Search Results for author:
Found 74 papers, 27 papers with code
Achieving Stability and Optimality: Control Strategy for a Wind Turbine Supplying an Electrolyzer in the Islanded Storage-less Microgrid
Wind power generation supplying electrolyzers in islanded microgrids is an essential technical pathway for green hydrogen production, attracting growing attention in the transition towards net zero carbon emissions.
C2PD: Continuity-Constrained Pixelwise Deformation for Guided Depth Super-Resolution
Guided depth super-resolution (GDSR) has demonstrated impressive performance across a wide range of domains, with numerous methods being proposed.
Ultrasound-QBench: Can LLMs Aid in Quality Assessment of Ultrasound Imaging?
With the dramatic upsurge in the volume of ultrasound examinations, low-quality ultrasound imaging has gradually increased due to variations in operator proficiency and imaging circumstances, imposing a severe burden on diagnosis accuracy and even entailing the risk of restarting the diagnosis in critical cases.
Evaluating the Design Features of an Intelligent Tutoring System for Advanced Mathematics Learning
Xiaomai is an intelligent tutoring system (ITS) designed to help Chinese college students in learning advanced mathematics and preparing for the graduate school math entrance exam.
SGTC: Semantic-Guided Triplet Co-training for Sparsely Annotated Semi-Supervised Medical Image Segmentation
To address these issues, we propose a novel Semantic-Guided Triplet Co-training (SGTC) framework, which achieves high-end medical image segmentation by only annotating three orthogonal slices of a few volumetric samples, significantly alleviating the burden of radiologists.
Twin Trigger Generative Networks for Backdoor Attacks against Object Detection
Furthermore, the triggers for most existing backdoor attacks on object detection are manually generated, requiring prior knowledge and consistent patterns between the training and inference stages.
CLIP-SCGI: Synthesized Caption-Guided Inversion for Person Re-Identification
To address quality issues in generated captions, we introduce a caption-guided inversion module that captures semantic attributes from images by converting relevant visual information into pseudo-word tokens based on the descriptions.
UniEmoX: Cross-modal Semantic-Guided Large-Scale Pretraining for Universal Scene Emotion Perception
To the best of our knowledge, this is the first large-scale pretraining framework that integrates psychological theories with contemporary contrastive learning and masked image modeling techniques for emotion analysis across diverse scenarios.
Few-Shot Class-Incremental Learning with Non-IID Decentralized Data
Within this framework, a noise-aware generative replay module is developed to fine-tune local models with a balance of new and replay data, while generating synthetic data of new classes to further expand the replay buffer for future tasks.
class-incremental learning
Few-Shot Class-Incremental Learning
+3
Optimizing Federated Graph Learning with Inherent Structural Knowledge and Dual-Densely Connected GNNs
Federated Graph Learning (FGL) is an emerging technology that enables clients to collaboratively train powerful Graph Neural Networks (GNNs) in a distributed manner without exposing their private data.
FacialPulse: An Efficient RNN-based Depression Detection via Temporal Facial Landmarks
Depression is a prevalent mental health disorder that significantly impacts individuals' lives and well-being.
A nonparametric test for diurnal variation in spot correlation processes
This suggests diurnal variation in the correlation process is a nontrivial effect in practice.
Distribution Alignment for Fully Test-Time Adaptation with Dynamic Online Data Streams
Given a model trained on source data, Test-Time Adaptation (TTA) enables adaptation and inference in test data streams with domain shifts from the source.
ORCDF: An Oversmoothing-Resistant Cognitive Diagnosis Framework for Student Learning in Online Education Systems
This paper contends that the oversmoothing issue arises from that existing CDMs seldom utilize response signals on exercises in the learning part but only use them as labels in the assessing part.
SGSM: A Foundation-model-like Semi-generalist Sensing Model
The significance of intelligent sensing systems is growing in the realm of smart services.
XctDiff: Reconstruction of CT Images with Consistent Anatomical Structures from a Single Radiographic Projection Image
In this paper, we present XctDiff, an algorithm framework for reconstructing CT from a single radiograph, which decomposes the reconstruction process into two easily controllable tasks: feature extraction and CT reconstruction.
Annotation Guidelines-Based Knowledge Augmentation: Towards Enhancing Large Language Models for Educational Text Classification
GPT 4. 0 with AGKA few-shot outperforms full-shot fine-tuned models such as BERT and RoBERTa on simple binary classification datasets.
Safety-Critical Control of Euler-Lagrange Systems Subject to Multiple Obstacles and Velocity Constraints
This paper studies the safety-critical control problem for Euler-Lagrange (EL) systems subject to multiple ball obstacles and velocity constraints in accordance with affordable velocity ranges.
MINet: Multi-scale Interactive Network for Real-time Salient Object Detection of Strip Steel Surface Defects
Embarking on this module, we propose a lightweight Multi-scale Interactive Network (MINet) to conduct real-time salient object detection of strip steel surface defects.
Reinforcement Learning Based Robust Volt/Var Control in Active Distribution Networks With Imprecisely Known Delay
Due to the fluctuation and intermittency of PV generation, the state gap, arising from time-inconsistent states and exacerbated by imprecisely known system delays, significantly impacts the accuracy of voltage control.
WiOpen: A Robust Wi-Fi-based Open-set Gesture Recognition Framework
This uncertainty, resulting from noise and domains, leads to widely scattered and irregular data distributions in collected Wi-Fi sensing data.
A Survey on Generative AI and LLM for Video Generation, Understanding, and Streaming
This paper offers an insightful examination of how currently top-trending AI technologies, i. e., generative artificial intelligence (Generative AI) and large language models (LLMs), are reshaping the field of video technology, including video generation, understanding, and streaming.
Deep Reinforcement Learning for Voltage Control and Renewable Accommodation Using Spatial-Temporal Graph Information
Renewable energy resources (RERs) have been increasingly integrated into distribution networks (DNs) for decarbonization.
Context-Aware Interaction Network for RGB-T Semantic Segmentation
Specifically, we propose a Context-Aware Complementary Reasoning (CACR) module aimed at establishing the complementary relationship between multimodal features with the long-term context in both spatial and channel dimensions.
Ranked #6 on
Semantic Segmentation
on SYN-UDTIRI
Dynamic Routing for Integrated Satellite-Terrestrial Networks: A Constrained Multi-Agent Reinforcement Learning Approach
To address these challenges, we study packet routing with ground stations and satellites working jointly to transmit packets, while prioritizing fast communication and meeting energy efficiency and packet loss requirements.
Viewport Prediction for Volumetric Video Streaming by Exploring Video Saliency and Trajectory Information
To our knowledge, this is the first comprehensive study of viewport prediction in volumetric video streaming.
Salient Object Detection in Optical Remote Sensing Images Driven by Transformer
KTM models the contextual correlation knowledge of two middle-level features of different scales based on the self-attention mechanism, and transfers the knowledge to the raw features to generate more discriminative features.
No-Service Rail Surface Defect Segmentation via Normalized Attention and Dual-scale Interaction
DIB is specifically designed for the feature interaction of the enhanced features.
Deep Neural Networks in Video Human Action Recognition: A Review
Our current related research addresses multiple novel proposed research works and compares their advantages and disadvantages between the derived deep learning frameworks rather than machine learning frameworks.
Shared and Private Information Learning in Multimodal Sentiment Analysis with Deep Modal Alignment and Self-supervised Multi-Task Learning
In this work, we propose a deep modal shared information learning module based on the covariance matrix to capture the shared information between modalities.
Multimodal Sentiment Analysis: A Survey
Multimodal sentiment analysis has become an important research area in the field of artificial intelligence.
SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention
To address this issue, we propose a slice grouped domain attention (SGDA) module to enhance the generalization capability of the pulmonary nodule detection networks.
Incipient Fault Detection in Power Distribution System: A Time-Frequency Embedded Deep Learning Based Approach
Incipient fault detection in power distribution systems is crucial to improve the reliability of the grid.
Lightweight Salient Object Detection in Optical Remote-Sensing Images via Semantic Matching and Edge Alignment
Then, semantic kernels are used to activate salient object locations in two groups of high-level features through dynamic convolution operations in DSMM.
Towards Reliable Item Sampling for Recommendation Evaluation
Since Rendle and Krichene argued that commonly used sampling-based evaluation metrics are "inconsistent" with respect to the global metrics (even in expectation), there have been a few studies on the sampling-based recommender system evaluation.
Fraudulent User Detection Via Behavior Information Aggregation Network (BIAN) On Large-Scale Financial Social Network
In financial fraud detection, the modus operandi of criminals can be identified by analyzing user profile and their behaviors such as transaction, loaning etc.
RGB-T Semantic Segmentation with Location, Activation, and Sharpening
The highlight of LASNet is that we fully consider the characteristics of cross-modal features at different levels, and accordingly propose three specific modules for better segmentation.
Ranked #31 on
Thermal Image Segmentation
on MFN Dataset
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation
In this paper, we propose task-specific meta distillation that simultaneously learns two models in meta-learning: a large teacher model and a small student model.
Vision-based Uneven BEV Representation Learning with Polar Rasterization and Surface Estimation
In this work, we propose PolarBEV for vision-based uneven BEV representation learning.
Estimating spot volatility under infinite variation jumps with dependent market microstructure noise
Jumps and market microstructure noise are stylized features of high-frequency financial data.
Quantifying Spatial Under-reporting Disparities in Resident Crowdsourcing
A major concern is that residents do not report problems at the same rates, with heterogeneous reporting delays directly translating to downstream disparities in how quickly incidents can be addressed.
Adjacent Context Coordination Network for Salient Object Detection in Optical Remote Sensing Images
As the key component of ACCoNet, ACCoM activates the salient regions of output features of the encoder and transmits them to the decoder.
Robust Event Triggering Control for Lateral Dynamics of Intelligent Vehicles with Designable Inter-event Times
In this brief, an improved event-triggered update mechanism (ETM) for the linear quadratic regulator is proposed to solve the lateral motion control problem of intelligent vehicle under bounded disturbances.
Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV Feedback
Most of the current prediction methods combining saliency detection and FoV information neither take into account that the distortion of projected 360-degree videos can invalidate the weight sharing of traditional convolutional networks, nor do they adequately consider the difficulty of obtaining complete multi-user FoV information, which degrades the prediction performance.
Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation
Then, following the coarse-to-fine strategy, we generate an initial coarse saliency map from high-level semantic features in a Correlation Module (CorrM).
Deep Reinforcement Learning for Optimal Power Flow with Renewables Using Graph Information
Considering uncertainties and voltage fluctuation issues introduced by RERs, in this paper, we propose a deep reinforcement learning (DRL)-based strategy leveraging spatial-temporal (ST) graphical information of power systems, to dynamically search for the optimal operation, i. e., optimal power flow (OPF), of power systems with a high uptake of RERs.
Multi-Content Complementation Network for Salient Object Detection in Optical Remote Sensing Images
In this paper, we propose a novel Multi-Content Complementation Network (MCCNet) to explore the complementarity of multiple content for RSI-SOD.
On the regularization landscape for the linear recommendation models
Recently, a wide range of recommendation algorithms inspired by deep learning techniques have emerged as the performance leaders several standard recommendation benchmarks.
Spatio-Temporal Self-Attention Network for Video Saliency Prediction
3D convolutional neural networks have achieved promising results for video tasks in computer vision, including video saliency prediction that is explored in this paper.
On Sampling Top-K Recommendation Evaluation
Recently, Rendle has warned that the use of sampling-based top-$k$ metrics might not suffice.
Towards a Better Understanding of Linear Models for Recommendation
Through the derivation and analysis of the closed-form solutions for two basic regression and matrix factorization approaches, we found these two approaches are indeed inherently related but also diverge in how they "scale-down" the singular values of the original user-item interaction matrix.
Elastic Deep Learning in Multi-Tenant GPU Clusters
We study how to support elasticity, that is, the ability to dynamically adjust the parallelism (i. e., the number of GPUs), for deep neural network (DNN) training in a GPU cluster.
On Estimating Recommendation Evaluation Metrics under Sampling
The proposed approaches either are rather uninformative (linking sampling to metric evaluation) or can only work on simple metrics, such as Recall/Precision (Krichene and Rendle 2020; Li et al. 2020).
Referring Segmentation in Images and Videos with Cross-Modal Self-Attention Network
In this paper, we propose a cross-modal self-attention (CMSA) module to utilize fine details of individual words and the input image or video, which effectively captures the long-range dependencies between linguistic and visual features.
Ranked #5 on
Referring Expression Segmentation
on J-HMDB
(Precision@0.9 metric)
Personal Fixations-Based Object Segmentation with Object Localization and Boundary Preservation
In this paper, we focus on Personal Fixations-based Object Segmentation (PFOS) to address issues in previous studies, such as the lack of appropriate dataset and the ambiguity in fixations-based interaction.
DRLE: Decentralized Reinforcement Learning at the Edge for Traffic Light Control in the IoV
To this end, we propose a Decentralized Reinforcement Learning at the Edge for traffic light control in the IoV (DRLE).
Computation Offloading in Beyond 5G Networks: A Distributed Learning Framework and Applications
Facing the trend of merging wireless communications and multi-access edge computing (MEC), this article studies computation offloading in the beyond fifth-generation networks.
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.
Cross-Modal Weighting Network for RGB-D Salient Object Detection
In this paper, we propose a novel Cross-Modal Weighting (CMW) strategy to encourage comprehensive interactions between RGB and depth channels for RGB-D SOD.
Ranked #10 on
RGB-D Salient Object Detection
on NJU2K
Observables in quantum field theory on a collapsing black hole background
In a gravitational collapse scenario, they also depend on the details of the collapsing classical matter which determines the background spacetime.
General Relativity and Quantum Cosmology High Energy Physics - Theory
Large-scale Real-time Personalized Similar Product Recommendations
Similar product recommendation is one of the most common scenes in e-commerce.
Implications of the virus-encoded miRNA and host miRNA in the pathogenicity of SARS-CoV-2
The outbreak of COVID-19 caused by SARS-CoV-2 has rapidly spread worldwide and has caused over 1, 400, 000 infections and 80, 000 deaths.
Genomics Biomolecules
Dual Convolutional LSTM Network for Referring Image Segmentation
Given an input image and a referring expression in the form of a natural language sentence, the goal is to segment the object of interest in the image referred by the linguistic query.
Robust Q-tunable topological induced transparency in metasurface
In this study, we demonstrate first, to the best of our knowledge, robust and dynamically polarization-controlled tunable-high-Q PIT in designed nanostructures metasurface, whose sharp resonance is guaranteed by design and protected against large geometrical imperfections.
Optics Applied Physics
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.
BeSense: Leveraging WiFi Channel Data and Computational Intelligence for Behavior Analysis
The ever evolving informatics technology has gradually bounded human and computer in a compact way.
Deep Saliency Models : The Quest For The Loss Function
We demonstrate that on a fixed network architecture, modifying the loss function can significantly improve (or depreciate) the results, hence emphasizing the importance of the choice of the loss function when designing a model.
Cross-Modal Self-Attention Network for Referring Image Segmentation
This module controls the information flow of features at different levels.
Ranked #16 on
Referring Video Object Segmentation
on Refer-YouTube-VOS
(using extra training data)
AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint Construction in Indoor Localization System
Based on the extended fingerprint database, the accuracy of indoor localization system can be improved with reduced human effort.
Networking and Internet Architecture
Learning Semantic Segmentation with Diverse Supervision
Models based on deep convolutional neural networks (CNN) have significantly improved the performance of semantic segmentation.
Face Parsing via a Fully-Convolutional Continuous CRF Neural Network
In this work, we address the face parsing task with a Fully-Convolutional continuous CRF Neural Network (FC-CNN) architecture.
Computational Model for Predicting Visual Fixations from Childhood to Adulthood
In this paper, we demonstrate that the computational modelling of visual attention, through the use of saccadic model, can be efficiently adapted to emulate the gaze behavior of a specific group of observers.
The SYSU System for the Interspeech 2015 Automatic Speaker Verification Spoofing and Countermeasures Challenge
In order to detect these spoofed speech signals as a countermeasure, we propose a score level fusion approach with several different i-vector subsystems.
