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 1–10 of 124 papers

TitleStatusHype
Domain Adaptation for Multi-label Image Classification: a Discriminator-free ApproachCode1
Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning—0
Multi-label Classification using Deep Multi-order Context-aware Kernel Networks—0
When the Small-Loss Trick is Not Enough: Multi-Label Image Classification with Noisy Labels Applied to CCTV Sewer Inspections—0
Category-Prompt Refined Feature Learning for Long-Tailed Multi-Label Image ClassificationCode1
HSVLT: Hierarchical Scale-Aware Vision-Language Transformer for Multi-Label Image Classification—0
Multi-Label Plant Species Classification with Self-Supervised Vision TransformersCode1
Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes—0
reBEN: Refined BigEarthNet Dataset for Remote Sensing Image AnalysisCode1
Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels—0
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All datasetsBigEarthNetBigEarthNet (official test set)MSCOCOBigEarthNet-S1 (official test set)BigEarthNet-10%VizWiz-ClassificationVOC2007

Benchmark Results

#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