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Found 54 papers, 5 papers with code
Rethinking Label-specific Features for Label Distribution Learning
This leads to a novel LSFs construction strategy, LIFT-SAP, which enhances LIFT by integrating both distance and direction information of each instance relative to SAPs.
ZeroLM: Data-Free Transformer Architecture Search for Language Models
Neural architecture search (NAS) provides a systematic framework for automating the design of neural network architectures, yet its widespread adoption is hindered by prohibitive computational requirements.
Bridging Visualization and Optimization: Multimodal Large Language Models on Graph-Structured Combinatorial Optimization
This approach allows machines to emulate human-like processing in addressing complex combinatorial challenges.
Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls
Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks.
Machine Learning Innovations in CPR: A Comprehensive Survey on Enhanced Resuscitation Techniques
This survey paper explores the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR).
LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Data
To this end, we propose LOCAL, a highly efficient, easy-to-implement, and constraint-free method for recovering dynamic causal structures.
Linguistic Fuzzy Information Evolution with Random Leader Election Mechanism for Decision-Making Systems
Linguistic fuzzy information evolution is crucial in understanding information exchange among agents.
Cost-Effective Community-Hierarchy-Based Mutual Voting Approach for Influence Maximization in Complex Networks
Second, we develop a method named Cost-Effective Mutual-Influence-based Voting for seed nodes selection.
An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting
Specifically, we first construct a max-margin optimization-based model to model potentially non-monotonic preferences and inconsistent assignment example preference information in each iteration of the incremental preference elicitation process.
A Distance Similarity-based Genetic Optimization Algorithm for Satellite Ground Network Planning Considering Feeding Mode
In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM).
Generation of Granular-Balls for Clustering Based on the Principle of Justifiable Granularity
Efficient and robust data clustering remains a challenging task in the field of data analysis.
DCNN: Dual Cross-current Neural Networks Realized Using An Interactive Deep Learning Discriminator for Fine-grained Objects
Accurate classification of fine-grained images remains a challenge in backbones based on convolutional operations or self-attention mechanisms.
Fine-Grained Image Classification
Weakly-supervised Learning
CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement
Low-light image enhancement (LLIE) aims to improve low-illumination images.
BEND: Bagging Deep Learning Training Based on Efficient Neural Network Diffusion
The originality of BEND comes from the first use of a neural network diffusion model to efficiently build base classifiers for bagging.
Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities
Reinforcement learning (RL) integrated as a component in the EA framework has demonstrated superior performance in recent years.
TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Support
Among them, the spatial aggregation layer adopts a defense mechanism to robustly aggregate local trust, and the temporal aggregation layer applies an attention mechanism for effective learning of temporal patterns.
A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks
Transformer is a deep neural network that employs a self-attention mechanism to comprehend the contextual relationships within sequential data.
A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching
The RL-GP adopts the ensemble population strategies.
Ensemble Reinforcement Learning: A Survey
By offering guidance for future scientific research and engineering applications, this survey significantly contributes to the advancement of ERL.
Accelerated Fuzzy C-Means Clustering Based on New Affinity Filtering and Membership Scaling
However, FCM and its many accelerated variants have low efficiency in the mid-to-late stage of the clustering process.
Exploring Contextual Representation and Multi-Modality for End-to-End Autonomous Driving
Recent perception systems enhance spatial understanding with sensor fusion but often lack full environmental context.
Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation
especially for cohorts with different lung diseases.
ARES: An Efficient Algorithm with Recurrent Evaluation and Sampling-Driven Inference for Maximum Independent Set
The Maximum Independent Set (MIS) problem is a well-known NP-complete problem with a wide range of applications across various fields.
DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAV
Moreover, we also show via an ablation study that our ITS can help achieve a balance between the performance and training efficiency.
Asymmetric Transfer Hashing with Adaptive Bipartite Graph Learning
Specifically, ATH characterizes the domain distribution gap by the discrepancy between two asymmetric hash functions, and minimizes the feature gap with the help of a novel adaptive bipartite graph constructed on cross-domain data.
Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning
However, the existing contrastive learning methods are inadequate for heterogeneous graphs because they construct contrastive views only based on data perturbation or pre-defined structural properties (e. g., meta-path) in graph data while ignore the noises that may exist in both node attributes and graph topologies.
Logistics in the Sky: A Two-phase Optimization Approach for the Drone Package Pickup and Delivery System
Different from the previous urban distribution mode that depends on trucks, this paper proposes a novel package pick-up and delivery mode and system in which multiple drones collaborate with automatic devices.
Vision Transformer with Convolutions Architecture Search
The high-performance backbone network searched by VTCAS introduces the desirable features of convolutional neural networks into the Transformer architecture while maintaining the benefits of the multi-head attention mechanism.
Robust and Precise Facial Landmark Detection by Self-Calibrated Pose Attention Network
We show that by integrating the BALI fields and SCPA model into a novel self-calibrated pose attention network, more facial prior knowledge can be learned and the detection accuracy and robustness of our method for faces with large poses and heavy occlusions have been improved.
An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problem
Vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem, and many models and algorithms have been proposed to solve the VRP and its variants.
A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems
This offers a novel and general paradigm that combines RL with OR approaches to solving scheduling problems, which leverages the respective strengths of RL and OR: The MDP narrows down the search space of the original problem through an RL method, while the mixed-integer programming process is settled by an OR algorithm.
LDNet: End-to-End Lane Marking Detection Approach Using a Dynamic Vision Sensor
Traditional lane detection methods incorporate handcrafted or deep learning-based features followed by postprocessing techniques for lane extraction using frame-based RGB cameras.
Ranked #1 on
Lane Detection
on DET
Online Multi-Object Tracking and Segmentation with GMPHD Filter and Mask-based Affinity Fusion
One affinity, for position and motion, is computed by using the GMPHD filter, and the other affinity, for appearance is computed by using the responses from a single object tracker such as a kernalized correlation filter.
Bottom-up mechanism and improved contract net protocol for the dynamic task planning of heterogeneous Earth observation resources
It also demonstrates that this method can help to efficiently obtain replanning schemes based on original scheme in dynamic environments.
Integrating Variable Reduction Strategy with Evolutionary Algorithm for Solving Nonlinear Equations Systems
To test the effectiveness of VRS in dealing with NESs, this paper integrates VRS into two existing state-of-the-art EA methods (i. e., MONES and DRJADE), respectively.
Exponentially Weighted l_2 Regularization Strategy in Constructing Reinforced Second-order Fuzzy Rule-based Model
In the conventional Takagi-Sugeno-Kang (TSK)-type fuzzy models, constant or linear functions are usually utilized as the consequent parts of the fuzzy rules, but they cannot effectively describe the behavior within local regions defined by the antecedent parts.
G-image Segmentation: Similarity-preserving Fuzzy C-Means with Spatial Information Constraint in Wavelet Space
G-images refer to image data defined on irregular graph domains.
Exploring Thermal Images for Object Detection in Underexposure Regions for Autonomous Driving
A thermal camera captures an image using the heat difference emitted by objects in the infrared spectrum, and object detection in thermal images becomes effective for autonomous driving in challenging conditions.
A Novel Granular-Based Bi-Clustering Method of Deep Mining the Co-Expressed Genes
With the information granules we build a characteristic matrix of the gene data to capture the fluctuation trend of the expression value between consecutive conditions to mine the ideal bi-clusters.
Residual-driven Fuzzy C-Means Clustering for Image Segmentation
Due to its inferior characteristics, an observed (noisy) image's direct use gives rise to poor segmentation results.
Augmentation of the Reconstruction Performance of Fuzzy C-Means with an Optimized Fuzzification Factor Vector
In this paper, to enhance the quality of the degranulation (reconstruction) process, we augment the FCM-based degranulation mechanism by introducing a vector of fuzzification factors (fuzzification factor vector) and setting up an adjustment mechanism to modify the prototypes and the partition matrix.
Granular Computing: An Augmented Scheme of Degranulation Through a Modified Partition Matrix
By proposing the augmented scheme, we dwell on a novel collection of granulation-degranulation mechanisms.
Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions
Agile satellites with advanced attitude maneuvering capability are the new generation of Earth observation satellites (EOSs).
Kullback-Leibler Divergence-Based Fuzzy $C$-Means Clustering Incorporating Morphological Reconstruction and Wavelet Frames for Image Segmentation
Considering these feature sets as data of clustering, an modified FCM algorithm is proposed, which introduces a KL divergence term in the partition matrix into its objective function.
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
In the case of traffic line detection, an essential perception module, many condition should be considered, such as number of traffic lines and computing power of the target system.
Ranked #47 on
Lane Detection
on CULane
(using extra training data)
Residual-Sparse Fuzzy $C$-Means Clustering Incorporating Morphological Reconstruction and Wavelet frames
To further enhance the segmentation effects of the improved FCM algorithm, we also employ the morphological reconstruction to smoothen the labels generated by clustering.
The Learning of Fuzzy Cognitive Maps With Noisy Data: A Rapid and Robust Learning Method With Maximum Entropy
Index Terms-Fuzzy cognitive maps (FCMs), maximum entropy, noisy data, rapid and robust learning.
An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams
DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely gClass, drives the hidden layer.
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
The paper presents advancement of a newly developed ensemble learning algorithm, pENsemble+, where online active learning scenario is incorporated to reduce operator labelling effort.
Evolving Ensemble Fuzzy Classifier
pENsemble adopts a dynamic ensemble structure to output a final classification decision where it features a novel drift detection scenario to grow the ensemble structure.
Aggregation of Classifiers: A Justifiable Information Granularity Approach
In this study, we introduce a new approach to combine multi-classifiers in an ensemble system.
Global and Local Structure Preserving Sparse Subspace Learning: An Iterative Approach to Unsupervised Feature Selection
In this paper, we propose a global and local structure preserving sparse subspace learning (GLoSS) model for unsupervised feature selection.
Entropic one-class classifiers
The one-class classification problem is a well-known research endeavor in pattern recognition.
An adaptive Simulated Annealing-based satellite observation scheduling method combined with a dynamic task clustering strategy
In this study, we present an adaptive Simulated Annealing based scheduling algorithm aggregated with a dynamic task clustering strategy (or ASA-DTC for short) for satellite observation scheduling problems (SOSPs).
