Papers Open Set Learning
“Open Set Learning” 태그가 달린 논문 267편 · 필터 해제
Simple Domain Generalization Methods are Strong Baselines for Open Domain Generalization
In real-world applications, a machine learning model is required to handle an open-set recognition (OSR), where unknown classes appear during the inference, in addition to a domain shift, where the distribution of data d…
Data AugmentationDomain GeneralizationEnsemble LearningMeta-Learning+1On the link between generative semi-supervised learning and generative open-set recognition
This study investigates the relationship between semi-supervised learning (SSL, which is training off partially labelled datasets) and open-set recognition (OSR, which is classification with simultaneous novelty detectio…
Novelty DetectionOpen Set LearningSemi-supervised learning-open set recognitionProgressive Open Space Expansion for Open-Set Model Attribution
Despite the remarkable progress in generative technology, the Janus-faced issues of intellectual property protection and malicious content supervision have arisen. Efforts have been paid to manage synthetic images by att…
AttributeOpen Set LearningM-Tuning: Prompt Tuning with Mitigated Label Bias in Open-Set Scenarios
In realistic open-set scenarios where labels of a part of testing data are totally unknown, when vision-language (VL) prompt learning methods encounter inputs related to unknown classes (i.e., not seen during training), …
Open Set LearningPrompt LearningOpen-World Object Detection via Discriminative Class Prototype Learning
Open-world object detection (OWOD) is a challenging problem that combines object detection with incremental learning and open-set learning. Compared to standard object detection, the OWOD setting is task to: 1) detect ob…
Incremental LearningObjectobject-detectionObject Detection+2Zero-Knowledge Zero-Shot Learning for Novel Visual Category Discovery
Generalized Zero-Shot Learning (GZSL) and Open-Set Recognition (OSR) are two mainstream settings that greatly extend conventional visual object recognition. However, the limitations of their problem settings are not negl…
Generalized Zero-Shot LearningObject RecognitionOpen Set LearningZero-Shot LearningThe Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognition
Open-set Recognition (OSR) aims to identify test samples whose classes are not seen during the training process. Recently, Unified Open-set Recognition (UOSR) has been proposed to reject not only unknown samples but also…
Open Set LearningImproving Open-Set Semi-Supervised Learning with Self-Supervision
Open-set semi-supervised learning (OSSL) embodies a practical scenario within semi-supervised learning, wherein the unlabeled training set encompasses classes absent from the labeled set. Many existing OSSL methods assum…
Open Set LearningContracting Skeletal Kinematics for Human-Related Video Anomaly Detection
Detecting the anomaly of human behavior is paramount to timely recognizing endangering situations, such as street fights or elderly falls. However, anomaly detection is complex since anomalous events are rare and because…
Anomaly DetectionOpen Set LearningVideo Anomaly DetectionOpen-Set Likelihood Maximization for Few-Shot Learning
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have a few labeled samples, while simultaneously detecting instances that do not belong to …
Few-Shot Image ClassificationFew-Shot LearningOpen Set LearningOutlier DetectionSubject-Independent Brain-Computer Interfaces with Open-Set Subject Recognition
A brain-computer interface (BCI) can't be effectively used since electroencephalography (EEG) varies between and within subjects. BCI systems require calibration steps to adjust the model to subject-specific data. It is …
Brain Computer InterfaceEEGElectroencephalogram (EEG)Open Set LearningVocabulary-informed Zero-shot and Open-set Learning
Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, the ability to learn from limited labeled data and to recognize object classes within large, potent…
Object CategorizationOpen Set LearningZero-Shot LearningCascade Evidential Learning for Open-World Weakly-Supervised Temporal Action Localization
Targeting at recognizing and localizing action instances with only video-level labels during training, Weakly-supervised Temporal Action Localization (WTAL) has achieved significant progress in recent years. However,…
Action LocalizationOpen Set LearningTemporal Action LocalizationWeakly-supervised Temporal Action LocalizationFedPD: Federated Open Set Recognition with Parameter Disentanglement
Existing federated learning (FL) approaches are deployed under the unrealistic closed-set setting, with both training and testing classes belong to the same set, which makes the global model fail to identify the unse…
DisentanglementFederated LearningOpen Set LearningDeep Simplex Classifier for Maximizing the Margin in Both Euclidean and Angular Spaces
The classification loss functions used in deep neural network classifiers can be grouped into two categories based on maximizing the margin in either Euclidean or angular spaces. Euclidean distances between sample vector…
ClassificationOpen Set LearningAccurate Open-set Recognition for Memory Workload
How can we accurately identify new memory workloads while classifying known memory workloads? Verifying DRAM (Dynamic Random Access Memory) using various workloads is an important task to guarantee the quality of DRAM. A…
Open Set LearningSpatial-Temporal Attention Network for Open-Set Fine-Grained Image Recognition
Triggered by the success of transformers in various visual tasks, the spatial self-attention mechanism has recently attracted more and more attention in the computer vision community. However, we empirically found that a…
Fine-Grained Image RecognitionOpen Set LearningInvisible Backdoor Attack with Dynamic Triggers against Person Re-identification
In recent years, person Re-identification (ReID) has rapidly progressed with wide real-world applications, but also poses significant risks of adversarial attacks. In this paper, we focus on the backdoor attack on deep R…
AllBackdoor AttackImage SteganographyOpen Set Learning+1PartCom: Part Composition Learning for 3D Open-Set Recognition
3D recognition is the foundation of 3D deep learning in many emerging fields, such as autonomous driving and robotics.Existing 3D methods mainly focus on the recognition of a fixed set of known classes and neglect possib…
Autonomous DrivingOpen Set LearningMetaMax: Improved Open-Set Deep Neural Networks via Weibull Calibration
Open-set recognition refers to the problem in which classes that were not seen during training appear at inference time. This requires the ability to identify instances of novel classes while maintaining discriminative c…
Open Set Learning