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Found 66 papers, 9 papers with code
TACT: A Transfer Actor-Critic Learning Framework for Energy Saving in Cellular Radio Access Networks
Recent works have validated the possibility of improving energy efficiency in radio access networks (RANs), achieved by dynamically turning on/off some base stations (BSs).
Adaptive Learning of Region-based pLSA Model for Total Scene Annotation
In this paper, we present a region-based pLSA model to accomplish the task of total scene annotation.
Graph Regularized Non-negative Matrix Factorization By Maximizing Correntropy
Non-negative matrix factorization (NMF) has proved effective in many clustering and classification tasks.
Joint Patch and Multi-Label Learning for Facial Action Unit Detection
The most commonly used taxonomy to describe facial behaviour is the Facial Action Coding System (FACS).
Making Better Use of Edges via Perceptual Grouping
We propose a perceptual grouping framework that organizes image edges into meaningful structures and demonstrate its usefulness on various computer vision tasks.
Learning Semi-Supervised Representation Towards a Unified Optimization Framework for Semi-Supervised Learning
State of the art approaches for Semi-Supervised Learning (SSL) usually follow a two-stage framework -- constructing an affinity matrix from the data and then propagating the partial labels on this affinity matrix to infer those unknown labels.
Deep Region and Multi-Label Learning for Facial Action Unit Detection
Region learning (RL) and multi-label learning (ML) have recently attracted increasing attentions in the field of facial Action Unit (AU) detection.
Ranked #6 on
Facial Action Unit Detection
on DISFA
The Learning and Prediction of Application-level Traffic Data in Cellular Networks
Afterwards, with the aid of the traffic "big data", we make a comprehensive study over the modeling and prediction framework of cellular network traffic.
Residual Attention Network for Image Classification
In this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion.
Ranked #635 on
Image Classification
on ImageNet
Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory
So, the RCLSTM, with certain intrinsic sparsity, have many neural connections absent (distinguished from the full connectivity) and which leads to the reduction of the parameters to be trained and the computational cost.
Dual Attention Matching Network for Context-Aware Feature Sequence based Person Re-Identification
Typical person re-identification (ReID) methods usually describe each pedestrian with a single feature vector and match them in a task-specific metric space.
Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning
To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services.
Deep Reinforcement Learning for Resource Management in Network Slicing
Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices.
AI-based Two-Stage Intrusion Detection for Software Defined IoT Networks
Software Defined Internet of Things (SD-IoT) Networks profits from centralized management and interactive resource sharing which enhances the efficiency and scalability of IoT applications.
Deep Attentive Tracking via Reciprocative Learning
Visual attention, derived from cognitive neuroscience, facilitates human perception on the most pertinent subset of the sensory data.
Deep Learning with Long Short-Term Memory for Time Series Prediction
Time series prediction can be generalized as a process that extracts useful information from historical records and then determines future values.
Brain-Inspired Stigmergy Learning
Stigmergy has proved its great superiority in terms of distributed control, robustness and adaptability, thus being regarded as an ideal solution for large-scale swarm control problems.
Evaluation Mechanism of Collective Intelligence for Heterogeneous Agents Group
Collective intelligence is manifested when multiple agents coherently work in observation, interaction, decision-making and action.
Internet of Intelligence: The Collective Advantage for Advancing Communications and Intelligence
Afterwards, we highlight the potential huge impact of CI on both communications and intelligence.
Self-Supervised Convolutional Subspace Clustering Network
However, the applicability of subspace clustering has been limited because practical visual data in raw form do not necessarily lie in such linear subspaces.
Ranked #2 on
Image Clustering
on Extended Yale-B
GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing
Moreover, we further develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to learn the action-value distribution by estimating the state-value distribution and the action advantage function.
Distributional Reinforcement Learning
Generative Adversarial Network
+3
Decentralized Deep Reinforcement Learning for Delay-Power Tradeoff in Vehicular Communications
To simplify the decision-making process, we first decompose the MDP into a series of per-VUE-pair MDPs.
Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing
Furthermore, as DPGD only works in continuous action space, we embed a k-nearest neighbor algorithm into DQL to quickly find a valid action in the discrete space nearest to the DPGD output.
Age of Information-Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective
In this paper, we investigate the problem of age of information (AoI)-aware radio resource management for expected long-term performance optimization in a Manhattan grid vehicle-to-vehicle network.
A Deep Reinforcement Learning Approach to Multi-component Job Scheduling in Edge Computing
We are interested in the optimal scheduling of a collection of multi-component application jobs in an edge computing system that consists of geo-distributed edge computing nodes connected through a wide area network.
Stigmergic Independent Reinforcement Learning for Multi-Agent Collaboration
With the rapid evolution of wireless mobile devices, there emerges an increased need to design effective collaboration mechanisms between intelligent agents, so as to gradually approach the final collective objective through continuously learning from the environment based on their individual observations.
Convolutional Subspace Clustering Network with Block Diagonal Prior
Standard methods of subspace clustering are based on self-expressiveness in the original data space, which states that a data point in a subspace can be expressed as a linear combination of other points.
Pseudo-Labeling for Small Lesion Detection on Diabetic Retinopathy Images
The first one is that our dataset is not fully labeled, i. e., only a subset of all lesion instances are marked.
Adaptive convolutional neural networks for k-space data interpolation in fast magnetic resonance imaging
Deep learning in k-space has demonstrated great potential for image reconstruction from undersampled k-space data in fast magnetic resonance imaging (MRI).
Learning to Prune in Training via Dynamic Channel Propagation
In this paper, we propose a novel network training mechanism called "dynamic channel propagation" to prune the neural networks during the training period.
Information Freshness-Aware Task Offloading in Air-Ground Integrated Edge Computing Systems
Using the proposed deep RL scheme, each MU in the system is able to make decisions without a priori statistical knowledge of dynamics.
Deep Sketch-Based Modeling: Tips and Tricks
Deep image-based modeling received lots of attention in recent years, yet the parallel problem of sketch-based modeling has only been briefly studied, often as a potential application.
PIAP-DF: Pixel-Interested and Anti Person-Specific Facial Action Unit Detection Net With Discrete Feedback Learning
Experimental results on the two popular AU detection datasets BP4D and DISFA prove that PIAP-DF can be the new state-of-the-art method.
SketchAA: Abstract Representation for Abstract Sketches
The superiority of explicitly abstracting sketch representation is empirically validated on a number of sketch analysis tasks, including sketch recognition, fine-grained sketch-based image retrieval, and generative sketch healing.
The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication
The paper considers independent reinforcement learning (IRL) for multi-agent collaborative decision-making in the paradigm of federated learning (FL).
3DRIMR: 3D Reconstruction and Imaging via mmWave Radar based on Deep Learning
In this paper we propose 3D Reconstruction and Imaging via mmWave Radar (3DRIMR), a deep learning based architecture that reconstructs 3D shape of an object in dense detailed point cloud format, based on sparse raw mmWave radar intensity data.
Semantic Communication with Adaptive Universal Transformer
With the development of deep learning (DL), natural language processing (NLP) makes it possible for us to analyze and understand a large amount of language texts.
Reinforcement Learning-powered Semantic Communication via Semantic Similarity
We introduce a new semantic communication mechanism - SemanticRL, whose key idea is to preserve the semantic information instead of strictly securing the bit-level precision.
DeepPoint: A Deep Learning Model for 3D Reconstruction in Point Clouds via mmWave Radar
Built on our recent proposed 3DRIMR (3D Reconstruction and Imaging via mmWave Radar), we introduce in this paper DeepPoint, a deep learning model that generates 3D objects in point cloud format that significantly outperforms the original 3DRIMR design.
Rethinking Modern Communication from Semantic Coding to Semantic Communication
Modern communications are usually designed to pursue a higher bit-level precision and fewer bits while transmitting a message.
Finding Badly Drawn Bunnies
Our key discovery lies in exploiting the magnitude (L2 norm) of a sketch feature as a quantitative quality metric.
RePre: Improving Self-Supervised Vision Transformer with Reconstructive Pre-training
Recently, self-supervised vision transformers have attracted unprecedented attention for their impressive representation learning ability.
Communication-Efficient Consensus Mechanism for Federated Reinforcement Learning
The paper considers independent reinforcement learning (IRL) for multi-agent decision-making process in the paradigm of federated learning (FL).
Adaptive Bit Rate Control in Semantic Communication with Incremental Knowledge-based HARQ
Semantic communication has witnessed a great progress with the development of natural language processing (NLP) and deep learning (DL).
R2P: A Deep Learning Model from mmWave Radar to Point Cloud
Recent research has shown the effectiveness of mmWave radar sensing for object detection in low visibility environments, which makes it an ideal technique in autonomous navigation systems.
Contrastive Monotonic Pixel-Level Modulation
Continuous one-to-many mapping is a less investigated yet important task in both low-level visions and neural image translation.
AoI-based Temporal Attention Graph Neural Network for Popularity Prediction and Content Caching
In this paper, to maximize the cache hit rate, we leverage an effective dynamic graph neural network (DGNN) to jointly learn the structural and temporal patterns embedded in the bipartite graph.
Learning Audio-Visual embedding for Person Verification in the Wild
It has already been observed that audio-visual embedding is more robust than uni-modality embedding for person verification.
Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning
We then put forward a novel offline DAC scheme, which estimates the optimal control policy from a previously collected dataset without any further interactions with the system.
3D Reconstruction of Multiple Objects by mmWave Radar on UAV
The radar data is sent to a deep neural network model, which outputs the point cloud reconstruction of the multiple objects in the space.
Semantics-Empowered Communication: A Tutorial-cum-Survey
Along with the springing up of the semantics-empowered communication (SemCom) research, it is now witnessing an unprecedentedly growing interest towards a wide range of aspects (e. g., theories, applications, metrics and implementations) in both academia and industry.
Optimal Proposal Learning for Deployable End-to-End Pedestrian Detection
End-to-end pedestrian detection focuses on training a pedestrian detection model via discarding the Non-Maximum Suppression (NMS) post-processing.
Knowledge Enhanced Semantic Communication Receiver
In recent years, with the rapid development of deep learning and natural language processing technologies, semantic communication has become a topic of great interest in the field of communication.
NetGPT: A Native-AI Network Architecture Beyond Provisioning Personalized Generative Services
Towards personalized generative services, a collaborative cloud-edge methodology is promising, as it facilitates the effective orchestration of heterogeneous distributed communication and computing resources.
Decentralized Adaptive Formation via Consensus-Oriented Multi-Agent Communication
Adaptive multi-agent formation control, which requires the formation to flexibly adjust along with the quantity variations of agents in a decentralized manner, belongs to one of the most challenging issues in multi-agent systems, especially under communication-limited constraints.
Alternate Learning based Sparse Semantic Communications for Visual Transmission
Semantic communication (SemCom) demonstrates strong superiority over conventional bit-level accurate transmission, by only attempting to recover the essential semantic information of data.
Select2Col: Leveraging Spatial-Temporal Importance of Semantic Information for Efficient Collaborative Perception
Collaborative perception by leveraging the shared semantic information plays a crucial role in overcoming the individual limitations of isolated agents.
Imitation Learning based Alternative Multi-Agent Proximal Policy Optimization for Well-Formed Swarm-Oriented Pursuit Avoidance
Multi-Robot System (MRS) has garnered widespread research interest and fostered tremendous interesting applications, especially in cooperative control fields.
Sketch-based Video Object Segmentation: Benchmark and Analysis
Reference-based video object segmentation is an emerging topic which aims to segment the corresponding target object in each video frame referred by a given reference, such as a language expression or a photo mask.
Wired Perspectives: Multi-View Wire Art Embraces Generative AI
Creating multi-view wire art (MVWA), a static 3D sculpture with diverse interpretations from different viewpoints, is a complex task even for skilled artists.
Self-Critical Alternate Learning based Semantic Broadcast Communication
In particular, to enable stable optimization via a nondifferentiable semantic metric, we regard sentence similarity as a reward and formulate this learning process as an RL problem.
More than Vanilla Fusion: a Simple, Decoupling-free, Attention Module for Multimodal Fusion Based on Signal Theory
The vanilla fusion methods still dominate a large percentage of mainstream audio-visual tasks.
Interplay of Semantic Communication and Knowledge Learning
In the swiftly advancing realm of communication technologies, Semantic Communication (SemCom), which emphasizes knowledge understanding and processing, has emerged as a hot topic.
Topology Data Analysis-based Error Detection for Semantic Image Transmission with Incremental Knowledge-based HARQ
In particular, different from the conventional CRC, we introduce a topological data analysis (TDA)-based error detection method, which capably digs out the inner topological and geometric information of images, so as to capture semantic information and determine the necessity for re-transmission.
