Papers Multi-Label Learning
“Multi-Label Learning” 태그가 달린 논문 334편 · 필터 해제
Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classification
described by multiple instances (e.g., image patches) and simultaneously associated with multiple labels. Existing MIML methods are useful in many applications but most of which suffer from relatively low accuracy and tr…
image-classificationImage ClassificationMedical Image ClassificationMulti-Label LearningLabel Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing Labels
Contrastive learning (CL) has shown impressive advances in image representation learning in whichever supervised multi-class classification or unsupervised learning. However, these CL methods fail to be directly adapted …
Contrastive Learningimage-classificationImage ClassificationMissing Labels+4From Multi-label Learning to Cross-Domain Transfer: A Model-Agnostic Approach
In multi-label learning, a particular case of multi-task learning where a single data point is associated with multiple target labels, it was widely assumed in the literature that, to obtain best accuracy, the dependence…
Multi-Label LearningMulti-Task LearningTransfer LearningIDEA: Increasing Text Diversity via Online Multi-Label Recognition for Vision-Language Pre-training
Vision-Language Pre-training (VLP) with large-scale image-text pairs has demonstrated superior performance in various fields. However, the image-text pairs co-occurrent on the Internet typically lack explicit alignment i…
DiversityMulti-Label LearningObjectTAGTowards Effective Multi-Label Recognition Attacks via Knowledge Graph Consistency
Many real-world applications of image recognition require multi-label learning, whose goal is to find all labels in an image. Thus, robustness of such systems to adversarial image perturbations is extremely important. Ho…
Multi-Label LearningMulti-Label Learning to Rank through Multi-Objective Optimization
Learning to Rank (LTR) technique is ubiquitous in the Information Retrieval system nowadays, especially in the Search Ranking application. The query-item relevance labels typically used to train the ranking model are oft…
Information RetrievalLearning-To-RankMulti-Label LearningRetrievalUniversal Domain Adaptive Object Detector
Universal domain adaptive object detection (UniDAOD)is more challenging than domain adaptive object detection (DAOD) since the label space of the source domain may not be the same as that of the target and the scale of o…
Multi-Label LearningObjectobject-detectionObject DetectionDiscriminatory Label-specific Weights for Multi-label Learning with Missing Labels
Class labels in multi-label datasets are only associated with a very small fraction of the data instances leading to a class imbalance problem. There exist multi-label learning algorithms that handle the datasets’ class …
Missing LabelsMulti-Label ClassificationMulti-Label LearningOne Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement
Multi-label learning (MLL) learns from the examples each associated with multiple labels simultaneously, where the high cost of annotating all relevant labels for each training example is challenging for real-world appli…
Multi-Label LearningObject-Aware Self-supervised Multi-Label Learning
Multi-label Learning on Image data has been widely exploited with deep learning models. However, supervised training on deep CNN models often cannot discover sufficient discriminative features for classification. As a re…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningObjectRandom Manifold Sampling and Joint Sparse Regularization for Multi-label Feature Selection
Multi-label learning is usually used to mine the correlation between features and labels, and feature selection can retain as much information as possible through a small number of features. $\ell_{2,1}$ regularization m…
feature selectionGraph EmbeddingMulti-Label LearningAcknowledging the Unknown for Multi-label Learning with Single Positive Labels
Due to the difficulty of collecting exhaustive multi-label annotations, multi-label datasets often contain partial labels. We consider an extreme of this weakly supervised learning problem, called single positive multi-l…
Multi-Label LearningWeakly-supervised LearningThe Overlooked Classifier in Human-Object Interaction Recognition
Human-Object Interaction (HOI) recognition is challenging due to two factors: (1) significant imbalance across classes and (2) requiring multiple labels per image. This paper shows that these two challenges can be effect…
ClassificationHuman-Object Interaction DetectionMulti-Label LearningObject+2A Similarity-based Framework for Classification Task
Similarity-based method gives rise to a new class of methods for multi-label learning and also achieves promising performance. In this paper, we generalize this method, resulting in a new framework for classification tas…
ClassificationMulti-Label LearningLow rank label subspace transformation for multi-label learning with missing labels
Multi-label datasets often contain label information with missing values and recovering them is a non-trivial challenge. Several methods augment the observed label matrix by constructing auxiliary labels and learning hig…
Missing LabelsMissing ValuesMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Incomplete Multi-View Weak-Label Learning with Noisy Features and Imbalanced Labels
A variety of modern applications exhibit multi-view multi-label learning, where each sample has multi-view features, and multiple labels are correlated via common views. Current methods usually fail to directly deal with…
Multi-Label LearningThe Overlooked Classifier in Human-Object Interaction Recognition
Human-Object Interaction (HOI) recognition is challenging due to two factors: (1) significant imbalance across classes and (2) requiring multiple labels per image. This paper shows that these two challenges can be effect…
ClassificationHuman-Object Interaction DetectionMulti-Label LearningObject+2Simple and Robust Loss Design for Multi-Label Learning with Missing Labels
Multi-label learning in the presence of missing labels (MLML) is a challenging problem. Existing methods mainly focus on the design of network structures or training schemes, which increase the complexity of implementati…
image-classificationImage ClassificationMissing LabelsMulti-Label Image Classification+1Improving Predictions of Tail-end Labels using Concatenated BioMed-Transformers for Long Medical Documents
Multi-label learning predicts a subset of labels from a given label set for an unseen instance while considering label correlations. A known challenge with multi-label classification is the long-tailed distribution of la…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningUnderstanding Partial Multi-Label Learning via Mutual Information
To deal with ambiguities in partial multilabel learning (PML), state-of-the-art methods perform disambiguation by identifying ground-truth labels directly. However, there is an essential question:“Can the ground-truth la…
Multi-Label Learning