Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
G2NetPL: Generic Game-Theoretic Network for Partial-Label Image Classification
Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, since it could be expensive in practice to annotate all the labels in every …
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+3Uncertainty in Extreme Multi-label Classification
Uncertainty quantification is one of the most crucial tasks to obtain trustworthy and reliable machine learning models for decision making. However, most research in this domain has only focused on problems with small la…
ClassificationDecision MakingExtreme Multi-Label ClassificationMulti-Label Classification+4ELIAS: End-to-End Learning to Index and Search in Large Output Spaces
Extreme multi-label classification (XMC) is a popular framework for solving many real-world problems that require accurate prediction from a very large number of potential output choices. A popular approach for dealing w…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONKnowledge-Driven New Drug Recommendation
Drug recommendation assists doctors in prescribing personalized medications to patients based on their health conditions. Existing drug recommendation solutions adopt the supervised multi-label classification setup and o…
Few-Shot LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONImproving Dense Contrastive Learning with Dense Negative Pairs
Many contrastive representation learning methods learn a single global representation of an entire image. However, dense contrastive representation learning methods such as DenseCL (Wang et al., 2021) can learn better re…
Contrastive LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRepresentation Learning+1Concise and interpretable multi-label rule sets
Multi-label classification is becoming increasingly ubiquitous, but not much attention has been paid to interpretability. In this paper, we develop a multi-label classifier that can be represented as a concise set of sim…
DiversityMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMultiGuard: Provably Robust Multi-label Classification against Adversarial Examples
Multi-label classification, which predicts a set of labels for an input, has many applications. However, multiple recent studies showed that multi-label classification is vulnerable to adversarial examples. In particular…
ClassificationMulti-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAn Ensemble of Convolutional Neural Networks to Detect Foliar Diseases in Apple Plants
Apple diseases, if not diagnosed early, can lead to massive resource loss and pose a serious threat to humans and animals who consume the infected apples. Hence, it is critical to diagnose these diseases early in order t…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-label Classification of Scientific Research Documents Across Domains and Languages
Automatically organizing scholarly literature is a necessary and challenging task. By assigning scientific research publications key concepts, researchers, policymakers, and the general public are able to search for and …
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONtext-classification+1Multi Label Image Classification using Adaptive Graph Convolutional Networks (ML-AGCN)
In this paper, a novel graph-based approach for multi-label image classification called Multi-Label Adaptive Graph Convolutional Network (ML-AGCN) is introduced. Graph-based methods have shown great potential in the fiel…
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring
Numerous real-world problems from a diverse set of application areas exist that exhibit temporal dependencies. We focus on a specific type of time series classification which we refer to as aggregated time series classif…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTime Series+2Hierarchical MixUp Multi-label Classification with Imbalanced Interdisciplinary Research Proposals
Funding agencies are largely relied on a topic matching between domain experts and research proposals to assign proposal reviewers. As proposals are increasingly interdisciplinary, it is challenging to profile the interd…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONObj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks
Visual tasks vary a lot in their output formats and concerned contents, therefore it is hard to process them with an identical structure. One main obstacle lies in the high-dimensional outputs in object-level visual task…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONObjectObject Detection+1Source detection via multi-label classification
Radio source detection through conventional algorithms has been unreliable when trying to solve for large number of sources in the presence of low SINR and less number of snapshots. We address this by reformulating sourc…
ClassificationMulti-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONBERT-based Ensemble Approaches for Hate Speech Detection
With the freedom of communication provided in online social media, hate speech has increasingly generated. This leads to cyber conflicts affecting social life at the individual and national levels. As a result, hateful c…
Hate Speech DetectionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA patch-based architecture for multi-label classification from single label annotations
In this paper, we propose a patch-based architecture for multi-label classification problems where only a single positive label is observed in images of the dataset. Our contributions are twofold. First, we introduce a l…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONCombining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image Classification
Multi-label image classification allows predicting a set of labels from a given image. Unlike multiclass classification, where only one label per image is assigned, such a setup is applicable for a broader range of appli…
Binary ClassificationClassificationimage-classificationImage Classification+3Class-Level Logit Perturbation
Features, logits, and labels are the three primary data when a sample passes through a deep neural network. Feature perturbation and label perturbation receive increasing attention in recent years. They have been proven …
Data Augmentationimage-classificationImage ClassificationMulti-Label Classification+1A Capsule Network for Hierarchical Multi-Label Image Classification
Image classification is one of the most important areas in computer vision. Hierarchical multi-label classification applies when a multi-class image classification problem is arranged into smaller ones based upon a hiera…
ClassificationHierarchical Multi-label Classificationimage-classificationImage Classification+3Exploring LRP and Grad-CAM visualization to interpret multi-label-multi-class pathology prediction using chest radiography
The area of interpretable deep neural networks has received increased attention in recent years due to the need for transparency in various fields, including medicine, healthcare, stock market analysis, compliance with l…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATION