Papers Classification Consistency
“Classification Consistency” 태그가 달린 논문 13편 · 필터 해제
VisionGuard: Synergistic Framework for Helmet Violation Detection
Enforcing helmet regulations among motorcyclists is essential for enhancing road safety and ensuring the effectiveness of traffic management systems. However, automatic detection of helmet violations faces significant ch…
Classification ConsistencyData-Driven Hierarchical Open Set Recognition
This paper presents a novel data-driven hierarchical approach to open set recognition (OSR) for robust perception in robotics and computer vision, utilizing constrained agglomerative clustering to automatically build a h…
Classification Consistencyopen-set classificationOpen Set LearningDual-stage Hyperspectral Image Classification Model with Spectral Supertoken
Hyperspectral image classification, a task that assigns pre-defined classes to each pixel in a hyperspectral image of remote sensing scenes, often faces challenges due to the neglect of correlations between spectrally si…
ClassificationClassification ConsistencyDiversityHyperspectral Image Classification+3AllMatch: Exploiting All Unlabeled Data for Semi-Supervised Learning
Existing semi-supervised learning algorithms adopt pseudo-labeling and consistency regulation techniques to introduce supervision signals for unlabeled samples. To overcome the inherent limitation of threshold-based pseu…
AllBinary ClassificationClassification ConsistencyPseudo LabelImplicit Generative Prior for Bayesian Neural Networks
Predictive uncertainty quantification is crucial for reliable decision-making in various applied domains. Bayesian neural networks offer a powerful framework for this task. However, defining meaningful priors and ensurin…
Classification ConsistencyComputational EfficiencyDecision Makingimage-classification+3CCFace: Classification Consistency for Low-Resolution Face Recognition
In recent years, deep face recognition methods have demonstrated impressive results on in-the-wild datasets. However, these methods have shown a significant decline in performance when applied to real-world low-resolutio…
ClassificationClassification ConsistencyData AugmentationFace Recognition+2End-to-End Semi-Supervised Learning for Video Action Detection
In this work, we focus on semi-supervised learning for video action detection which utilizes both labeled as well as unlabeled data. We propose a simple end-to-end consistency based approach which effectively utilizes th…
Action DetectionClassification ConsistencySemantic SegmentationSemi-Supervised Video Action Detection+4Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity
We study a family of loss functions named label-distributionally robust (LDR) losses for multi-class classification that are formulated from distributionally robust optimization (DRO) perspective, where the uncertainty i…
Classification ConsistencyMulti-class ClassificationG5: A Universal GRAPH-BERT for Graph-to-Graph Transfer and Apocalypse Learning
The recent GRAPH-BERT model introduces a new approach to learning graph representations merely based on the attention mechanism. GRAPH-BERT provides an opportunity for transferring pre-trained models and learned graph re…
Classification ConsistencyGraph Representation LearningRepresentation LearningSparse Representation Classification via Screening for Graphs
The sparse representation classifier (SRC) is shown to work well for image recognition problems that satisfy a subspace assumption. In this paper we propose a new implementation of SRC via screening, establish its equiva…
ClassificationClassification ConsistencyGeneral ClassificationMaking Convolutional Networks Shift-Invariant Again
Modern convolutional networks are not shift-invariant, as small input shifts or translations can cause drastic changes in the output. Commonly used downsampling methods, such as max-pooling, strided-convolution, and aver…
Classification ConsistencyConditional Image GenerationDomain GeneralizationImage Classification+1Sparse Representation Classification Beyond L1 Minimization and the Subspace Assumption
The sparse representation classifier (SRC) has been utilized in various classification problems, which makes use of L1 minimization and works well for image recognition satisfying a subspace assumption. In this paper we …
ClassificationClassification ConsistencyGeneral ClassificationSimultaneous sparse estimation of canonical vectors in the p>>N setting
This article considers the problem of sparse estimation of canonical vectors in linear discriminant analysis when $p\gg N$. Several methods have been proposed in the literature that estimate one canonical vector in the t…
Classification Consistencyfeature selectionVariable Selection