Search Results for author:
Found 47 papers, 16 papers with code
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 #21 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).
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.
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.
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 #9 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 #5 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.
