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 51–75 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
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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