Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Two-Way Multi-Label Loss
A natural image frequently contains multiple classification targets, accordingly providing multiple class labels rather than a single label per image. While the single-label classification is effectively addressed by…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONOrder-Prompted Tag Sequence Generation for Video Tagging
Video Tagging intends to infer multiple tags spanning relevant content for a given video. Typically, video tags are freely defined and uploaded by a variety of users, so they have two characteristics: abundant in qua…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTAGHolistic Label Correction for Noisy Multi-Label Classification
Multi-label classification aims to learn classification models from instances associated with multiple labels. It is pivotal to learn and utilize the label dependence among multiple labels in multi-label classificati…
ClassificationMemorizationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSaliency Regularization for Self-Training with Partial Annotations
Partially annotated images are easy to obtain in multi-label classification. However, unknown labels in partially annotated images exacerbate the positive-negative imbalance inherent in multi-label classification, wh…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-label Image Recognition with Partial LabelsHierarchical Forgery Classifier On Multi-modality Face Forgery Clues
Face forgery detection plays an important role in personal privacy and social security. With the development of adversarial generative models, high-quality forgery images become more and more indistinguishable from real …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONFirst Demonstration of 512-Color Shift Keying Signal Demodulation Using Neural Equalization for Optical Camera Communication
This paper experimentally demonstrates 512 color shift keying (CSK) signal transmission for optical camera communication (OCC). We achieved error-free operation with a CMOS image sensor module and a multi-label classific…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONNEEDED: Introducing Hierarchical Transformer to Eye Diseases Diagnosis
With the development of natural language processing techniques(NLP), automatic diagnosis of eye diseases using ophthalmology electronic medical records (OEMR) has become possible. It aims to evaluate the condition of bot…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSentenceConfidence-Aware Paced-Curriculum Learning by Label Smoothing for Surgical Scene Understanding
Curriculum learning and self-paced learning are the training strategies that gradually feed the samples from easy to more complex. They have captivated increasing attention due to their excellent performance in robotic v…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONScene UnderstandingSemantic SegmentationCNO-LSTM: A Chaotic Neural Oscillatory Long Short-Term Memory Model for Text Classification
Long Short-Term Memory (LSTM) networks are unique to exercise data in its memory cell with long-term memory as Natural Language Processing (NLP) tasks have inklings of intensive time and computational power due to their …
ClassificationGPULanguage ModelingLanguage Modelling+4Tencent AVS: A Holistic Ads Video Dataset for Multi-modal Scene Segmentation
Temporal video segmentation and classification have been advanced greatly by public benchmarks in recent years. However, such research still mainly focuses on human actions, failing to describe videos in a holistic view.…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONScene SegmentationSegmentation+2RLSEP: Learning Label Ranks for Multi-label Classification
Multi-label ranking maps instances to a ranked set of predicted labels from multiple possible classes. The ranking approach for multi-label learning problems received attention for its success in multi-label classificati…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningYolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detection
Out-of-distribution (OOD) detection has attracted a large amount of attention from the machine learning research community in recent years due to its importance in deployed systems. Most of the previous studies focused o…
ClassificationMulti-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+5Deep Active Learning for Multi-Label Classification of Remote Sensing Images
In this letter, we introduce deep active learning (AL) for multi-label classification (MLC) problems in remote sensing (RS). In particular, we investigate the effectiveness of several AL query functions for MLC of RS ima…
Active LearningClusteringDiversityMulti-Label Classification+2Learning Disentangled Label Representations for Multi-label Classification
Although various methods have been proposed for multi-label classification, most approaches still follow the feature learning mechanism of the single-label (multi-class) classification, namely, learning a shared image fe…
AttributeClassificationDisentanglementMulti-class Classification+5An Effective Deployment of Contrastive Learning in Multi-label Text Classification
The effectiveness of contrastive learning technology in natural language processing tasks is yet to be explored and analyzed. How to construct positive and negative samples correctly and reasonably is the core challenge …
ClassificationContrastive LearningMulti-Label ClassificationMulti Label Text Classification+3X-PuDu at SemEval-2022 Task 6: Multilingual Learning for English and Arabic Sarcasm Detection
Detecting sarcasm and verbal irony from people's subjective statements is crucial to understanding their intended meanings and real sentiments and positions in social scenarios. This paper describes the X-PuDu system tha…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONNatural Language UnderstandingSarcasm Detection+1A Cross-Conformal Predictor for Multi-label Classification
Unlike the typical classification setting where each instance is associated with a single class, in multi-label learning each instance is associated with multiple classes simultaneously. Therefore the learning task in th…
ClassificationConformal PredictionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Identifying Incorrect Annotations in Multi-Label Classification Data
In multi-label classification, each example in a dataset may be annotated as belonging to one or more classes (or none of the classes). Example applications include image (or document) tagging where each possible tag eit…
ClassificationLabel Error DetectionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt
Automatic International Classification of Diseases (ICD) coding aims to assign multiple ICD codes to a medical note with an average of 3,000+ tokens. This task is challenging due to the high-dimensional space of multi-la…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONCollaborative Training of Medical Artificial Intelligence Models with non-uniform Labels
Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep learning (DL). However, building powerful and robust DL models requires training with large multi-party datasets. While m…
Federated LearningMedical DiagnosisMedical Image AnalysisMulti-Label Classification+2