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Found 231 papers, 49 papers with code
Unsupervised Pretraining Encourages Moderate-Sparseness
It is well known that direct training of deep neural networks will generally lead to poor results.
Sparse Deep Stacking Network for Image Classification
Luckily, a simplified neural network module (SNNM) has been proposed to directly learn the discriminative dictionaries for avoiding the expensive inference.
Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps
Crowd counting is an important task in computer vision, which has many applications in video surveillance.
ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering
For spectral embedding/clustering, it is still an open problem on how to construct an relation graph to reflect the intrinsic structures in data.
A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images
Estimating depth from a single RGB image is an ill-posed and inherently ambiguous problem.
Ranked #69 on
Monocular Depth Estimation
on NYU-Depth V2
GRASS: Generative Recursive Autoencoders for Shape Structures
We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures.
Dense Transformer Networks
The proposed dense transformer modules are differentiable, thus the entire network can be trained.
Spectrum-based deep neural networks for fraud detection
Due to the small dimension of spectral coordinates (compared with the dimension of the adjacency matrix derived from a graph), training deep neural networks becomes feasible.
Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment
Processing of digital images is continuously gaining in volume and relevance, with concomitant demands on data storage, transmission and processing power.
Quantum Physics
Clustering with Outlier Removal
Then an objective function based Holoentropy is designed to enhance the compactness of each cluster with a few outliers removed.
Adversarial Metric Learning
In distinguishment stage, a metric is exhaustively learned to try its best to distinguish both the adversarial pairs and the original training pairs.
One-Class Adversarial Nets for Fraud Detection
Currently, most of the fraud detection approaches require a training dataset that contains records of both benign and malicious users.
Im2Struct: Recovering 3D Shape Structure from a Single RGB Image
We propose to recover 3D shape structures from single RGB images, where structure refers to shape parts represented by cuboids and part relations encompassing connectivity and symmetry.
Generative Steganography by Sampling
In this paper, a new data-driven information hiding scheme called generative steganography by sampling (GSS) is proposed.
Privacy Preservation in Location-Based Services: A Novel Metric and Attack Model
In this paper, we incorporate a new type of side information based on consecutive location changes of users and propose a new metric called transition-entropy to investigate the location privacy preservation, followed by two algorithms to improve the transition-entropy for a given dummy generation algorithm.
Predictive Local Smoothness for Stochastic Gradient Methods
To address this problem, we propose a simple yet effective method for improving stochastic gradient methods named predictive local smoothness (PLS).
Accurate Spectral Super-resolution from Single RGB Image Using Multi-scale CNN
Different from traditional hyperspectral super-resolution approaches that focus on improving the spatial resolution, spectral super-resolution aims at producing a high-resolution hyperspectral image from the RGB observation with super-resolution in spectral domain.
Wide Contextual Residual Network with Active Learning for Remote Sensing Image Classification
As it is very difficult and expensive to obtain class labels in real world, we integrate the proposed WCRN with AL to improve its generalization by using the most informative training samples.
Ranked #10 on
Hyperspectral Image Classification
on Pavia University
(Overall Accuracy metric)
GraphSeq2Seq: Graph-Sequence-to-Sequence for Neural Machine Translation
In this paper, we propose a Graph-Sequence-to-Sequence(GraphSeq2Seq) model to fuse the dependency graph among words into the traditional Seq2Seq framework.
On Learning and Learned Data Representation by Capsule Networks
In this work, we investigate the following: 1) how the routing affects the CapsNet model fitting; 2) how the representation using capsules helps discover global structures in data distribution, and; 3) how the learned data representation adapts and generalizes to new tasks.
Deep Discriminative Representation Learning with Attention Map for Scene Classification
However, most recent approaches to remote sensing scene classification are based on Convolutional Neural Networks (CNNs).
Privacy Preserving Location Data Publishing: A Machine Learning Approach
By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use $k$-means algorithm for this purpose.
Pancreas segmentation with probabilistic map guided bi-directional recurrent UNet
To alleviate these problems, we propose a probabilistic-map-guided bi-directional recurrent UNet (PBR-UNet) architecture, which fuses intra-slice information and inter-slice probabilistic maps into a local 3D hybrid regularization scheme, which is followed by bi-directional recurrent network optimization.
A Model-Driven Stack-Based Fully Convolutional Network for Pancreas Segmentation
The irregular geometry and high inter-slice variability in computerized tomography (CT) scans of the human pancreas make an accurate segmentation of this crucial organ a challenging task for existing data-driven deep learning methods.
Shakeout: A New Approach to Regularized Deep Neural Network Training
Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training.
A Distributed Approach towards Discriminative Distance Metric Learning
Distance metric learning is successful in discovering intrinsic relations in data.
Learning Part Generation and Assembly for Structure-aware Shape Synthesis
Enlightened by the fact that 3D shape structure is characterized as part composition and placement, we propose to model 3D shape variations with a part-aware deep generative network, coined as PAGENet.
Dense Transformer Networks for Brain Electron Microscopy Image Segmentation
The proposed dense transformer modules are differentiable, thus the entire network can be trained.
Ranked #1 on
Electron Microscopy Image Segmentation
on SNEMI3D
Electron Microscopy Image Segmentation
Image Segmentation
+1
Cross-X Learning for Fine-Grained Visual Categorization
Recognizing objects from subcategories with very subtle differences remains a challenging task due to the large intra-class and small inter-class variation.
Ranked #18 on
Fine-Grained Image Classification
on NABirds
(using extra training data)
Fine-Grained Image Classification
Fine-Grained Visual Categorization
On Safeguarding Privacy and Security in the Framework of Federated Learning
Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) paradigm has emerged, namely federated learning (FL).
Networking and Internet Architecture
Variable selection with false discovery rate control in deep neural networks
Deep neural networks (DNNs) are famous for their high prediction accuracy, but they are also known for their black-box nature and poor interpretability.
Weakly Supervised Energy-Based Learning for Action Segmentation
This paper is about labeling video frames with action classes under weak supervision in training, where we have access to a temporal ordering of actions, but their start and end frames in training videos are unknown.
Weighted graphlets and deep neural networks for protein structure classification
We develop a weighted network that depicts the protein structures, and more importantly, we propose the first graphlet-based measure that applies to weighted networks.
Bandwidth Slicing to Boost Federated Learning in Edge Computing
Bandwidth slicing is introduced to support federated learning in edge computing to assure low communication delay for training traffic.
LPRNet: Lightweight Deep Network by Low-rank Pointwise Residual Convolution
To reduce the computation and memory costs, we propose a novel lightweight deep learning module by low-rank pointwise residual (LPR) convolution, called LPRNet.
Federated Learning with Differential Privacy: Algorithms and Performance Analysis
Specifically, the theoretical bound reveals the following three key properties: 1) There is a tradeoff between the convergence performance and privacy protection levels, i. e., a better convergence performance leads to a lower protection level; 2) Given a fixed privacy protection level, increasing the number $N$ of overall clients participating in FL can improve the convergence performance; 3) There is an optimal number of maximum aggregation times (communication rounds) in terms of convergence performance for a given protection level.
Symplectic $(-2)$-spheres and the symplectomorphism group of small rational 4-manifolds, II
For $(\mathbb{C} P^2 \# 5{\overline {\mathbb{C} P^2}},\omega)$, let $N_{\omega}$ be the number of $(-2)$-symplectic spherical homology classes. We completely determine the Torelli symplectic mapping class group (Torelli SMCG): the Torelli SMCG is trivial if $N_{\omega}>8$; it is $\pi_0(Diff^+(S^2, 5))$ if $N_{\omega}=0$ (by Paul Seidel and Jonathan Evans); it is $\pi_0(Diff^+(S^2, 4))$ in the remaining case.
Symplectic Geometry
Lifelong Spectral Clustering
As a new spectral clustering task arrives, L2SC firstly transfers knowledge from both basis library and feature library to obtain encoding matrix, and further redefines the library base over time to maximize performance across all the clustering tasks.
Curvilinear Distance Metric Learning
To address this issue, we first reveal that the traditional linear distance metric is equivalent to the cumulative arc length between the data pair's nearest points on the learned straight measurer lines.
Naive Gabor Networks for Hyperspectral Image Classification
Specifically, we develop an innovative phase-induced Gabor kernel, which is trickily designed to perform the Gabor feature learning via a linear combination of local low-frequency and high-frequency components of data controlled by the kernel phase.
UAV-Enabled Confidential Data Collection in Wireless Networks
This work, for the first time, considers confidential data collection in the context of unmanned aerial vehicle (UAV) wireless networks, where the scheduled ground sensor node (SN) intends to transmit confidential information to the UAV without being intercepted by other unscheduled ground SNs.
Recommendations to enhance rigor and reproducibility in biomedical research
Computational methods have reshaped the landscape of modern biology.
Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation
To tackle intra-class shape variations, we learn canonical shape space (CASS), a unified representation for a large variety of instances of a certain object category.
Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transform
We specify two new optimization algorithms: Cayley SGD with momentum, and Cayley ADAM on the Stiefel manifold.
Face Hallucination with Finishing Touches
The coarse-level FHnet generates a frontal coarse HR face and then the fine-level FHnet makes use of the facial component appearance prior, i. e., fine-grained facial components, to attain a frontal HR face image with authentic details.
Vehicle Tracking in Wireless Sensor Networks via Deep Reinforcement Learning
Vehicle tracking has become one of the key applications of wireless sensor networks (WSNs) in the fields of rescue, surveillance, traffic monitoring, etc.
Set-Constrained Viterbi for Set-Supervised Action Segmentation
This paper is about weakly supervised action segmentation, where the ground truth specifies only a set of actions present in a training video, but not their true temporal ordering.
User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization
According to our analysis, the UDP framework can realize $(\epsilon_{i}, \delta_{i})$-LDP for the $i$-th MT with adjustable privacy protection levels by varying the variances of the artificial noise processes.
A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems
One major challenge in system design is to reconstruct local gradient vectors accurately at the central server, which are computed-and-sent from the wireless devices.
G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes
Counterfactual prediction is a fundamental task in decision-making.
Learnable Subspace Clustering
This paper studies the large-scale subspace clustering (LSSC) problem with million data points.
DNN-aided Read-voltage Threshold Optimization for MLC Flash Memory with Finite Block Length
The error correcting performance of multi-level-cell (MLC) NAND flash memory is closely related to the block length of error correcting codes (ECCs) and log-likelihood-ratios (LLRs) of the read-voltage thresholds.
Reconfigurable Intelligent Surface (RIS)-Enhanced Two-Way OFDM Communications
In this paper, we focus on the reconfigurable intelligent surface (RIS)-enhanced two-way device-to-device (D2D) multi-pair orthogonal-frequency-division-multiplexing (OFDM) communication systems.
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
Specifically, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification, and use a vector to represent arbitrary distribution of box locations.
Ranked #93 on
Object Detection
on COCO test-dev
Learning Semantically Enhanced Feature for Fine-Grained Image Classification
We aim to provide a computationally cheap yet effective approach for fine-grained image classification (FGIC) in this letter.
Ranked #14 on
Fine-Grained Image Classification
on Stanford Dogs
AReLU: Attention-based Rectified Linear Unit
Adding the attention module with a rectified linear unit (ReLU) results in an amplification of positive elements and a suppression of negative ones, both with learned, data-adaptive parameters.
An Empirical Comparison of Unsupervised Constituency Parsing Methods
Unsupervised constituency parsing aims to learn a constituency parser from a training corpus without parse tree annotations.
RDP-GAN: A Rényi-Differential Privacy based Generative Adversarial Network
Generative adversarial network (GAN) has attracted increasing attention recently owing to its impressive ability to generate realistic samples with high privacy protection.
LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark
We evaluate and analyze more than 30 trackers on LSOTB-TIR to provide a series of baselines, and the results show that deep trackers achieve promising performance.
Thermal Infrared Object Tracking
Vocal Bursts Intensity Prediction
A Short Review on Data Modelling for Vector Fields
On the application side, vector fields are an extremely useful type of data in empirical sciences, as well as signal processing, e. g. non-parametric transformations of 3D point clouds using 3D vector fields, the modelling of the fluid flow in earth science, and the modelling of physical fields.
When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm
Motivated by the explosive computing capabilities at end user equipments, as well as the growing privacy concerns over sharing sensitive raw data, a new machine learning paradigm, named federated learning (FL) has emerged.
Networking and Internet Architecture
Reconstruction of Quantitative Susceptibility Maps from Phase of Susceptibility Weighted Imaging with Cross-Connected $Ψ$-Net
In this work, we propose to explore a new value of the high-pass filtered phase data generated in susceptibility weighted imaging (SWI), and develop an end-to-end Cross-connected $\Psi$-Net (C$\Psi$-Net) to reconstruct QSM directly from these phase data in SWI without additional pre-processing.
Scalable Federated Learning over Passive Optical Networks
Two-step aggregation is introduced to facilitate scalable federated learning (SFL) over passive optical networks (PONs).
Networking and Internet Architecture
Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection
Such a property makes the distribution statistics of a bounding box highly correlated to its real localization quality.
Ranked #26 on
Object Detection
on COCO-O
Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients
The proposed BLADE-FL has a good performance in terms of privacy preservation, tamper resistance, and effective cooperation of learning.
Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models
Due to the lack of a large-scale pelvic CT dataset with annotations, deep learning methods are not fully explored.
Robust Dynamical Decoupling for the Manipulation of a Spin Network via a Single Spin
High-fidelity control of quantum systems is crucial for quantum information processing, but is often limited by perturbations from the environment and imperfections in the applied control fields.
Quantum Physics
Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation
Focusing on this problem, we explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal K, the learning parameters, and the proportion of lazy clients.
Solving localized wave solutions of the derivative nonlinear Schrodinger equation using an improved PINN method
On the bases of the improved method, the effects for different numbers of initial points sampled, residual collocation points sampled, network layers, neurons per hidden layer on the second order genuine rational soliton solution dynamics of the DNLS are considered, and the relevant analysis when the locally adaptive activation function chooses different initial values of scalable parameters are also exhibited in the simulation of the two-order rogue wave solution.
Pattern Formation and Solitons Exactly Solvable and Integrable Systems
Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization
An attacker in FL may control a number of participant clients, and purposely craft the uploaded model parameters to manipulate system outputs, namely, model poisoning (MP).
Effects of Number of Filters of Convolutional Layers on Speech Recognition Model Accuracy
Our results show a strong dependency of word accuracy on the Number of Filters of convolutional layers.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multi-Pretext Attention Network for Few-shot Learning with Self-supervision
Self-supervised learning is emerged as an efficient method to utilize unlabeled data.
Roles of the Narrow Electronic Band near the Fermi Level in 1$T$-TaS$_2$-Related Layered Materials
4$H_{\rm b}$-TaS$_2$ is a superconducting compound with alternating 1$T$-TaS$_2$ and 1$H$-TaS$_2$ layers, where the 1$H$-TaS$_2$ layer has weak charge density wave (CDW) pattern and reduces the CDW coupling between the adjacent 1$T$-TaS$_2$ layers.
Mesoscale and Nanoscale Physics Materials Science
S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection
3D object detection plays a crucial role in environmental perception for autonomous vehicles, which is the prerequisite of decision and control.
Regularized Covariance Estimation for Polarization Radar Detection in Compound Gaussian Sea Clutter
This paper investigates regularized estimation of Kronecker-structured covariance matrices (CM) for polarization radar in sea clutter scenarios where the data are assumed to follow the complex, elliptically symmetric (CES) distributions with a Kronecker-structured CM.
Experimental Quantum Principal Component Analysis via Parametrized Quantum Circuits
Principal component analysis (PCA) is a widely applied but rather time-consuming tool in machine learning techniques.
A novel multimodal fusion network based on a joint coding model for lane line segmentation
There has recently been growing interest in utilizing multimodal sensors to achieve robust lane line segmentation.
Cross-Validated Tuning of Shrinkage Factors for MVDR Beamforming Based on Regularized Covariance Matrix Estimation
We propose leave-one-out cross-validation (LOOCV) choices for the shrinkage factors to optimize the beamforming performance, referred to as $\text{S}^2$CM-CV and STE-CV.
Potential Convolution: Embedding Point Clouds into Potential Fields
In this work, rather than defining a continuous or discrete kernel, we directly embed convolutional kernels into the learnable potential fields, giving rise to potential convolution.
Action Shuffle Alternating Learning for Unsupervised Action Segmentation
Our SSL trains an RNN to recognize positive and negative action sequences, and the RNN's hidden layer is taken as our new action-level feature embedding.
Anchor-Constrained Viterbi for Set-Supervised Action Segmentation
This paper is about action segmentation under weak supervision in training, where the ground truth provides only a set of actions present, but neither their temporal ordering nor when they occur in a training video.
Federated Learning for Internet of Things: A Comprehensive Survey
The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI).
ASPCNet: A Deep Adaptive Spatial Pattern Capsule Network for Hyperspectral Image Classification
Based on this observation, this paper proposes an adaptive spatial pattern capsule network (ASPCNet) architecture by developing an adaptive spatial pattern (ASP) unit, that can rotate the sampling location of convolutional kernels on the basis of an enlarged receptive field.
A lightweight deep learning based cloud detection method for Sentinel-2A imagery fusing multi-scale spectral and spatial features
In the encoder, three input branches are designed to handle spectral bands at their native resolution and extract multiscale spectral features.
Lite-FPN for Keypoint-based Monocular 3D Object Detection
3D object detection with a single image is an essential and challenging task for autonomous driving.
Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design
Owing to the low communication costs and privacy-promoting capabilities, Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients.
An Extension of BIM Using AI: a Multi Working-Machines Pathfinding Solution
In the practical implementation of a construction site, it is sensible to solve the problem with a hybrid solution; therefore, in our study, we proposed an algorithm based on a cutting-edge multi-pathfinding algorithm to enable the massive number of machines cooperation and offer the advice to modify the unreasonable part of the working site in the meantime.
Dynamic network analysis improves protein 3D structural classification
Here, we propose for the first time a way to model 3D structures of proteins as dynamic PSNs, with the hypothesis that this will improve upon the current state-of-the-art PSC approaches that are based on static PSNs (and thus upon the existing state-of-the-art sequence and other 3D structural approaches).
CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography
Spine-related diseases have high morbidity and cause a huge burden of social cost.
Federated Learning for Industrial Internet of Things in Future Industries
The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems and becomes a key enabler for future industries.
IPS300+: a Challenging Multimodal Dataset for Intersection Perception System
Through an IPS (Intersection Perception System) installed at the diagonal of the intersection, this paper proposes a high-quality multimodal dataset for the intersection perception task.
Low-Latency Federated Learning over Wireless Channels with Differential Privacy
Then, we convert the MAMAB to a max-min bipartite matching problem at each communication round, by estimating rewards with the upper confidence bound (UCB) approach.
Joint Communication and Trajectory Design for Intelligent Reflecting Surface Empowered UAV SWIPT Networks
Aiming at the limited battery capacity of widely deployed low-power smart devices in the Internet-of-things (IoT), this paper proposes a novel intelligent reflecting surface (IRS) empowered unmanned aerial vehicle (UAV) simultaneous wireless information and power transfer (SWIPT) network framework, in which IRS is used to reconstruct the wireless channel to enhance the wireless energy transmission efficiency and coverage area of the UAV SWIPT networks.
RigNet: Repetitive Image Guided Network for Depth Completion
However, blurry guidance in the image and unclear structure in the depth still impede the performance of the image guided frameworks.
Ranked #2 on
Depth Completion
on KITTI Depth Completion
An optical biomimetic eyes with interested object imaging
We presented an optical system to perform imaging interested objects in complex scenes, like the creature easy see the interested prey in the hunt for complex environments.
Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark
Monocular depth estimation aims at predicting depth from a single image or video.
6G Internet of Things: A Comprehensive Survey
The sixth generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) towards a future of fully intelligent and autonomous systems.
Elevation Angle-Dependent 3D Trajectory Design for Aerial RIS-aided Communication
This paper investigates an aerial reconfigurable intelligent surface (RIS)-aided communication system under the probabilistic line-of-sight (LoS) channel, where an unmanned aerial vehicle (UAV) equipped with an RIS is deployed to assist two ground nodes in their information exchange.
iVPF: Numerical Invertible Volume Preserving Flow for Efficient Lossless Compression
We also propose a lossless compression algorithm based on iVPF.
Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration
Based on the reward function, for each state, we then construct a fused score function to evaluate the sampled transformations, where we weight the current and future rewards of the transformations.
Constructing Orthogonal Convolutions in an Explicit Manner
A convolution layer with an orthogonal Jacobian matrix is 1-Lipschitz in the 2-norm, making the output robust to the perturbation in input.
Cooperative Task Offloading and Block Mining in Blockchain-based Edge Computing with Multi-agent Deep Reinforcement Learning
The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in mobile networks, by offering task offloading solutions with security enhancement empowered by blockchain mining.
$f$-Divergence Thermodynamic Variational Objective: a Deformed Geometry Perspective
In this paper, we propose a $f$-divergence Thermodynamic Variational Objective ($f$-TVO).
RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images
Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs.
Fast and Light-Weight Network for Single Frame Structured Illumination Microscopy Super-Resolution
In our SF-SIM, we propose a noise estimator which can effectively suppress the noise in the image and enable our method to work under the low light and short exposure environment, without the need for stacking multiple frames for non-local denoising.
CT-block: a novel local and global features extractor for point cloud
Meanwhile, the transformer-branch performs offset-attention process on the whole point cloud to extract the global feature.
Which images to label for few-shot medical landmark detection?
We herein propose a novel Sample Choosing Policy (SCP) to select "the most worthy" images for annotation, in the context of few-shot medical landmark detection.
Temporal Transformer Networks with Self-Supervision for Action Recognition
The temporal sequence self-supervision module we employ unprecedentedly adopts the streamlined strategy of "random batch random channel" to reverse the sequence of video frames, allowing robust extractions of motion information representation from inversed temporal dimensions and improving the generalization capability of the model.
Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent
Coded distributed computation has become common practice for performing gradient descent on large datasets to mitigate stragglers and other faults.
Locally Random Alloy Codes with Channel Coding Theorems for Distributed Matrix Multiplication
Matrix multiplication is a fundamental operation in machine learning and is commonly distributed into multiple parallel tasks for large datasets.
Vertical Federated Learning: Challenges, Methodologies and Experiments
As a special architecture in FL, vertical FL (VFL) is capable of constructing a hyper ML model by embracing sub-models from different clients.
A Deep Learning Approach to Predicting Ventilator Parameters for Mechanically Ventilated Septic Patients
We develop a deep learning approach to predicting a set of ventilator parameters for a mechanically ventilated septic patient using a long and short term memory (LSTM) recurrent neural network (RNN) model.
Universal Segmentation of 33 Anatomies
Firstly, while it is ideal to learn such a model from a large-scale, fully-annotated dataset, it is practically hard to curate such a dataset.
MixCL: Pixel label matters to contrastive learning
It is essential for medical image analysis, which is often notorious for its lack of annotations.
Multi-Modal Masked Pre-Training for Monocular Panoramic Depth Completion
To deal with the PDC task, we train a deep network that takes both depth and image as inputs for the dense panoramic depth recovery.
Industrial Style Transfer with Large-scale Geometric Warping and Content Preservation
Given a source product, a target product, and an art style image, our method produces a neural warping field that warps the source shape to imitate the geometric style of the target and a neural texture transformation network that transfers the artistic style to the warped source product.
Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning
Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Federated Learning-Based Localization with Heterogeneous Fingerprint Database
Fingerprint-based localization plays an important role in indoor location-based services, where the position information is usually collected in distributed clients and gathered in a centralized server.
Nested Collaborative Learning for Long-Tailed Visual Recognition
NCL consists of two core components, namely Nested Individual Learning (NIL) and Nested Balanced Online Distillation (NBOD), which focus on the individual supervised learning for each single expert and the knowledge transferring among multiple experts, respectively.
Ranked #6 on
Long-tail Learning
on CIFAR-10-LT (ρ=50)
Energy-Efficient IRS-Aided NOMA Beamforming for 6G Wireless Communications
This manuscript presents an energy-efficient alternating optimization framework based on intelligent reflective surfaces (IRS) aided non-orthogonal multiple access beamforming (NOMA-BF) system for 6G wireless communications.
Semi-Data-Aided Channel Estimation for MIMO Systems via Reinforcement Learning
Data-aided channel estimation is a promising solution to improve channel estimation accuracy by exploiting data symbols as pilot signals for updating an initial channel estimate.
MC-UNet Multi-module Concatenation based on U-shape Network for Retinal Blood Vessels Segmentation
The proposed network structure retains three layers the essential structure of U-Net, in which the atrous convolution combining the multi-kernel pooling blocks are designed to obtain more contextual information.
A3CLNN: Spatial, Spectral and Multiscale Attention ConvLSTM Neural Network for Multisource Remote Sensing Data Classification
The problem of effectively exploiting the information multiple data sources has become a relevant but challenging research topic in remote sensing.
Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives
Therefore, remote sensing image understanding often faces the problems of incomplete, inexact, and inaccurate supervised information, which will affect the breadth and depth of remote sensing applications.
CoDo: Contrastive Learning with Downstream Background Invariance for Detection
To bridge the performance gap, we propose a novel object-level self-supervised learning method, called Contrastive learning with Downstream background invariance (CoDo).
A Tale of Two Flows: Cooperative Learning of Langevin Flow and Normalizing Flow Toward Energy-Based Model
Under the short-run non-mixing MCMC scenario, the estimation of the energy-based model is shown to follow the perturbation of maximum likelihood, and the short-run Langevin flow and the normalizing flow form a two-flow generator that we call CoopFlow.
Providing Location Information at Edge Networks: A Federated Learning-Based Approach
Recently, the development of mobile edge computing has enabled exhilarating edge artificial intelligence (AI) with fast response and low communication cost.
CompleteDT: Point Cloud Completion with Dense Augment Inference Transformers
Point cloud completion task aims to predict the missing part of incomplete point clouds and generate complete point clouds with details.
Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, the latest technological advance of deep learning, has gained prevalence in natural language processing or computer vision.
Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles
We introduce a new lightweight convolution technique, GSConv, to lighten the model but maintain the accuracy.
Design of optical voltage sensor based on electric field regulation and rotating isomerism electrode
This technology could shift the measured signal frequency band from near 50 Hz moved to several kilometer Hz, so as to make the output signal avoid the interference from low-frequency temperature drift, stress birefringence and vibration, leading to higher stability and reliability.
A Self-Guided Framework for Radiology Report Generation
Moreover, SGF successfully improves the accuracy and length of medical report generation by incorporating a similarity comparison mechanism that imitates the process of human self-improvement through compar-ative practice.
Kernel Attention Transformer (KAT) for Histopathology Whole Slide Image Classification
Transformer has been widely used in histopathology whole slide image (WSI) classification for the purpose of tumor grading, prognosis analysis, etc.
Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images Analysis
In this paper, we proposed a novel contrastive representation learning framework named Lesion-Aware Contrastive Learning (LACL) for histopathology whole slide image analysis.
Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection
Besides, by leveraging full training set and the additional 48K raw images of KITTI, it can further improve the MonoFlex by +4. 65% improvement on AP@0. 7 for car detection, reaching 18. 54% AP@0. 7, which ranks the 1st place among all monocular based methods on KITTI test leaderboard.
Network medicine framework reveals generic herb-symptom effectiveness of Traditional Chinese Medicine
We validate our framework with real-world hospital patient data by showing that (1) shorter network distance between symptoms of inpatients correlates with higher relative risk (co-occurrence), and (2) herb-symptom network proximity is indicative of patients' symptom recovery rate after herbal treatment.
Global Attention-based Encoder-Decoder LSTM Model for Temperature Prediction of Permanent Magnet Synchronous Motors
Temperature monitoring is critical for electrical motors to determine if device protection measures should be executed.
SIND: A Drone Dataset at Signalized Intersection in China
Then, the behaviors of traffic light violations in SIND are recorded.
CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion
As such, we propose a novel camera-LiDAR fusion 3D MOT framework based on the Combined Appearance-Motion Optimization (CAMO-MOT), which uses both camera and LiDAR data and significantly reduces tracking failures caused by occlusion and false detection.
Fairness in Medical Image Analysis and Healthcare: A Literature Survey
Machine learning-enabled medical imaging analysis has become a vital part of the automatic diagnosis system.
A Cooperative Perception System Robust to Localization Errors
We propose a distributed object-level cooperative perception system called OptiMatch, in which the detected 3D bounding boxes and local state information are shared between the connected vehicles.
Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control
The explosive growth of dynamic and heterogeneous data traffic brings great challenges for 5G and beyond mobile networks.
PeSOTIF: a Challenging Visual Dataset for Perception SOTIF Problems in Long-tail Traffic Scenarios
Perception algorithms in autonomous driving systems confront great challenges in long-tail traffic scenarios, where the problems of Safety of the Intended Functionality (SOTIF) could be triggered by the algorithm performance insufficiencies and dynamic operational environment.
Information-guided pixel augmentation for pixel-wise contrastive learning
To the best of our knowledge, we are the first to propose a pixel augmentation method with a pixel granularity for enhancing unsupervised pixel-wise contrastive learning.
Normal Transformer: Extracting Surface Geometry from LiDAR Points Enhanced by Visual Semantics
High-quality estimation of surface normal can help reduce ambiguity in many geometry understanding problems, such as collision avoidance and occlusion inference.
DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion
Unsupervised depth completion aims to recover dense depth from the sparse one without using the ground-truth annotation.
Curriculum Temperature for Knowledge Distillation
In this paper, we propose a simple curriculum-based technique, termed Curriculum Temperature for Knowledge Distillation (CTKD), which controls the task difficulty level during the student's learning career through a dynamic and learnable temperature.
No driver, No Regulation? --Online Legal Driving Behavior Monitoring for Self-driving Vehicles
To address these challenges, this paper aims to digitize traffic law comprehensively and provide an application for online monitoring of legal driving behavior for autonomous vehicles.
Robust Sum-Rate Maximization in Transmissive RMS Transceiver-Enabled SWIPT Networks
Since the coupling of optimization variables, the whole optimization problem is non-convex and cannot be solved directly.
Reversible Column Networks
Such architectural scheme attributes RevCol very different behavior from conventional networks: during forward propagation, features in RevCol are learned to be gradually disentangled when passing through each column, whose total information is maintained rather than compressed or discarded as other network does.
Ranked #8 on
Semantic Segmentation
on ADE20K
(using extra training data)
SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network for Object Segmentation
This paper proposes a novel self-supervised based Cut-and-Paste GAN to perform foreground object segmentation and generate realistic composite images without manual annotations.
Failure Detection for Motion Prediction of Autonomous Driving: An Uncertainty Perspective
Motion prediction is essential for safe and efficient autonomous driving.
How Does Traffic Environment Quantitatively Affect the Autonomous Driving Prediction?
The results indicate that the deep ensemble-based method has advantages in improving prediction robustness and estimating epistemic uncertainty.
Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning
Proactive edge association is capable of improving wireless connectivity at the cost of increased handover (HO) frequency and energy consumption, while relying on a large amount of private information sharing required for decision making.
Structure Flow-Guided Network for Real Depth Super-Resolution
Specifically, our framework consists of a cross-modality flow-guided upsampling network (CFUNet) and a flow-enhanced pyramid edge attention network (PEANet).
AMD: Adaptive Masked Distillation for Object Detection
As a general model compression paradigm, feature-based knowledge distillation allows the student model to learn expressive features from the teacher counterpart.
Recurrent Structure Attention Guidance for Depth Super-Resolution
Second, instead of the coarse concatenation guidance, we propose a recurrent structure attention block, which iteratively utilizes the latest depth estimation and the image features to jointly select clear patterns and boundaries, aiming at providing refined guidance for accurate depth recovery.
InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation
Moreover, they cannot explore which 3D geometric characteristics are essential to alleviate the catastrophic forgetting on old classes of 3D objects.
EfficientFace: An Efficient Deep Network with Feature Enhancement for Accurate Face Detection
Besides, this is conducive to estimating the locations of faces and enhancing the descriptive power of face features.
Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning
The contribution of the new DP mechanism to the convergence and accuracy of privacy-preserving FL is corroborated, compared to the state-of-the-art Gaussian noise mechanism with a persistent noise amplitude.
Non-aligned supervision for Real Image Dehazing
In particular, we explore a non-alignment scenario that a clear reference image, unaligned with the input hazy image, is utilized to supervise the dehazing network.
O2CTA: Introducing Annotations from OCT to CCTA in Coronary Plaque Analysis
Therefore, it is clinically critical to introduce annotations of plaque tissue and lumen characteristics from OCT to paired CCTA scans, denoted as \textbf{the O2CTA problem} in this paper.
BEVHeight: A Robust Framework for Vision-based Roadside 3D Object Detection
In essence, instead of predicting the pixel-wise depth, we regress the height to the ground to achieve a distance-agnostic formulation to ease the optimization process of camera-only perception methods.
Ranked #3 on
3D Object Detection
on Rope3D
Frozen Language Model Helps ECG Zero-Shot Learning
In downstream classification tasks, METS achieves around 10% improvement in performance without using any annotated data via zero-shot classification, compared to other supervised and SSL baselines that rely on annotated data.
Pi-ViMo: Physiology-inspired Robust Vital Sign Monitoring using mmWave Radars
In this paper, we address these limitations and present "Pi-ViMo", a non-contact Physiology-inspired Robust Vital Sign Monitoring system, using mmWave radars.
Boundary-to-Solution Mapping for Groundwater Flows in a Toth Basin
In this paper, the authors propose a new approach to solving the groundwater flow equation in the Toth basin of arbitrary top and bottom topographies using deep learning.
LogoNet: a fine-grained network for instance-level logo sketch retrieval
To our knowledge, this is the first publicly available instance-level logo sketch dataset.
Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy
Specifically, we first design a random sparsification algorithm to retain a fraction of the gradient elements in each client's local training, thereby mitigating the performance degradation induced by DP and and reducing the number of transmission parameters over wireless channels.
Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning
In this paper, we design two-level incentive mechanisms for the HFL with a two-tiered computing structure to encourage the participation of entities in each tier in the HFL training.
Collaborative Multi-BS Power Management for Dense Radio Access Network using Deep Reinforcement Learning
Network energy efficiency is a main pillar in the design and operation of wireless communication systems.
Informative Data Selection with Uncertainty for Multi-modal Object Detection
To quantify the correlation in multi-modal information, we model the uncertainty, as the inverse of data information, in different modalities and embed it in the bounding box generation.
ScatterFormer: Locally-Invariant Scattering Transformer for Patient-Independent Multispectral Detection of Epileptiform Discharges
Patient-independent detection of epileptic activities based on visual spectral representation of continuous EEG (cEEG) has been widely used for diagnosing epilepsy.
Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing
However, it suffers from issues in terms of communication, resource of MTs, and privacy.
Human Machine Co-adaption Interface via Cooperation Markov Decision Process System
Based on the proposed co-adaptive MDPs, the simulation study indicates the non-stationary problem can be mitigated using various proposed Policy Improvement approaches.
Model-based Reinforcement Learning
Multi-agent Reinforcement Learning
+1
Path Planning for Air-Ground Robot Considering Modal Switching Point Optimization
A robot for the field application environment was proposed, and a lightweight global spatial planning technique for the robot based on the graph-search algorithm taking mode switching point optimization into account, with an emphasis on energy efficiency, searching speed, and the viability of real deployment.
Self-Aware Trajectory Prediction for Safe Autonomous Driving
Trajectory prediction is one of the key components of the autonomous driving software stack.
EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection
In face detection, low-resolution faces, such as numerous small faces of a human group in a crowded scene, are common in dense face prediction tasks.
Variable Radiance Field for Real-Life Category-Specifc Reconstruction from Single Image
Our key contributions are: (1) We parameterize the geometry and appearance of the object using a multi-scale global feature extractor, which avoids frequent point-wise feature retrieval and camera dependency.
Learnable Differencing Center for Nighttime Depth Perception
Depth completion is the task of recovering dense depth maps from sparse ones, usually with the help of color images.
NCL++: Nested Collaborative Learning for Long-Tailed Visual Recognition
To achieve the collaborative learning in long-tailed learning, the balanced online distillation is proposed to force the consistent predictions among different experts and augmented copies, which reduces the learning uncertainties.
Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning
As an efficient distributed machine learning approach, Federated learning (FL) can obtain a shared model by iterative local model training at the user side and global model aggregating at the central server side, thereby protecting privacy of users.
General vs. Long-Tailed Age Estimation: An Approach to Kill Two Birds with One Stone
Driven by this, some works suggest that each class should be treated equally to improve performance in tail classes (with a minority of samples), which can be summarized as Long-tailed Age Estimation.
Bayesian Linear Regression with Cauchy Prior and Its Application in Sparse MIMO Radar
In this paper, a sparse signal recovery algorithm using Bayesian linear regression with Cauchy prior (BLRC) is proposed.
Creative Birds: Self-Supervised Single-View 3D Style Transfer
Our focus lies primarily on birds, a popular subject in 3D reconstruction, for which no existing single-view 3D transfer methods have been developed. The method we propose seeks to generate a 3D mesh shape and texture of a bird from two single-view images.
UniAP: Unifying Inter- and Intra-Layer Automatic Parallelism by Mixed Integer Quadratic Programming
To the best of our knowledge, UniAP is the first parallel method that can jointly optimize the two categories of parallel strategies to find an optimal solution.
Aspect based sentimental analysis for travellers' reviews
The results provide tangible reasons to use aspect based sentimental analysis in order to understand more the travellers and spot airport services that are in need for improvement.
Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity
With the rapid proliferation of smart mobile devices, federated learning (FL) has been widely considered for application in wireless networks for distributed model training.
Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosis
In this paper, a dynamic dual-graph fusion convolutional network is proposed to improve Alzheimer's disease (AD) diagnosis performance.
A One-dimensional HEVC video steganalysis method using the Optimality of Predicted Motion Vectors
And then, we define the optimal rate of MVP in HEVC video as a steganalysis feature.
AltNeRF: Learning Robust Neural Radiance Field via Alternating Depth-Pose Optimization
Neural Radiance Fields (NeRF) have shown promise in generating realistic novel views from sparse scene images.
SkipcrossNets: Adaptive Skip-cross Fusion for Road Detection
Multi-modal fusion is increasingly being used for autonomous driving tasks, as images from different modalities provide unique information for feature extraction.
Fragment and Integrate Network (FIN): A Novel Spatial-Temporal Modeling Based on Long Sequential Behavior for Online Food Ordering Click-Through Rate Prediction
(ii) Integrate Network (IN) builds a new integrated sequence by utilizing spatial-temporal interaction on MSS and captures the comprehensive spatial-temporal representation by modeling the integrated sequence with a complicated attention.
RigNet++: Semantic Assisted Repetitive Image Guided Network for Depth Completion
To tackle these challenges, we explore a repetitive design in our image guided network to gradually and sufficiently recover depth values.
DMKD: Improving Feature-based Knowledge Distillation for Object Detection Via Dual Masking Augmentation
Recent mainstream masked distillation methods function by reconstructing selectively masked areas of a student network from the feature map of its teacher counterpart.
Create Your World: Lifelong Text-to-Image Diffusion
Text-to-image generative models can produce diverse high-quality images of concepts with a text prompt, which have demonstrated excellent ability in image generation, image translation, etc.
A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand prediction
Along with the proliferation of electric vehicles (EVs), optimizing the use of EV charging space can significantly alleviate the growing load on intelligent transportation systems.
TSSAT: Two-Stage Statistics-Aware Transformation for Artistic Style Transfer
To address this issue, we imitate the drawing process of humans and propose a Two-Stage Statistics-Aware Transformation (TSSAT) module, which first builds the global style foundation by aligning the global statistics of content and style features and then further enriches local style details by swapping the local statistics (instead of local features) in a patch-wise manner, significantly improving the stylization effects.
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems
Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT).
BEVHeight++: Toward Robust Visual Centric 3D Object Detection
In essence, we regress the height to the ground to achieve a distance-agnostic formulation to ease the optimization process of camera-only perception methods.
MonoGAE: Roadside Monocular 3D Object Detection with Ground-Aware Embeddings
We discover that most existing monocular 3D object detectors rely on the ego-vehicle prior assumption that the optical axis of the camera is parallel to the ground.
