Search Results for author:
Found 31 papers, 10 papers with code
Referring Flexible Image Restoration
These situations and requirements shed light on a new challenge in image restoration, where a model must perceive and remove specific degradation types specified by human commands in images with multiple degradations.
How to characterize imprecision in multi-view clustering?
Moreover, if an object is in the overlapping region of several singleton clusters, it can be assigned to a meta-cluster, defined as the union of these singleton clusters, to characterize the local imprecision in the result.
Few-Shot Object Detection: Research Advances and Challenges
This paper presents a comprehensive survey to review the significant advancements in the field of FSOD in recent years and summarize the existing challenges and solutions.
DE$^3$-BERT: Distance-Enhanced Early Exiting for BERT based on Prototypical Networks
DE$^3$-BERT implements a hybrid exiting strategy that supplements classic entropy-based local information with distance-based global information to enhance the estimation of prediction correctness for more reliable early exiting decisions.
Achelous++: Power-Oriented Water-Surface Panoptic Perception Framework on Edge Devices based on Vision-Radar Fusion and Pruning of Heterogeneous Modalities
Urban water-surface robust perception serves as the foundation for intelligent monitoring of aquatic environments and the autonomous navigation and operation of unmanned vessels, especially in the context of waterway safety.
Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review
This review focuses on exploring different radar data representations utilized in autonomous driving systems.
Survey on deep learning in multimodal medical imaging for cancer detection
Finally, we discuss the current scope of work and provide directions for the future development of multimodal cancer detection.
InfoFlowNet: A Multi-head Attention-based Self-supervised Learning Model with Surrogate Approach for Uncovering Brain Effective Connectivity
In this study, we introduce a novel brain causal inference model named InfoFlowNet, which leverages the self-attention mechanism to capture associations among electroencephalogram (EEG) time series.
EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learning
However, existing GAN-based generative ZSL methods are based on hand-crafted models, which cannot adapt to various datasets/scenarios and fails to model instability.
Generative Adversarial Network
Neural Architecture Search
+1
WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmark for Autonomous Driving on Water Surfaces
This work presents WaterScenes, the first multi-task 4D radar-camera fusion dataset for autonomous driving on water surfaces.
Ranked #1 on
Object Detection
on WaterScenes
A Survey on Fairness-aware Recommender Systems
As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which makes them vital and indispensable to human society in the information era.
GBG++: A Fast and Stable Granular Ball Generation Method for Classification
Second, considering the influence of the sample size within the GB on the GB's quality, based on the GBG++ method, an improved GB-based $k$-nearest neighbors algorithm (GB$k$NN++) is presented, which can reduce misclassification at the class boundary.
Assessor-Guided Learning for Continual Environments
This paper proposes an assessor-guided learning strategy for continual learning where an assessor guides the learning process of a base learner by controlling the direction and pace of the learning process thus allowing an efficient learning of new environments while protecting against the catastrophic interference problem.
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.
Machine learning techniques for the Schizophrenia diagnosis: A comprehensive review and future research directions
Moreover, for accurate diagnosis of SCZ, researchers have used machine learning (ML) algorithms for the past decade to distinguish the brain patterns of healthy and SCZ brains using MRI and fMRI images.
Efficient Evaluation Methods for Neural Architecture Search: A Survey
In surveying each category, we further discuss the design principles and analyze the strength and weaknesses to clarify the landscape of existing EEMs, thus making easily understanding the research trends of EEMs.
Spectral Cross-Domain Neural Network with Soft-adaptive Threshold Spectral Enhancement
The robust performance of SCDNN provides a new perspective to exploit knowledge across deep learning models from time and spectral domains.
FAF: A novel multimodal emotion recognition approach integrating face, body and text
In this paper, we developed a large multimodal emotion dataset, named "HED" dataset, to facilitate the emotion recognition task, and accordingly propose a multimodal emotion recognition method.
FedTP: Federated Learning by Transformer Personalization
Instead of using a vanilla personalization mechanism that maintains personalized self-attention layers of each client locally, we develop a learn-to-personalize mechanism to further encourage the cooperation among clients and to increase the scablability and generalization of FedTP.
A Novel Approach for Optimum-Path Forest Classification Using Fuzzy Logic
In the past decades, fuzzy logic has played an essential role in many research areas.
Fuzzy temporal convolutional neural networks in P300-based Brain-computer interface for smart home interaction
The remarkable performance of the proposed model, EEG-TCFNet, and the general integration of fuzzy units to other classifiers would pave the way for enhanced P300-based BCIs for smart home interaction within natural settings.
A Unified Granular-ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set
The granular-ball rough set can simultaneously represent Pawlak rough sets, and the neighborhood rough set, so as to realize the unified representation of the two.
Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information
The dual discriminator design aims to improve the edge information in MRI reconstruction.
Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images
The multi-domain shift problem for the multi-center and multi-scanner studies is therefore nontrivial that is also crucial for a dependable recognition and critical for reproducible and objective diagnosis and prognosis.
DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays
The global pandemic of COVID-19 is continuing to have a significant effect on the well-being of global population, increasing the demand for rapid testing, diagnosis, and treatment.
CDE-GAN: Cooperative Dual Evolution Based Generative Adversarial Network
In essence, CDE-GAN incorporates dual evolution with respect to the generator(s) and discriminators into a unified evolutionary adversarial framework to conduct effective adversarial multi-objective optimization.
Discovering High Utility-Occupancy Patterns from Uncertain Data
To find patterns that can represent the supporting transaction, a recent study was conducted to mine high utility-occupancy patterns whose contribution to the utility of the entire transaction is greater than a certain value.
Databases
A General Approach for Using Deep Neural Network for Digital Watermarking
Technologies of the Internet of Things (IoT) facilitate digital contents such as images being acquired in a massive way.
Automatic Construction of Multi-layer Perceptron Network from Streaming Examples
The advantage of NADINE, namely elastic structure and online learning trait, is numerically validated using nine data stream classification and regression problems where it demonstrates performance improvement over prominent algorithms in all problems.
Effects of Repetitive SSVEPs on EEG Complexity using Multiscale Inherent Fuzzy Entropy
These results suggest that multiscale inherent fuzzy entropy is an EEG pattern with which brain complexity can be assessed using repetitive SSVEP stimuli.
Signal Processing
Semi-supervised Feature Learning For Improving Writer Identification
In this study, a semi-supervised feature learning pipeline was proposed to improve the performance of writer identification by training with extra unlabeled data and the original labeled data simultaneously.
