Metric Learning On Cars196

Métriques

R@1

Résultats

Résultats de performance de divers modèles sur ce benchmark

Tableau comparatif

Nom du modèle	R@1
dissecting-the-impact-of-different-loss	86.5
hyperbolic-vision-transformers-combining	89.2
hyperbolic-vision-transformers-combining	86.5
proxy-anchor-loss-for-deep-metric-learning	88.3
das-densely-anchored-sampling-for-deep-metric	88.34
the-group-loss-for-deep-metric-learning	85.6
attention-based-ensemble-for-deep-metric	85.2
pads-policy-adapted-sampling-for-visual	83.5
das-densely-anchored-sampling-for-deep-metric	87.8
hard-negative-examples-are-hard-but-useful	73.2
towards-interpretable-deep-metric-learning	87.01
learning-intra-batch-connections-for-deep	88.1
learning-semantic-proxies-from-visual-prompts	91.2
improved-embeddings-with-easy-positive	82.7
calibrated-neighborhood-aware-confidence	91.5
sampling-matters-in-deep-embedding-learning	79.6
recall-k-surrogate-loss-with-large-batches	88.3
metric-learning-with-horde-high-order	88.0
it-takes-two-to-tango-mixup-for-deep-metric	89.6
recall-k-surrogate-loss-with-large-batches	89.5
hardness-aware-deep-metric-learning	79.1
s2sd-simultaneous-similarity-based-self	89.5
unicom-universal-and-compact-representation	98.2
metric-learning-cross-entropy-vs-pairwise	89.3
diva-diverse-visual-feature-aggregation	87.6
non-isotropy-regularization-for-proxy-based	89.1
hyperbolic-vision-transformers-combining	92.8
integrating-language-guidance-into-vision	90.2
proxynca-revisiting-and-revitalizing-proxy	86.5
attributable-visual-similarity-learning	91.5
softtriple-loss-deep-metric-learning-without	84.5
center-contrastive-loss-for-metric-learning	91.02
circle-loss-a-unified-perspective-of-pair	83.4
improved-embeddings-with-easy-positive	75.5
learning-intra-batch-connections-for-deep	91.5
mic-mining-interclass-characteristics-for	82.6