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المنصة
الرئيسية
SOTA
التعلم ذي الذيل الطويل
Long Tail Learning On Places Lt
Long Tail Learning On Places Lt
المقاييس
Top-1 Accuracy
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
Columns
اسم النموذج
Top-1 Accuracy
Paper Title
LIFT (ViT-L/14)
53.7
Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
LIFT (ViT-B/16)
52.2
Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
MAM (ViT-B/16)
51.4
Improving Image Recognition by Retrieving from Web-Scale Image-Text Data
VL-LTR (ViT-B-16)
50.1
VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition
BALLAD(ViT-B-16)
49.5
A Simple Long-Tailed Recognition Baseline via Vision-Language Model
BALLAD(ResNet-50×16)
49.3
A Simple Long-Tailed Recognition Baseline via Vision-Language Model
VL-LTR (ResNet-50)
48.0
VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition
BALLAD(ResNet-101)
47.9
A Simple Long-Tailed Recognition Baseline via Vision-Language Model
RAC (ViT-B-16)
47.17
Retrieval Augmented Classification for Long-Tail Visual Recognition
BALLAD(ResNet-50)
46.5
A Simple Long-Tailed Recognition Baseline via Vision-Language Model
APA (SE-ResNet-50)
42.0
Adaptive Parametric Activation
GPaCo (ResNet-152)
41.7
Generalized Parametric Contrastive Learning
Difficulty-Net (ResNet-152)
41.7
Difficulty-Net: Learning to Predict Difficulty for Long-Tailed Recognition
NCL(ResNet-152)
41.5
Nested Collaborative Learning for Long-Tailed Visual Recognition
TADE
41.3
Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition
PaCo
41.2
Parametric Contrastive Learning
OPeN (ResNet-152)
40.5
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images
IEM
39.7
Inflated Episodic Memory With Region Self-Attention for Long-Tailed Visual Recognition
DisAlign
39.3
Distribution Alignment: A Unified Framework for Long-tail Visual Recognition
LDAM-DRS-RSG
39.3
RSG: A Simple but Effective Module for Learning Imbalanced Datasets
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