers is studied with an emphasis on the tradeoff between the gain of including exogenous information in the system state for reducing uncertainty and the performance erosion due to the curse-of-dimensionality.
Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Scheduling
Low earth orbit (LEO) satellite-assisted communications have been considered as one of key elements in beyond 5G systems to provide wide coverage and cost-efficient data services.
Federated Reinforcement Learning: Techniques, Applications, and Open Challenges
This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL), an emerging and promising field in Reinforcement Learning (RL).
Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report
A detailed description of all models developed in the challenge is provided in this paper.
An Ensemble Deep Convolutional Neural Network Model for Electricity Theft Detection in Smart Grids
Smart grids extremely rely on Information and Communications Technology (ICT) and smart meters to control and manage numerous parameters of the network.
Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization
The conventional RL/DRL, e. g., deep Q-learning, however, is limited in dealing with two main issues in constrained combinatorial optimization, i. e., exponentially increasing action space and infeasible actions.
ProxSGD: Training Structured Neural Networks under Regularization and Constraints
In this paper, we consider the problem of training neural networks (NN).
Dynamic Energy Dispatch Based on Deep Reinforcement Learning in IoT-Driven Smart Isolated Microgrids
Microgrids (MGs) are small, local power grids that can operate independently from the larger utility grid.
Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges
Next, a comprehensive survey of the state-of-art research on DRL for AIoT is presented, where the existing works are classified and summarized under the umbrella of the proposed general DRL model.
Multi-user Resource Control with Deep Reinforcement Learning in IoT Edge Computing
By leveraging the concept of mobile edge computing (MEC), massive amount of data generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC server at the edge of wireless network for further computational intensive processing.
Patent Analytics Based on Feature Vector Space Model: A Case of IoT
In order to address the above limitations, we propose a patent analytics based on feature vector space model (FVSM), where the FVSM is constructed by mapping patent documents to feature vectors extracted by convolutional neural networks (CNN).
An In-Vehicle KWS System with Multi-Source Fusion for Vehicle Applications
In order to maximize detection precision rate as well as the recall rate, this paper proposes an in-vehicle multi-source fusion scheme in Keyword Spotting (KWS) System for vehicle applications.
