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Vision And Language Navigation On Vln

Métriques

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Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
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Paper TitleRepository
Global Normalization2.99686.860.990.010.74--
DDL3.9713.750.710.580.64--
SYSU-ISE5.4510.650.60.480.52--
Active Exploration (Pre-explore)3.39.850.770.680.7--
zq4.3213.430.690.550.61--
trysth4.4912.220.670.540.59--
DCMT4.9612.510.610.480.54--
lxyict3.6114.60.770.590.7--
test-sf4.5710.990.650.50.57--
single-run3.8713.110.720.590.65Vision-Language Navigation with Random Environmental Mixup-
HOC3.3815.470.790.590.71--
Self-Monitoring Navigation Agent (no beam search; Progress Inference)5.6718.040.590.350.48Self-Monitoring Navigation Agent via Auxiliary Progress Estimation-
Khanh Nguyen6.4613.080.450.30.37--
single-run5.3710.00.590.50.53--
ADad3.31786.350.990.010.69--
reward-vln4.6512.190.620.520.56--
SEA features + AuxRN (single-run)4.7110.310.640.550.59--
Single-Run, No Pre-Explore3.7315.890.730.60.66--
Reinforced Cross-Modal Matching (single trajectory; NO beam search)6.1211.970.50.380.43--
Lily3.4416.140.790.60.72--
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