Papers Multi-Label Image Classification
“Multi-Label Image Classification” 태그가 달린 논문 131편 · 필터 해제
Combining 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+3A 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+3Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing Labels
Contrastive learning (CL) has shown impressive advances in image representation learning in whichever supervised multi-class classification or unsupervised learning. However, these CL methods fail to be directly adapted …
Contrastive Learningimage-classificationImage ClassificationMissing Labels+4PLMCL: Partial-Label Momentum Curriculum Learning for Multi-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, given the fact that it could be expensive in practice to annotate all labels…
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2A Deep Model for Partial Multi-Label Image Classification with Curriculum Based Disambiguation
In this paper, we study the partial multi-label (PML) image classification problem, where each image is annotated with a candidate label set consists of multiple relevant labels and other noisy labels. Existing PML metho…
image-classificationImage ClassificationMulti-Label Image ClassificationSelf-supervised Learning in Remote Sensing: A Review
In deep learning research, self-supervised learning (SSL) has received great attention triggering interest within both the computer vision and remote sensing communities. While there has been a big success in computer vi…
Earth ObservationImage ClassificationMulti-Label Image ClassificationSelf-Supervised LearningBoosting Multi-Label Image Classification with Complementary Parallel Self-Distillation
Multi-Label Image Classification (MLIC) approaches usually exploit label correlations to achieve good performance. However, emphasizing correlation like co-occurrence may overlook discriminative features of the target it…
image-classificationImage ClassificationKnowledge DistillationMulti-Label Image ClassificationSpatial Consistency Loss for Training Multi-Label Classifiers from Single-Label Annotations
As natural images usually contain multiple objects, multi-label image classification is more applicable "in the wild" than single-label classification. However, exhaustively annotating images with every object of interes…
Data Augmentationimage-classificationImage ClassificationMulti-Label Classification+2Graph Attention Transformer Network for Multi-Label Image Classification
Multi-label classification aims to recognize multiple objects or attributes from images. However, it is challenging to learn from proper label graphs to effectively characterize such inter-label correlations or dependenc…
ClassificationGraph Attentionimage-classificationImage Classification+3Unified smoke and fire detection in an evolutionary framework with self-supervised progressive data augment
Few researches have studied simultaneous detection of smoke and flame accompanying fires due to their different physical natures that lead to uncertain fluid patterns. In this study, we collect a large image data set to …
Fire Detectionimage-classificationImage ClassificationMulti-Label Image Classification+1Multi-relation Message Passing for Multi-label Text Classification
A well-known challenge associated with the multi-label classification problem is modelling dependencies between labels. Most attempts at modelling label dependencies focus on co-occurrences, ignoring the valuable informa…
Classificationimage-classificationImage ClassificationMulti-Label Classification+7Simple and Robust Loss Design for Multi-Label Learning with Missing Labels
Multi-label learning in the presence of missing labels (MLML) is a challenging problem. Existing methods mainly focus on the design of network structures or training schemes, which increase the complexity of implementati…
image-classificationImage ClassificationMissing LabelsMulti-Label Image Classification+1Visual Transformers with Primal Object Queries for Multi-Label Image Classification
Multi-label image classification is about predicting a set of class labels that can be considered as orderless sequential data. Transformers process the sequential data as a whole, therefore they are inherently good at s…
Decoderimage-classificationImage ClassificationMulti-Label Classification+5Spatial-context-aware deep neural network for multi-class image classification
Multi-label image classification is a fundamental but challenging task in computer vision. Over the past few decades, solutions exploring relationships between semantic labels have made great progress. However, the under…
Classificationimage-classificationImage ClassificationMulti-Label Image ClassificationBenchmarking and scaling of deep learning models for land cover image classification
The availability of the sheer volume of Copernicus Sentinel-2 imagery has created new opportunities for exploiting deep learning (DL) methods for land use land cover (LULC) image classification. However, an extensive set…
BenchmarkingClassificationGPUimage-classification+3Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias
Contextual information is a valuable cue for Deep Neural Networks (DNNs) to learn better representations and improve accuracy. However, co-occurrence bias in the training dataset may hamper a DNN model's generalizability…
Active LearningAttributeimage-classificationImage Classification+3Contrastively Enforcing Distinctiveness for Multi-Label Classification
Recently, as an effective way of learning latent representations, contrastive learning has been increasingly popular and successful in various domains. The success of constrastive learning in single-label classifications…
ClassificationContrastive Learningimage-classificationImage Classification+3Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image Classification
Annotated images are required for both supervised model training and evaluation in image classification. Manually annotating images is arduous and expensive, especially for multi-labeled images. A recent trend for conduc…
Active Learningimage-classificationImage ClassificationMulti-Label Image ClassificationSCIDA: 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+2Residual Attention: A Simple but Effective Method for Multi-Label Recognition
Multi-label image recognition is a challenging computer vision task of practical use. Progresses in this area, however, are often characterized by complicated methods, heavy computations, and lack of intuitive explanatio…
Multi-Label Image ClassificationMulti-Label Image Recognition