Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Dense-Caption Matching and Frame-Selection Gating for Temporal Localization in VideoQA
Videos convey rich information. Dynamic spatio-temporal relationships between people/objects, and diverse multimodal events are present in a video clip. Hence, it is important to develop automated models that can accurat…
Image CaptioningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONQuestion Answering+2SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis
Recently, sentiment analysis has seen remarkable advance with the help of pre-training approaches. However, sentiment knowledge, such as sentiment words and aspect-sentiment pairs, is ignored in the process of pre-traini…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSentiment AnalysisStock Market PredictionQuda: Natural Language Queries for Visual Data Analytics
The identification of analytic tasks from free text is critical for visualization-oriented natural language interfaces (V-NLIs) to suggest effective visualizations. However, it is challenging due to the ambiguity and com…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONNatural Language QueriesParaphrase GenerationGenerating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques
Following each patient visit, physicians draft long semi-structured clinical summaries called SOAP notes. While invaluable to clinicians and researchers, creating digital SOAP notes is burdensome, contributing to physici…
Meeting SummarizationMulti-Label ClassificationSentenceInvestigating Class-level Difficulty Factors in Multi-label Classification Problems
This work investigates the use of class-level difficulty factors in multi-label classification problems for the first time. Four class-level difficulty factors are proposed: frequency, visual variation, semantic abstract…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONLinguistic Typology Features from Text: Inferring the Sparse Features of World Atlas of Language Structures
The use of linguistic typological resources in natural language processing has been steadily gaining more popularity. It has been observed that the use of typological information, often combined with distributed language…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONEntity Type Prediction in Knowledge Graphs using Embeddings
Open Knowledge Graphs (such as DBpedia, Wikidata, YAGO) have been recognized as the backbone of diverse applications in the field of data mining and information retrieval. Hence, the completeness and correctness of the K…
Entity TypingInformation RetrievalKnowledge GraphsMulti-Label Classification+5Natural language processing for achieving sustainable development: the case of neural labelling to enhance community profiling
In recent years, there has been an increasing interest in the application of Artificial Intelligence - and especially Machine Learning - to the field of Sustainable Development (SD). However, until now, NLP has not been …
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONGraftNet: An Engineering Implementation of CNN for Fine-grained Multi-label Task
Multi-label networks with branches are proved to perform well in both accuracy and speed, but lacks flexibility in providing dynamic extension onto new labels due to the low efficiency of re-work on annotating and traini…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-label Stream Classification with Self-Organizing Maps
Several learning algorithms have been proposed for offline multi-label classification. However, applications in areas such as traffic monitoring, social networks, and sensors produce data continuously, the so called data…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Unsupervised Person Re-identification via Multi-label Classification
The challenge of unsupervised person re-identification (ReID) lies in learning discriminative features without true labels. This paper formulates unsupervised person ReID as a multi-label classification task to progressi…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+5Data Augmentation Imbalance For Imbalanced Attribute Classification
Pedestrian attribute recognition is an important multi-label classification problem. Although the convolutional neural networks are prominent in learning discriminative features from images, the data imbalance in multi-l…
AttributeClassificationData AugmentationGeneral Classification+3Automated Diabetic Retinopathy Grading using Deep Convolutional Neural Network
Diabetic Retinopathy is a global health problem, influences 100 million individuals worldwide, and in the next few decades, these incidences are expected to reach epidemic proportions. Diabetic Retinopathy is a subtle ey…
Diabetic Retinopathy GradingGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONKnowledge Distillation for Mobile Edge Computation Offloading
Edge computation offloading allows mobile end devices to put execution of compute-intensive task on the edge servers. End devices can decide whether offload the tasks to edge servers, cloud servers or execute locally acc…
Imitation LearningKnowledge DistillationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSaliency-based Weighted Multi-label Linear Discriminant Analysis
In this paper, we propose a new variant of Linear Discriminant Analysis (LDA) to solve multi-label classification tasks. The proposed method is based on a probabilistic model for defining the weights of individual sample…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONExemplar Auditing for Multi-Label Biomedical Text Classification
Many practical applications of AI in medicine consist of semi-supervised discovery: The investigator aims to identify features of interest at a resolution more fine-grained than that of the available human labels. This i…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Extreme Multi-label Classification from Aggregated Labels
Extreme multi-label classification (XMC) is the problem of finding the relevant labels for an input, from a very large universe of possible labels. We consider XMC in the setting where labels are available only for group…
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMulti-Label Classification+1Adversarial Learning for Personalized Tag Recommendation
We have recently seen great progress in image classification due to the success of deep convolutional neural networks and the availability of large-scale datasets. Most of the existing work focuses on single-label image …
General Classificationimage-classificationImage ClassificationMulti-Label Classification+2A Thorough Comparison Study on Adversarial Attacks and Defenses for Common Thorax Disease Classification in Chest X-rays
Recently, deep neural networks (DNNs) have made great progress on automated diagnosis with chest X-rays images. However, DNNs are vulnerable to adversarial examples, which may cause misdiagnoses to patients when applying…
Adversarial AttackGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTResNet: High Performance GPU-Dedicated Architecture
Many deep learning models, developed in recent years, reach higher ImageNet accuracy than ResNet50, with fewer or comparable FLOPS count. While FLOPs are often seen as a proxy for network efficiency, when measuring actua…
Fine-Grained Image ClassificationGeneral ClassificationGPUImage Classification+5