Papers Feature Correlation
“Feature Correlation” 태그가 달린 논문 124편 · 필터 해제
Vector Contrastive Learning For Pixel-Wise Pretraining In Medical Vision
Contrastive learning (CL) has become a cornerstone of self-supervised pretraining (SSP) in foundation models, however, extending CL to pixel-wise representation, crucial for medical vision, remains an open problem. Stand…
Contrastive LearningFeature CorrelationregressionSelf-LearningStepwise Decomposition and Dual-stream Focus: A Novel Approach for Training-free Camouflaged Object Segmentation
While promptable segmentation (\textit{e.g.}, SAM) has shown promise for various segmentation tasks, it still requires manual visual prompts for each object to be segmented. In contrast, task-generic promptable segmentat…
Camouflaged Object SegmentationFeature CorrelationImage CaptioningSegmentation+3MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains
Despite the widespread use of tabular data in real-world applications, most benchmarks rely on average-case metrics, which fail to reveal how model behavior varies across diverse data regimes. To address this, we propose…
Feature CorrelationFFCBA: Feature-based Full-target Clean-label Backdoor Attacks
Backdoor attacks pose a significant threat to deep neural networks, as backdoored models would misclassify poisoned samples with specific triggers into target classes while maintaining normal performance on clean samples…
Feature CorrelationSpecificityA Semantic-Enhanced Heterogeneous Graph Learning Method for Flexible Objects Recognition
Flexible objects recognition remains a significant challenge due to its inherently diverse shapes and sizes, translucent attributes, and subtle inter-class differences. Graph-based models, such as graph convolution netwo…
Feature CorrelationGraph GenerationGraph LearningUnderstanding Dataset Distillation via Spectral Filtering
Dataset distillation (DD) has emerged as a promising approach to compress datasets and speed up model training. However, the underlying connections among various DD methods remain largely unexplored. In this paper, we in…
Dataset DistillationFeature CorrelationTraining-Free Motion-Guided Video Generation with Enhanced Temporal Consistency Using Motion Consistency Loss
In this paper, we address the challenge of generating temporally consistent videos with motion guidance. While many existing methods depend on additional control modules or inference-time fine-tuning, recent studies sugg…
Feature CorrelationVideo GenerationMulti-Task Semantic Communication With Graph Attention-Based Feature Correlation Extraction
Multi-task semantic communication can serve multiple learning tasks using a shared encoder model. Existing models have overlooked the intricate relationships between features extracted during an encoding process of tasks…
Feature CorrelationGraph AttentionSemantic CommunicationMultimodal joint prediction of traffic spatial-temporal data with graph sparse attention mechanism and bidirectional temporal convolutional network
Traffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is relat…
Feature CorrelationPredictionExplaining the Unexplained: Revealing Hidden Correlations for Better Interpretability
Deep learning has achieved remarkable success in processing and managing unstructured data. However, its "black box" nature imposes significant limitations, particularly in sensitive application domains. While existing i…
Feature CorrelationFeature Importanceimage-classificationImage Classification+4XAgents: A Framework for Interpretable Rule-Based Multi-Agents Cooperation
Extracting implicit knowledge and logical reasoning abilities from large language models (LLMs) has consistently been a significant challenge. The advancement of multi-agent systems has further en-hanced the capabilities…
Feature CorrelationLogical ReasoningM$^3$-Impute: Mask-guided Representation Learning for Missing Value Imputation
Missing values are a common problem that poses significant challenges to data analysis and machine learning. This problem necessitates the development of an effective imputation method to fill in the missing values accur…
Feature CorrelationGraph Neural NetworkImputationMissing Values+1Principal Orthogonal Latent Components Analysis (POLCA Net)
Representation learning is a pivotal area in the field of machine learning, focusing on the development of methods to automatically discover the representations or features needed for a given task from raw data. Unlike t…
Dimensionality ReductionFeature CorrelationFeature EngineeringMultiobjective Optimization+1Accelerating Flood Warnings by 10 Hours: The Power of River Network Topology in AI-enhanced Flood Forecasting
Climate change-driven floods demand advanced forecasting models, yet Graph Neural Networks (GNNs) underutilize river network topology due to tree-like structures causing over-squashing from high node resistance distances…
Cloth-Changing Person Re-IdentificationDensity Ratio EstimationFeature CorrelationPerson Re-IdentificationFairness-Aware Streaming Feature Selection with Causal Graphs
Its crux lies in the optimization of a tradeoff between accuracy and fairness of resultant models on the selected feature subset. The technical challenge of our setting is twofold: 1) streaming feature inputs, such that …
FairnessFeature Correlationfeature selectionDistillGrasp: Integrating Features Correlation with Knowledge Distillation for Depth Completion of Transparent Objects
Due to the visual properties of reflection and refraction, RGB-D cameras cannot accurately capture the depth of transparent objects, leading to incomplete depth maps. To fill in the missing points, recent studies tend to…
Depth CompletionFeature CorrelationKnowledge DistillationRobotic Grasping+1Topological Persistence Guided Knowledge Distillation for Wearable Sensor Data
Deep learning methods have achieved a lot of success in various applications involving converting wearable sensor data to actionable health insights. A common application areas is activity recognition, where deep-learnin…
Activity RecognitionDeep LearningFeature CorrelationKnowledge Distillation+2Encourage or Inhibit Monosemanticity? Revisit Monosemanticity from a Feature Decorrelation Perspective
To better interpret the intrinsic mechanism of large language models (LLMs), recent studies focus on monosemanticity on its basic units. A monosemantic neuron is dedicated to a single and specific concept, which forms a …
DiversityFeature CorrelationHyperbolic Benchmarking Unveils Network Topology-Feature Relationship in GNN Performance
Graph Neural Networks (GNNs) have excelled in predicting graph properties in various applications ranging from identifying trends in social networks to drug discovery and malware detection. With the abundance of new arch…
BenchmarkingDrug DiscoveryFeature CorrelationMalware Detection+1Tabular Data Contrastive Learning via Class-Conditioned and Feature-Correlation Based Augmentation
Contrastive learning is a model pre-training technique by first creating similar views of the original data, and then encouraging the data and its corresponding views to be close in the embedding space. Contrastive learn…
Contrastive LearningFeature Correlation