Papers Multi-Label Learning
“Multi-Label Learning” 태그가 달린 논문 334편 · 필터 해제
Multi-Label Learning with Pairwise Relevance Ordering
Precisely annotating objects with multiple labels is costly and has become a critical bottleneck in real-world multi-label classification tasks. Instead, deciding the relative order of label pairs is obviously less labor…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningMulti-label Iterated Learning for Image Classification with Label Ambiguity
Transfer learning from large-scale pre-trained models has become essential for many computer vision tasks. Recent studies have shown that datasets like ImageNet are weakly labeled since images with multiple object classe…
Classificationimage-classificationImage ClassificationMulti-Label Learning+1DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents
Scalability and accuracy are well recognized challenges in deep extreme multi-label learning where the objective is to train architectures for automatically annotating a data point with the most relevant subset of labels…
Multi-Label LearningMetaMIML: Meta Multi-Instance Multi-Label Learning
Multi-Instance Multi-Label learning (MIML) models complex objects (bags), each of which is associated with a set of interrelated labels and composed with a set of instances. Current MIML solutions still focus on a single…
Meta-LearningMulti-Label LearningNetwork EmbeddingA Multi-label Multi-hop Relation Detection Model based on Relation-aware Sequence Generation
Multi-hop relation detection in Knowledge Base Question Answering (KBQA) aims at retrieving the relation path starting from the topic entity to the answer node based on a given question, where the relation path may compr…
Knowledge Base Question AnsweringMulti-Label LearningQuestion AnsweringRelationA Simple Approach to Image Tilt Correction with Self-Attention MobileNet for Smartphones
The main contributions of our work are two-fold. First, we present a Self-Attention MobileNet, called SA-MobileNet Network that can model long-range dependencies between the image features instead of processing the local…
Multi-Label LearningActive Refinement for Multi-Label Learning: A Pseudo-Label Approach
The goal of multi-label learning (MLL) is to associate a given instance with its relevant labels from a set of concepts. Previous works of MLL mainly focused on the setting where the concept set is assumed to be fixed, w…
Active LearningMulti-Label LearningPseudo LabelOvercoming Label Ambiguity with Multi-label Iterated Learning
Transfer learning from ImageNet pre-trained models has become essential for many computer vision tasks. Recent studies have shown that ImageNet includes label ambiguity, where images with multiple object classes present …
Multi-Label LearningTransfer LearningBoost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction Prediction
Despite experimental and curation efforts, the extent of enzyme promiscuity on substrates continues to be largely unexplored and under documented. Recommender systems (RS), which are currently unexplored for the enzyme-s…
AttributeAuxiliary LearningCollaborative FilteringContrastive Learning+2Fast Multi-label Learning
Embedding approaches have become one of the most pervasive techniques for multi-label classification. However, the training process of embedding methods usually involves a complex quadratic or semidefinite programming pr…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningSCIDA: Self-Correction Integrated Domain Adaptation from Single- to Multi-label Aerial Images
Most publicly available datasets for image classification are with single labels, while images are inherently multi-labeled in our daily life. Such an annotation gap makes many pre-trained single-label classification mod…
Domain Adaptationimage-classificationImage ClassificationMulti-Label Image Classification+2Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound
Standard plane recognition plays an important role in prenatal ultrasound (US) screening. Automatically recognizing the standard plane along with the corresponding anatomical structures in US image can not only facilitat…
Contrastive LearningDiagnosticimage-classificationImage Classification+3Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators
Multi-label learning is an emerging extension of the multi-class classification where an image contains multiple labels. Not only acquiring a clean and fully labeled dataset in multi-label learning is extremely expensive…
Multi-class ClassificationMulti-Label LearningDECAF: Deep Extreme Classification with Label Features
Extreme multi-label classification (XML) involves tagging a data point with its most relevant subset of labels from an extremely large label set, with several applications such as product-to-product recommendation with m…
ClassificationExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+5T$_k$ML-AP: Adversarial Attacks to Top-$k$ Multi-Label Learning
Top-$k$ multi-label learning, which returns the top-$k$ predicted labels from an input, has many practical applications such as image annotation, document analysis, and web search engine. However, the vulnerabilities of …
Multi-Label LearningECLARE: Extreme Classification with Label Graph Correlations
Deep extreme classification (XC) seeks to train deep architectures that can tag a data point with its most relevant subset of labels from an extremely large label set. The core utility of XC comes from predicting labels …
ClassificationExtreme Multi-Label ClassificationGraph EmbeddingMulti-Label Classification+6XFL: Naming Functions in Binaries with Extreme Multi-label Learning
Reverse engineers benefit from the presence of identifiers such as function names in a binary, but usually these are removed for release. Training a machine learning model to predict function names automatically is promi…
Multi-Label LearningMLPD: Multi-Label Pedestrian Detector in Multispectral Domain
Multispectral pedestrian detection has been actively studied as a promising multi-modality solution to handle illumination and weather changes. Most multi-modality approaches carry the assumption that all inputs are full…
Multi-Label LearningMultispectral Object DetectionPedestrian DetectionAttack Transferability Characterization for Adversarially Robust Multi-label Classification
Despite of the pervasive existence of multi-label evasion attack, it is an open yet essential problem to characterize the origin of the adversarial vulnerability of a multi-label learning system and assess its attackabil…
Adversarial AttackClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Extreme Multi-label Learning for Semantic Matching in Product Search
We consider the problem of semantic matching in product search: given a customer query, retrieve all semantically related products from a huge catalog of size 100 million, or more. Because of large catalog spaces and rea…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Learning