BALLAD (ViT-B/16)

Reported on 5 benchmarks across 5 tasks · 1 paper · 5 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology3 results

Generalized Few-Shot ClassificationonCIFAR-100-LT (ρ=100)
Error Rate· uses extra data· 2021-11-29
22.2
best: 10.9 (LPT)
SOTA
A Simple Long-Tailed Recognition Baseline via Vision-Language Model arXiv:2111.14745
Long-tail LearningonCIFAR-100-LT (ρ=100)
Error Rate· uses extra data· 2021-11-29
22.2
best: 10.9 (LPT)
SOTA
A Simple Long-Tailed Recognition Baseline via Vision-Language Model arXiv:2111.14745
Generalized Few-Shot LearningonCIFAR-100-LT (ρ=100)
Error Rate· uses extra data· 2021-11-29
22.2
best: 10.9 (LPT)
SOTA
A Simple Long-Tailed Recognition Baseline via Vision-Language Model arXiv:2111.14745

Computer Vision2 results

Image ClassificationonCIFAR-100-LT (ρ=100)
Error Rate· uses extra data· 2021-11-29
22.2
best: 10.9 (LPT)
SOTA
A Simple Long-Tailed Recognition Baseline via Vision-Language Model arXiv:2111.14745
Few-Shot Image ClassificationonCIFAR-100-LT (ρ=100)
Error Rate· uses extra data· 2021-11-29
22.2
best: 10.9 (LPT)
SOTA
A Simple Long-Tailed Recognition Baseline via Vision-Language Model arXiv:2111.14745