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Models/AM3-TADAM

AM3-TADAM

Reported on 10 benchmarks across 2 tasks · 1 paper · 8 SOTA

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

Computer Vision10 results

Image ClassificationonMini-Imagenet 5-way (1-shot)
Accuracy· 2019-02-19
65.3
best: 97.95 (SgVA-CLIP)
SOTA
Adaptive Cross-Modal Few-Shot Learning arXiv:1902.07104
Image ClassificationonMini-Imagenet 5-way (10-shot)
Accuracy· 2019-02-19
81.57
best: 90.03 (PT+MAP)
SOTA
Adaptive Cross-Modal Few-Shot Learning arXiv:1902.07104
Image ClassificationonTiered ImageNet 5-way (1-shot)
Accuracy· 2019-02-19
69.08
best: 96.8 (CAML [Laion-2b])
SOTA
Adaptive Cross-Modal Few-Shot Learning arXiv:1902.07104
Image ClassificationonTiered ImageNet 5-way (5-shot)
Accuracy· 2019-02-19
82.58
best: 98.8 (CAML [Laion-2b])
SOTA
Adaptive Cross-Modal Few-Shot Learning arXiv:1902.07104
Few-Shot Image ClassificationonMini-Imagenet 5-way (1-shot)
Accuracy· 2019-02-19
65.3
best: 97.95 (SgVA-CLIP)
SOTA
Adaptive Cross-Modal Few-Shot Learning arXiv:1902.07104
Few-Shot Image ClassificationonMini-Imagenet 5-way (10-shot)
Accuracy· 2019-02-19
81.57
best: 90.03 (PT+MAP)
SOTA
Adaptive Cross-Modal Few-Shot Learning arXiv:1902.07104
Few-Shot Image ClassificationonTiered ImageNet 5-way (1-shot)
Accuracy· 2019-02-19
69.08
best: 96.8 (CAML [Laion-2b])
SOTA
Adaptive Cross-Modal Few-Shot Learning arXiv:1902.07104
Few-Shot Image ClassificationonTiered ImageNet 5-way (5-shot)
Accuracy· 2019-02-19
82.58
best: 98.8 (CAML [Laion-2b])
SOTA
Adaptive Cross-Modal Few-Shot Learning arXiv:1902.07104
Image ClassificationonMini-Imagenet 5-way (5-shot)
Accuracy· 2019-02-19
78.1
best: 98.72 (SgVA-CLIP)
Adaptive Cross-Modal Few-Shot Learning arXiv:1902.07104
Few-Shot Image ClassificationonMini-Imagenet 5-way (5-shot)
Accuracy· 2019-02-19
78.1
best: 98.72 (SgVA-CLIP)
Adaptive Cross-Modal Few-Shot Learning arXiv:1902.07104