Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Learning label-label correlations in Extreme Multi-label Classification via Label Features
Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Recent works in this domain have increasingly fo…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text Classification+4Using In-Service Train Vibration for Detecting Railway Maintenance Needs
The need for the maintenance of railway track systems have been increasing. Traditional methods that are currently being used are either inaccurate, labor and time intensive, or does not enable continuous monitoring of t…
Binary ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONLearning Multiple Object States from Actions via Large Language Models
Recognizing the states of objects in a video is crucial in understanding the scene beyond actions and objects. For instance, an egg can be raw, cracked, and whisked while cooking an omelet, and these states can coexist s…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONObjectWorld KnowledgeExplainable Multi-Label Classification of MBTI Types
In this study, we aim to identify the most effective machine learning model for accurately classifying Myers-Briggs Type Indicator (MBTI) types from Reddit posts and a Kaggle data set. We apply multi-label classification…
ClassificationExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)Multi-Label Classification+2Expanding the Horizon: Enabling Hybrid Quantum Transfer Learning for Long-Tailed Chest X-Ray Classification
Quantum machine learning (QML) has the potential for improving the multi-label classification of rare, albeit critical, diseases in large-scale chest x-ray (CXR) datasets due to theoretical quantum advantages over classi…
Binary ClassificationClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+3Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning
Multi-view learning has become a popular research topic in recent years, but research on the cross-application of classic multi-label classification and multi-view learning is still in its early stages. In this paper, we…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningMULTI-VIEW LEARNINGFederated Learning with Only Positive Labels by Exploring Label Correlations
Federated learning aims to collaboratively learn a model by using the data from multiple users under privacy constraints. In this paper, we study the multi-label classification problem under the federated learning settin…
Federated LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONDeep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification
We present a unified framework called deep dependency networks (DDNs) that combines dependency networks and deep learning architectures for multi-label classification, with a particular emphasis on image and video data. …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA Complexity Map of Probabilistic Reasoning for Neurosymbolic Classification Techniques
Neurosymbolic artificial intelligence is a growing field of research aiming to combine neural network learning capabilities with the reasoning abilities of symbolic systems. Informed multi-label classification is a sub-f…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONNavigateMulti-Label Continual Learning for the Medical Domain: A Novel Benchmark
Despite the critical importance of the medical domain in Deep Learning, most of the research in this area solely focuses on training models in static environments. It is only in recent years that research has begun to ad…
class-incremental learningClass Incremental LearningContinual LearningImage Classification+5Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition
This paper presents two models to address the problem of multi-person activity recognition using ambient sensors in a home. The first model, Seq2Res, uses a sequence generation approach to separate sensor events from dif…
Activity RecognitionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONHierarchical Insights: Exploiting Structural Similarities for Reliable 3D Semantic Segmentation
Safety-critical applications such as autonomous driving require robust 3D environment perception algorithms capable of handling diverse and ambiguous surroundings. The predictive performance of classification models is h…
3D Semantic SegmentationAutonomous DrivingHierarchical Multi-label ClassificationLIDAR Semantic Segmentation+3Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding
Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa. While these models demonstrate remarkable performance on general datas…
Domain AdaptationExtractive Question-AnsweringMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+5Supervised Gradual Machine Learning for Aspect Category Detection
Aspect Category Detection (ACD) aims to identify implicit and explicit aspects in a given review sentence. The state-of-the-art approaches for ACD use Deep Neural Networks (DNNs) to address the problem as a multi-label c…
Aspect Category DetectionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSentence+1IITK at SemEval-2024 Task 4: Hierarchical Embeddings for Detection of Persuasion Techniques in Memes
Memes are one of the most popular types of content used in an online disinformation campaign. They are primarily effective on social media platforms since they can easily reach many users. Memes in a disinformation campa…
Binary ClassificationClassificationHierarchical Multi-label ClassificationMeme Classification+3Diverse and Tailored Image Generation for Zero-shot Multi-label Classification
Recently, zero-shot multi-label classification has garnered considerable attention for its capacity to operate predictions on unseen labels without human annotations. Nevertheless, prevailing approaches often use seen cl…
Image GenerationLanguage ModellingLarge Language ModelMulti-Label Classification+3BCAmirs at SemEval-2024 Task 4: Beyond Words: A Multimodal and Multilingual Exploration of Persuasion in Memes
Memes, combining text and images, frequently use metaphors to convey persuasive messages, shaping public opinion. Motivated by this, our team engaged in SemEval-2024 Task 4, a hierarchical multi-label classification task…
Caption GenerationHierarchical Multi-label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONVHM: Versatile and Honest Vision Language Model for Remote Sensing Image Analysis
This paper develops a Versatile and Honest vision language Model (VHM) for remote sensing image analysis. VHM is built on a large-scale remote sensing image-text dataset with rich-content captions (VersaD), and an honest…
HallucinationImage CaptioningLanguage ModelingLanguage Modelling+6Modeling Weather Uncertainty for Multi-weather Co-Presence Estimation
Images from outdoor scenes may be taken under various weather conditions. It is well studied that weather impacts the performance of computer vision algorithms and needs to be handled properly. However, existing algorith…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSemantic SegmentationTabular Learning: Encoding for Entity and Context Embeddings
Examining the effect of different encoding techniques on entity and context embeddings, the goal of this work is to challenge commonly used Ordinal encoding for tabular learning. Applying different preprocessing methods …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATION