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ResearchFormerly WizWand

Continual Learning on ImageNet-R 10-task split

82.06FAAJoint-Train
57.630463.972770.31576.6573Sep 29, 2025
Updated 7mo ago

Evaluation results

MethodLinks
Joint-Train2025.0982.06—
SMoPE2025.0979.3284.39
VQ-Prompt2025.0978.7183.24
ConvPrompt2025.0977.381.46
CPrompt2025.0977.1482.92
CODA-Prompt2025.0975.4581.59
OVOR2025.0975.2579.78
NoRGa2025.0974.3979.68
HiDe-Prompt2025.0974.2579.64
SMoPEPre-training=iBOT-1K2025.0972.1777.24
VQ-PromptPre-training=iBOT-1K2025.0971.6876.66
Deep L2P++depth=deep2025.0971.6679.63
DualPrompt2025.0971.3278.94
OVORPre-training=iBOT-1K2025.0969.4674.95
L2P++2025.0969.2978.3
SMoPEPre-training=DINO-1K2025.0968.6175.14
VQ-PromptPre-training=DINO-1K2025.0968.4274.43
CODA-PromptPre-training=iBOT-1K2025.0966.5673.14
OVORPre-training=DINO-1K2025.0966.5572.89
NoRGaPre-training=iBOT-1K2025.0965.8172.11
HiDe-PromptPre-training=iBOT-1K2025.0965.3471.67
CODA-PromptPre-training=DINO-1K2025.0963.1569.73
NoRGaPre-training=DINO-1K2025.0963.0270.25
HiDe-PromptPre-training=DINO-1K2025.0962.9470.04
DualPromptPre-training=iBOT-1K2025.0961.5167.11
DualPromptPre-training=DINO-1K2025.0958.5764.89