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Long Tail Learning On Voc Mlt

Metrics

Average mAP

Results

Performance results of various models on this benchmark

		Paper Title	Repository
LMPT(ViT-B/16)	87.88	LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition
CLIP(ViT-B/16)	85.77	Learning Transferable Visual Models From Natural Language Supervision
LMPT(ResNet-50)	85.44	LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition
CLIP(ResNet-50)	84.30	Learning Transferable Visual Models From Natural Language Supervision
LTML(ResNet-50)	81.44	Long-Tailed Multi-Label Visual Recognition by Collaborative Training on Uniform and Re-Balanced Samplings	-
PG Loss(ResNet-50)	80.37	Probability Guided Loss for Long-Tailed Multi-Label Image Classification	-
DB Focal(ResNet-50)	78.94	Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets
RS(ResNet-50)	75.38	Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
CB Focal(ResNet-50)	75.24	Class-Balanced Loss Based on Effective Number of Samples
Focal Loss(ResNet-50)	73.88	Focal Loss for Dense Object Detection
OLTR(ResNet-50)	71.02	Large-Scale Long-Tailed Recognition in an Open World
LDAM(ResNet-50)	70.73	Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
ML-GCN(ResNet-50)	68.92	Multi-Label Image Recognition with Graph Convolutional Networks

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