Papers Open Set Learning
“Open Set Learning” 태그가 달린 논문 267편 · 필터 해제
Navigating Open Set Scenarios for Skeleton-based Action Recognition
In real-world scenarios, human actions often fall outside the distribution of training data, making it crucial for models to recognize known actions and reject unknown ones. However, using pure skeleton data in such open…
Action RecognitionActivity Recognitioncross-modal alignmentNovelty Detection+4Towards Open-set Gesture Recognition via Feature Activation Enhancement and Orthogonal Prototype Learning
Gesture recognition is a foundational task in human-machine interaction (HMI). While there has been significant progress in gesture recognition based on surface electromyography (sEMG), accurate recognition of predefined…
Gesture RecognitionOpen Set LearningVideo Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach
Video Anomaly Detection (VAD) is an open-set recognition task, which is usually formulated as a one-class classification (OCC) problem, where training data is comprised of videos with normal instances while test data con…
Anomaly DetectionOne-Class ClassificationOpen Set LearningOptical Flow Estimation+1Unified Classification and Rejection: A One-versus-All Framework
Classifying patterns of known classes and rejecting ambiguous and novel (also called as out-of-distribution (OOD)) inputs are involved in open world pattern recognition. Deep neural network models usually excel in closed…
AllBinary ClassificationClassificationOpen Set LearningEdgeFM: Leveraging Foundation Model for Open-set Learning on the Edge
Deep Learning (DL) models have been widely deployed on IoT devices with the help of advancements in DL algorithms and chips. However, the limited resources of edge devices make these on-device DL models hard to be genera…
Open Set LearningFamiliarity-Based Open-Set Recognition Under Adversarial Attacks
Open-set recognition (OSR), the identification of novel categories, can be a critical component when deploying classification models in real-world applications. Recent work has shown that familiarity-based scoring rules …
Open Set Learningscoring ruleAnalysis and Applications of Deep Learning with Finite Samples in Full Life-Cycle Intelligence of Nuclear Power Generation
The advent of Industry 4.0 has precipitated the incorporation of Artificial Intelligence (AI) methods within industrial contexts, aiming to realize intelligent manufacturing, operation as well as maintenance, also known …
Few-Shot LearningOpen Set LearningZero-Shot LearningOpen-Set Object Recognition Using Mechanical Properties During Interaction
while most of the tactile robots are operated in close-set conditions, it is challenging for them to operate in open-set conditions where test objects are beyond the robots' knowledge. We proposed an open-set recognition…
ClusteringObjectObject RecognitionOpen Set LearningOpen-Set Face Recognition with Maximal Entropy and Objectosphere Loss
Open-set face recognition characterizes a scenario where unknown individuals, unseen during the training and enrollment stages, appear on operation time. This work concentrates on watchlists, an open-set task that is exp…
Domain AdaptationFace RecognitionImage Classificationopen-set classification+1MENTOR: Human Perception-Guided Pretraining for Increased Generalization
Incorporating human perception into training of convolutional neural networks (CNN) has boosted generalization capabilities of such models in open-set recognition tasks. One of the active research questions is where (in …
Anomaly DetectionDecoderOpen Set LearningTransfer LearningOpen-Set Image Tagging with Multi-Grained Text Supervision
In this paper, we introduce the Recognize Anything Plus Model (RAM++), an open-set image tagging model effectively leveraging multi-grained text supervision. Previous approaches (e.g., CLIP) primarily utilize global text…
Human-Object Interaction DetectionOpen Set LearningTAGPairwise Similarity Learning is SimPLE
In this paper, we focus on a general yet important learning problem, pairwise similarity learning (PSL). PSL subsumes a wide range of important applications, such as open-set face recognition, speaker verification, image…
Face RecognitionImage RetrievalOpen Set LearningPerson Re-Identification+2OpenIncrement: A Unified Framework for Open Set Recognition and Deep Class-Incremental Learning
In most works on deep incremental learning research, it is assumed that novel samples are pre-identified for neural network retraining. However, practical deep classifiers often misidentify these samples, leading to erro…
class-incremental learningClass Incremental LearningIncremental LearningOpen Set LearningRecursive Counterfactual Deconfounding for Object Recognition
Image recognition is a classic and common task in the computer vision field, which has been widely applied in the past decade. Most existing methods in literature aim to learn discriminative features from labeled images …
counterfactualObjectObject RecognitionOpen Set LearningDomain Adaptive Few-Shot Open-Set Learning
Few-shot learning has made impressive strides in addressing the crucial challenges of recognizing unknown samples from novel classes in target query sets and managing visual shifts between domains. However, existing tech…
cross-domain few-shot learningFew-Shot LearningMeta-LearningOpen Set LearningLatent Space Energy-based Model for Fine-grained Open Set Recognition
Fine-grained open-set recognition (FineOSR) aims to recognize images belonging to classes with subtle appearance differences while rejecting images of unknown classes. A recent trend in OSR shows the benefit of generativ…
AttributeDensity EstimationOpen Set LearningDetecting Unknown Attacks in IoT Environments: An Open Set Classifier for Enhanced Network Intrusion Detection
The widespread integration of Internet of Things (IoT) devices across all facets of life has ushered in an era of interconnectedness, creating new avenues for cybersecurity challenges and underscoring the need for robust…
Intrusion DetectionNetwork Intrusion DetectionOpen Set LearningLORD: Leveraging Open-Set Recognition with Unknown Data
Handling entirely unknown data is a challenge for any deployed classifier. Classification models are typically trained on a static pre-defined dataset and are kept in the dark for the open unassigned feature space. As a …
Open Set LearningOpen-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation
Open-set face recognition refers to a scenario in which biometric systems have incomplete knowledge of all existing subjects. Therefore, they are expected to prevent face samples of unregistered subjects from being ident…
Face RecognitionImage Classificationopen-set classificationOpen Set LearningAn Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR
Existing synthetic aperture radar automatic target recognition (SAR ATR) methods have been effective for the classification of seen target classes. However, it is more meaningful and challenging to distinguish the unseen…
Meta-LearningOpen Set Learning