Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Predicting Event Time by Classifying Sub-Level Temporal Relations Induced from a Unified Representation of Time Anchors
Extracting event time from news articles is a challenging but attractive task. In contrast to the most existing pair-wised temporal link annotation, Reimers et al.(2016) proposed to annotate the time anchor (a.k.a. the e…
ArticlesMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONDeep Multi Query Image Retrieval
There exist few studies investigating the multi-query image retrieval problem. Existing methods are not based on hash codes. As a result, they are not efficient and fast. In this study, we develop an efficient and fast m…
Deep HashingImage RetrievalMulti-Label ClassificationReranking+1Feature Ranking for Semi-supervised Learning
The data made available for analysis are becoming more and more complex along several directions: high dimensionality, number of examples and the amount of labels per example. This poses a variety of challenges for the e…
ClassificationGeneral ClassificationHierarchical Multi-label ClassificationMulti-Label Classification+3DINE: A Framework for Deep Incomplete Network Embedding
Network representation learning (NRL) plays a vital role in a variety of tasks such as node classification and link prediction. It aims to learn low-dimensional vector representations for nodes based on network structure…
General ClassificationLink PredictionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+3Latent Topic-aware Multi-Label Classification
In real-world applications, data are often associated with different labels. Although most extant multi-label learning algorithms consider the label correlations, they rarely consider the topic information hidden in the …
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Fixing Localization Errors to Improve Image Classification
Deep neural networks are generally considered black-box models that offer less interpretability for their decision process. To address this limitation, Class Activation Map (CAM) provides an attractive solution that visu…
ClassificationGeneral Classificationimage-classificationImage Classification+3Multi-label Zero-shot Classification by Learning to Transfer from External Knowledge
Multi-label zero-shot classification aims to predict multiple unseen class labels for an input image. It is more challenging than its single-label counterpart. On one hand, the unconstrained number of labels assigned to …
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+3Oblique Predictive Clustering Trees
Predictive clustering trees (PCTs) are a well established generalization of standard decision trees, which can be used to solve a variety of predictive modeling tasks, including structured output prediction. Combining th…
ClusteringComputational EfficiencyFeature ImportanceHierarchical Multi-label Classification+2Zero-Shot Recognition through Image-Guided Semantic Classification
We present a new embedding-based framework for zero-shot learning (ZSL). Most embedding-based methods aim to learn the correspondence between an image classifier (visual representation) and its class prototype (semantic …
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Evolving Multi-label Classification Rules by Exploiting High-order Label Correlation
In multi-label classification tasks, each problem instance is associated with multiple classes simultaneously. In such settings, the correlation between labels contains valuable information that can be used to obtain mor…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONVocal Bursts Intensity PredictionMulti-label Contrastive Predictive Coding
Variational mutual information (MI) estimators are widely used in unsupervised representation learning methods such as contrastive predictive coding (CPC). A lower bound on MI can be obtained from a multi-class classific…
Knowledge DistillationMulti-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+3Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets
We present a new loss function called Distribution-Balanced Loss for the multi-label recognition problems that exhibit long-tailed class distributions. Compared to conventional single-label classification problem, multi-…
Binary ClassificationGeneral ClassificationLong-tail LearningMulti-Label Classification+1Conformal Rule-Based Multi-label Classification
We advocate the use of conformal prediction (CP) to enhance rule-based multi-label classification (MLC). In particular, we highlight the mutual benefit of CP and rule learning: Rules have the ability to provide natural (…
ClassificationConformal PredictionDecision MakingGeneral Classification+2Multimodal Word Sense Disambiguation in Creative Practice
Language is ambiguous; many terms and expressions can convey the same idea. This is especially true in creative practice, where ideas and design intents are highly subjective. We present a dataset, Ambiguous Descriptions…
ClassificationDescriptiveGeneral ClassificationImage Description+5Emoji Prediction: Extensions and Benchmarking
Emojis are a succinct form of language which can express concrete meanings, emotions, and intentions. Emojis also carry signals that can be used to better understand communicative intent. They have become a ubiquitous pa…
BenchmarkingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPredictionLearning Differential Diagnosis of Skin Conditions with Co-occurrence Supervision using Graph Convolutional Networks
Skin conditions are reported the 4th leading cause of nonfatal disease burden worldwide. However, given the colossal spectrum of skin disorders defined clinically and shortage in dermatology expertise, diagnosing skin co…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONDisentangled Variational Autoencoder based Multi-Label Classification with Covariance-Aware Multivariate Probit Model
Multi-label classification is the challenging task of predicting the presence and absence of multiple targets, involving representation learning and label correlation modeling. We propose a novel framework for multi-labe…
DecoderGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition
Multi-label image recognition is a practical and challenging task compared to single-label image classification. However, previous works may be suboptimal because of a great number of object proposals or complex attentio…
Diversityimage-classificationMulti-Label ClassificationMulti-Label Image Classification+1A Novel BGCapsule Network for Text Classification
Several text classification tasks such as sentiment analysis, news categorization, multi-label classification and opinion classification are challenging problems even for modern deep learning networks. Recently, Capsule …
ClassificationGeneral Classificationimage-classificationImage Classification+5Hyperbolic Capsule Networks for Multi-Label Classification
Although deep neural networks are effective at extracting high-level features, classification methods usually encode an input into a vector representation via simple feature aggregation operations (e.g. pooling). Such op…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION