Long Tail Learning On Coco Mlt

Metriken

Average mAP

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
LMPT(ViT-B/16)	66.19	LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition
CLIP(ViT-B/16)	60.17	Learning Transferable Visual Models From Natural Language Supervision
LMPT(ResNet-50)	58.97	LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition
LTML(ResNet-50)	56.90	Long-Tailed Multi-Label Visual Recognition by Collaborative Training on Uniform and Re-Balanced Samplings
CLIP(ResNet-50)	56.19	Learning Transferable Visual Models From Natural Language Supervision
PG Loss(ResNet-50)	54.43	Probability Guided Loss for Long-Tailed Multi-Label Image Classification
DB Focal(ResNet-50)	53.55	Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets
Focal Loss(ResNet-50)	49.46	Focal Loss for Dense Object Detection
CB Loss(ResNet-50)	49.06	Class-Balanced Loss Based on Effective Number of Samples
RS(ResNet-50)	46.97	Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
OLTR(ResNet-50)	45.83	Large-Scale Long-Tailed Recognition in an Open World
ML-GCN(ResNet-50)	44.24	Multi-Label Image Recognition with Graph Convolutional Networks
LDAM(ResNet-50)	40.53	Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

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Long Tail Learning On Coco Mlt | SOTA | HyperAI