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 76–100 of 124 papers

TitleStatusHype
Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision—0
GM-MLIC: Graph Matching based Multi-Label Image Classification—0
Heterogeneous Visual Features Fusion via Sparse Multimodal Machine—0
HSVLT: Hierarchical Scale-Aware Vision-Language Transformer for Multi-Label Image Classification—0
Joint Learning of Set Cardinality and State Distribution—0
Learning from Noisy Labels with Noise Modeling Network—0
Learning Graph Structure for Multi-Label Image Classification via Clique Generation—0
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision—0
Learn to Predict Sets Using Feed-Forward Neural Networks—0
MS-Twins: Multi-Scale Deep Self-Attention Networks for Medical Image Segmentation—0
Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning—0
Multi-Label Adversarial Perturbations—0
Multi-label Classification using Deep Multi-order Context-aware Kernel Networks—0
Multi-Label Continual Learning for the Medical Domain: A Novel Benchmark—0
Reconstruction Regularized Deep Metric Learning for Multi-label Image Classification—0
Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes—0
Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image Classification—0
Saliency-based Sequential Image Attention with Multiset Prediction—0
Scene-Aware Label Graph Learning for Multi-Label Image Classification—0
Semantic Embedded Deep Neural Network: A Generic Approach to Boost Multi-Label Image Classification Performance—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
Tensor Composition Net for Visual Relationship Prediction—0
Text as Image: Learning Transferable Adapter for Multi-Label Classification—0
The impact of Compositionality in Zero-shot Multi-label action recognition for Object-based tasks—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