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
Estimating Physical Information Consistency of Channel Data Augmentation for Remote Sensing Images—0
Auxiliary Tasks Enhanced Dual-affinity Learning for Weakly Supervised Semantic Segmentation—0
ProbMCL: Simple Probabilistic Contrastive Learning for Multi-label Visual ClassificationCode0
MS-Twins: Multi-Scale Deep Self-Attention Networks for Medical Image Segmentation—0
Text as Image: Learning Transferable Adapter for Multi-Label Classification—0
SpliceMix: A Cross-scale and Semantic Blending Augmentation Strategy for Multi-label Image ClassificationCode0
Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification—0
Conditional Consistency Regularization for Semi-Supervised Multi-label Image Classification—0
PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image ClassificationCode0
Probability Guided Loss for Long-Tailed Multi-Label Image Classification—0
Pseudo Labels for Single Positive Multi-Label Learning—0
Semantic Embedded Deep Neural Network: A Generic Approach to Boost Multi-Label Image Classification Performance—0
Unsupervised domain adaptation by learning using privileged information—0
Towards Reliable Assessments of Demographic Disparities in Multi-Label Image Classifiers—0
Deep Dependency Networks for Multi-Label Classification—0
Multi-label Image Classification using Adaptive Graph Convolutional Networks: from a Single Domain to Multiple Domains—0
Deep Learning based Multi-Label Image Classification of Protest Activities—0
Scene-Aware Label Graph Learning for Multi-Label Image Classification—0
Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision—0
MuMIC -- Multimodal Embedding for Multi-label Image Classification with Tempered Sigmoid—0
G2NetPL: Generic Game-Theoretic Network for Partial-Label Image Classification—0
Multi Label Image Classification using Adaptive Graph Convolutional Networks (ML-AGCN)—0
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
Spatial Consistency Loss for Training Multi-Label Classifiers from Single-Label Annotations—0
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
Spatial-context-aware deep neural network for multi-class image classification—0
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
Multi-Label Image Classification with Contrastive Learning—0
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
Tensor Composition Net for Visual Relationship Prediction—0
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
Unsupervised Image Classification for Deep Representation LearningCode0
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
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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