Papers Open Set Learning
“Open Set Learning” 태그가 달린 논문 267편 · 필터 해제
Unlocking Transfer Learning for Open-World Few-Shot Recognition
Few-Shot Open-Set Recognition (FSOSR) targets a critical real-world challenge, aiming to categorize inputs into known categories, termed closed-set classes, while identifying open-set inputs that fall outside these class…
Meta-LearningOpen Set LearningTransfer LearningFeature-Space Semantic Invariance: Enhanced OOD Detection for Open-Set Domain Generalization
Open-set domain generalization addresses a real-world challenge: training a model to generalize across unseen domains (domain generalization) while also detecting samples from unknown classes not encountered during train…
Domain GeneralizationOpen Set LearningElectromagnetic Scattering Kernel Guided Reciprocal Point Learning for SAR Open-Set Recognition
The limitations of existing Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) methods lie in their confinement by the closed-environment assumption, hindering their effective and robust handling of unknow…
AttributeOpen Set LearningDeep Learning for Leopard Individual Identification: An Adaptive Angular Margin Approach
Accurate identification of individual leopards across camera trap images is critical for population monitoring and ecological studies. This paper introduces a deep learning framework to distinguish between individual leo…
Deep LearningEdge DetectionOpen Set LearningTripletData-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 LearningLEGO-Learn: Label-Efficient Graph Open-Set Learning
How can we train graph-based models to recognize unseen classes while keeping labeling costs low? Graph open-set learning (GOL) and out-of-distribution (OOD) detection aim to address this challenge by training models tha…
Node ClassificationOpen Set LearningOut of Distribution (OOD) DetectionOpen World Object Detection: A Survey
Exploring new knowledge is a fundamental human ability that can be mirrored in the development of deep neural networks, especially in the field of object detection. Open world object detection (OWOD) is an emerging area …
Incremental LearningObjectobject-detectionObject Detection+3FSOS-AMC: Few-Shot Open-Set Learning for Automatic Modulation Classification
Automatic modulation classification (AMC) is essential for the advancement and efficiency of future wireless communication networks. Deep learning (DL)-based AMC frameworks have garnered extensive attention for their imp…
ClassificationOpen Set LearningSolution for OOD-CV Workshop SSB Challenge 2024 (Open-Set Recognition Track)
This report provides a detailed description of the method we explored and proposed in the OSR Challenge at the OOD-CV Workshop during ECCV 2024. The challenge required identifying whether a test sample belonged to the se…
Open Set LearningEffects of Common Regularization Techniques on Open-Set Recognition
In recent years there has been increasing interest in the field of Open-Set Recognition, which allows a classification model to identify inputs as "unknown" when it encounters an object or class not in the training set. …
Open Set LearningOpen-set recognition with long-tail sonar images
Current sonar image recognition methods excel in closed-set and balanced scenarios, but real underwater data often follow an open-set and long-tailed distribution, leading to misclassifications, especially among tail cla…
Open Set LearningOut-of-Distribution DetectionDissecting Out-of-Distribution Detection and Open-Set Recognition: A Critical Analysis of Methods and Benchmarks
Detecting test-time distribution shift has emerged as a key capability for safely deployed machine learning models, with the question being tackled under various guises in recent years. In this paper, we aim to provide a…
Open Set LearningOut-of-Distribution DetectionOut of Distribution (OOD) DetectionGallery-Aware Uncertainty Estimation For Open-Set Face Recognition
Accurately estimating image quality and model robustness improvement are critical challenges in unconstrained face recognition, which can be addressed through uncertainty estimation via probabilistic face embeddings. Pre…
Face RecognitionFace VerificationOpen Set LearningLearning Unknowns from Unknowns: Diversified Negative Prototypes Generator for Few-Shot Open-Set Recognition
Few-shot open-set recognition (FSOR) is a challenging task that requires a model to recognize known classes and identify unknown classes with limited labeled data. Existing approaches, particularly Negative-Prototype-Bas…
Meta-LearningOpen Set LearningAdvanced Vision Transformers and Open-Set Learning for Robust Mosquito Classification: A Novel Approach to Entomological Studies
Mosquito-related diseases pose a significant threat to global public health, necessitating efficient and accurate mosquito classification for effective surveillance and control. This work presents an innovative approach …
Data AugmentationDeep LearningOpen Set LearningEOL: Transductive Few-Shot Open-Set Recognition by Enhancing Outlier Logits
In Few-Shot Learning (FSL), models are trained to recognise unseen objects from a query set, given a few labelled examples from a support set. In standard FSL, models are evaluated on query instances sampled from the sam…
Few-Shot LearningOpen Set LearningOutlier DetectionPseudo LabelComparison of Embedded Spaces for Deep Learning Classification
Embedded spaces are a key feature in deep learning. Good embedded spaces represent the data well to support classification and advanced techniques such as open-set recognition, few-short learning and explainability. This…
ClassificationDeep LearningOpen Set LearningOpen Set Recognition for Random Forest
In many real-world classification or recognition tasks, it is often difficult to collect training examples that exhaust all possible classes due to, for example, incomplete knowledge during training or ever changing regi…
ClassificationMetric LearningOpen Set LearningTowards Open-Set Myoelectric Gesture Recognition via Dual-Perspective Inconsistency Learning
Gesture recognition based on surface electromyography (sEMG) has achieved significant progress in human-machine interaction (HMI), especially in prosthetic control and movement rehabilitation. However, accurately recogni…
DiversityEnsemble LearningGesture RecognitionOpen Set LearningZero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV
The Internet of Vehicles (IoV) is a crucial technology for Intelligent Transportation Systems (ITS) that integrates vehicles with the Internet and other entities. The emergence of 5G and the forthcoming 6G networks prese…
Federated LearningIntrusion DetectionOpen Set LearningPrivacy Preserving