Papers Feature Correlation
“Feature Correlation” 태그가 달린 논문 124편 · 필터 해제
ViGT: Proposal-free Video Grounding with Learnable Token in Transformer
The video grounding (VG) task aims to locate the queried action or event in an untrimmed video based on rich linguistic descriptions. Existing proposal-free methods are trapped in complex interaction between video and qu…
Feature CorrelationregressionVideo GroundingThe Relationship Between Speech Features Changes When You Get Depressed: Feature Correlations for Improving Speed and Performance of Depression Detection
This work shows that depression changes the correlation between features extracted from speech. Furthermore, it shows that using such an insight can improve the training speed and performance of depression detectors base…
Depression DetectionFeature CorrelationVertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks
Vertical Federated Learning (VFL) is a crucial paradigm for training machine learning models on feature-partitioned, distributed data. However, due to privacy restrictions, few public real-world VFL datasets exist for al…
DiversityFeature CorrelationFeature ImportanceFederated Learning+1Feature Representation Learning for NL2SQL Generation Based on Coupling and Decoupling
The NL2SQL task involves parsing natural language statements into SQL queries. While most state-of-the-art methods treat NL2SQL as a slot-filling task and use feature representation learning techniques, they overlook exp…
Feature CorrelationMulti-Task LearningRepresentation Learningslot-filling+1Hardness of Deceptive Certificate Selection
Recent progress towards theoretical interpretability guarantees for AI has been made with classifiers that are based on interactive proof systems. A prover selects a certificate from the datapoint and sends it to a verif…
Feature CorrelationLearning Dynamic Point Cloud Compression via Hierarchical Inter-frame Block Matching
3D dynamic point cloud (DPC) compression relies on mining its temporal context, which faces significant challenges due to DPC's sparsity and non-uniform structure. Existing methods are limited in capturing sufficient tem…
Feature CorrelationMotion CompensationMotion EstimationOptical Flow Estimation+1End-to-End Lidar-Camera Self-Calibration for Autonomous Vehicles
Autonomous vehicles are equipped with a multi-modal sensor setup to enable the car to drive safely. The initial calibration of such perception sensors is a highly matured topic and is routinely done in an automated facto…
Autonomous VehiclesFeature CorrelationTime delay multi-feature correlation analysis to extract subtle dependencies from EEG signals
Electroencephalography (EEG) signals are resultants of extremely complex brain activity. Some details of this hidden dynamics might be accessible through e.g. joint distributions $\rho_{\Delta t}$ of signals of pairs of …
Brain Computer InterfaceDimensionality ReductionEEGFeature CorrelationInstance-Aware Domain Generalization for Face Anti-Spoofing
Face anti-spoofing (FAS) based on domain generalization (DG) has been recently studied to improve the generalization on unseen scenarios. Previous methods typically rely on domain labels to align the distribution of each…
Domain GeneralizationFace Anti-SpoofingFeature Correlation$R^{2}$Former: Unified $R$etrieval and $R$eranking Transformer for Place Recognition
Visual Place Recognition (VPR) estimates the location of query images by matching them with images in a reference database. Conventional methods generally adopt aggregated CNN features for global retrieval and RANSAC-bas…
Feature CorrelationRerankingRetrievalVisual Place RecognitionWhy is plausibility surprisingly problematic as an XAI criterion?
Explainable artificial intelligence (XAI) is motivated by the problem of making AI predictions understandable, transparent, and responsible, as AI becomes increasingly impactful in society and high-stakes domains. The ev…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Feature CorrelationObject LocalizationUncertainty Driven Bottleneck Attention U-net for Organ at Risk Segmentation
Organ at risk (OAR) segmentation in computed tomography (CT) imagery is a difficult task for automated segmentation methods and can be crucial for downstream radiation treatment planning. U-net has become a de-facto stan…
Computed Tomography (CT)DecoderFeature CorrelationImage Segmentation+3Sequential Spatial Network for Collision Avoidance in Autonomous Driving
Several autonomous driving strategies have been applied to autonomous vehicles, especially in the collision avoidance area. The purpose of collision avoidance is achieved by adjusting the trajectory of autonomous vehicle…
Autonomous DrivingAutonomous VehiclesCollision AvoidanceFeature CorrelationFeature Importance: A Closer Look at Shapley Values and LOCO
There is much interest lately in explainability in statistics and machine learning. One aspect of explainability is to quantify the importance of various features (or covariates). Two popular methods for defining variabl…
Feature CorrelationFeature ImportanceOnline Sparse Streaming Feature Selection Using Adapted Classification
Traditional feature selections need to know the feature space before learning, and online streaming feature selection (OSFS) is proposed to process streaming features on the fly. Existing methods divide features into rel…
ClassificationFeature Correlationfeature selectionHow Does Traffic Environment Quantitatively Affect the Autonomous Driving Prediction?
An accurate trajectory prediction is crucial for safe and efficient autonomous driving in complex traffic environments. In recent years, artificial intelligence has shown strong capabilities in improving prediction accur…
Autonomous DrivingDecision MakingFeature CorrelationPrediction+1R2Former: Unified Retrieval and Reranking Transformer for Place Recognition
Visual Place Recognition (VPR) estimates the location of query images by matching them with images in a reference database. Conventional methods generally adopt aggregated CNN features for global retrieval and RANSAC…
Feature CorrelationRerankingRetrievalVisual Place RecognitionGlobal Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision
Multi-label image classification, which can be categorized into label-dependency and region-based methods, is a challenging problem due to the complex underlying object layouts. Although region-based methods are less lik…
Feature Correlationimage-classificationImage ClassificationMulti-Label Image ClassificationFeature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning
To eliminate the requirement of fully-labeled data for supervised model training in traditional Federated Learning (FL), extensive attention has been paid to the application of Self-supervised Learning (SSL) approaches o…
Feature CorrelationFederated LearningKnowledge DistillationPrivacy Preserving+2Hybrid CNN -Interpreter: Interpret local and global contexts for CNN-based Models
Convolutional neural network (CNN) models have seen advanced improvements in performance in various domains, but lack of interpretability is a major barrier to assurance and regulation during operation for acceptance and…
Feature Correlation