SOTAVerified

Multi-Label Image Classification

The Multi-Label Image Classification focuses on predicting labels for images in a multi-class classification problem where each image may belong to more than one class.

Papers

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Showing 51–100 of 124 papers

TitleStatusHype
G2NetPL: Generic Game-Theoretic Network for Partial-Label Image Classification—0
Multi Label Image Classification using Adaptive Graph Convolutional Networks (ML-AGCN)—0
Two-Stream Transformer for Multi-Label Image ClassificationCode1
Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image ClassificationCode1
A Capsule Network for Hierarchical Multi-Label Image Classification—0
Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing LabelsCode0
PLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification—0
A Deep Model for Partial Multi-Label Image Classification with Curriculum Based Disambiguation—0
Self-supervised Learning in Remote Sensing: A ReviewCode1
Boosting Multi-Label Image Classification with Complementary Parallel Self-DistillationCode1
Spatial Consistency Loss for Training Multi-Label Classifiers from Single-Label Annotations—0
Graph Attention Transformer Network for Multi-Label Image ClassificationCode1
Unified smoke and fire detection in an evolutionary framework with self-supervised progressive data augment—0
Multi-relation Message Passing for Multi-label Text ClassificationCode0
Simple and Robust Loss Design for Multi-Label Learning with Missing LabelsCode1
Visual Transformers with Primal Object Queries for Multi-Label Image ClassificationCode1
Spatial-context-aware deep neural network for multi-class image classification—0
Benchmarking and scaling of deep learning models for land cover image classificationCode1
Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model BiasCode0
Contrastively Enforcing Distinctiveness for Multi-Label Classification—0
Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image Classification—0
SCIDA: Self-Correction Integrated Domain Adaptation from Single- to Multi-label Aerial ImagesCode0
Residual Attention: A Simple but Effective Method for Multi-Label RecognitionCode1
Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic SegmentationCode1
Multi-Label Image Classification with Contrastive Learning—0
Multi-layered Semantic Representation Network for Multi-label Image ClassificationCode1
Multi-Label Learning from Single Positive LabelsCode1
MlTr: Multi-label Classification with TransformerCode1
GM-MLIC: Graph Matching based Multi-Label Image Classification—0
Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural NetworkCode0
Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and BenchmarkCode0
Efficient Online ML API Selection for Multi-Label Classification Tasks—0
Coarse to Fine: Multi-label Image Classification with Global/Local Attention—0
Deep Semantic Dictionary Learning for Multi-label Image ClassificationCode1
Tensor Composition Net for Visual Relationship Prediction—0
General Multi-label Image Classification with TransformersCode1
Evaluating the performance of the LIME and Grad-CAM explanation methods on a LEGO multi-label image classification task—0
Reconstruction Regularized Deep Metric Learning for Multi-label Image Classification—0
Learning to Discover Multi-Class Attentional Regions for Multi-Label Image RecognitionCode1
Unsupervised Image Classification for Deep Representation LearningCode0
Semi-supervised Medical Image Classification with Relation-driven Self-ensembling ModelCode1
Learning from Noisy Labels with Noise Modeling Network—0
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision—0
Learn to Predict Sets Using Feed-Forward Neural Networks—0
Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification—0
In-domain representation learning for remote sensingCode0
Distance-based Composable Representations with Neural Networks—0
Visual Attention Consistency Under Image Transforms for Multi-Label Image ClassificationCode0
SoDeep: a Sorting Deep net to learn ranking loss surrogatesCode0
Multi-View Matrix Completion for Multi-Label Image Classification—0
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All datasetsBigEarthNetBigEarthNet (official test set)MSCOCOBigEarthNet-S1 (official test set)BigEarthNet-10%VizWiz-ClassificationVOC2007

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MoCo-v2 (ResNet50, fine tune)mAP (micro)91.8—Unverified
2MoCo-v3 (ViT-S/16, fine tune)mAP (micro)89.9—Unverified
3MoCo-v2 (ResNet18, fine tune)mAP (micro)89.3—Unverified
4MAE (ViT-S/16, fine tune)mAP (micro)88.9—Unverified
5DINO-MCmAP (micro)88.75—Unverified
6WideResNet-B5-ECAFScore79—Unverified
7ViTM/20FScore77.1—Unverified
8ResNet50FScore76.8—Unverified
9ResNet50mAP (macro)75.36—Unverified
10MLPMixerFScore75.2—Unverified
#ModelMetricClaimedVerifiedStatus
1MoCov3 (ViT-S/16)mAP (micro)89.3—Unverified
2FG-MAE (ViT-S/16)mAP (micro)89.3—Unverified
3MoCov2 (ResNet50)mAP (micro)88.7—Unverified
4MAE (ViT-S/16)mAP (micro)88.6—Unverified
5ViT-S/16mAP (micro)87.8—Unverified
6ResNet50F1 Score76.8—Unverified
#ModelMetricClaimedVerifiedStatus
1IDA-SwinL(H) 384mAP90.3—Unverified
2ML-AGCNmean average precision86.9—Unverified
3IDA-R101(H) 576mAP86.3—Unverified
4IDA-R101(H)mAP84.8—Unverified
#ModelMetricClaimedVerifiedStatus
1FG-MAE (ViT-S/16)mAP (micro)82.7—Unverified
2MAE (ViT-S/16)mAP (micro)81.3—Unverified
3ViT-S/16mAP (micro)79.5—Unverified
#ModelMetricClaimedVerifiedStatus
1DINO-MCmean average precision84.2—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet151Accuracy47.5—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet101MAP96.8—Unverified