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 101–124 of 124 papers

TitleStatusHype
Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels—0
Semantic Embedded Deep Neural Network: A Generic Approach to Boost Multi-Label Image Classification Performance—0
Coarse to Fine: Multi-label Image Classification with Global/Local Attention—0
Unsupervised domain adaptation by learning using privileged information—0
BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding—0
Weakly-supervised Learning of Mid-level Features for Pedestrian Attribute Recognition and Localization—0
Spatial Consistency Loss for Training Multi-Label Classifiers from Single-Label Annotations—0
Spatial-context-aware deep neural network for multi-class image classification—0
G2NetPL: Generic Game-Theoretic Network for Partial-Label Image Classification—0
Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision—0
GM-MLIC: Graph Matching based Multi-Label Image Classification—0
Efficient Online ML API Selection for Multi-Label Classification Tasks—0
Heterogeneous Visual Features Fusion via Sparse Multimodal Machine—0
HSVLT: Hierarchical Scale-Aware Vision-Language Transformer for Multi-Label Image Classification—0
When the Small-Loss Trick is Not Enough: Multi-Label Image Classification with Noisy Labels Applied to CCTV Sewer Inspections—0
Auxiliary Tasks Enhanced Dual-affinity Learning for Weakly Supervised Semantic Segmentation—0
Joint Learning of Set Cardinality and State Distribution—0
A Capsule Network for Hierarchical Multi-Label Image Classification—0
Free Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification—0
FolkTalent: Enhancing Classification and Tagging of Indian Folk Paintings—0
Learning from Noisy Labels with Noise Modeling Network—0
Learning Graph Structure for Multi-Label Image Classification via Clique Generation—0
Attribute Recognition by Joint Recurrent Learning of Context and Correlation—0
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision—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