Vision And Language Navigation On Vln

Metriken

error

length

oracle success

spl

success

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Vergleichstabelle

Modellname	error	length	oracle success	spl	success
Modell 1	2.99	686.86	0.99	0.01	0.74
Modell 2	3.97	13.75	0.71	0.58	0.64
Modell 3	5.45	10.65	0.6	0.48	0.52
Modell 4	3.3	9.85	0.77	0.68	0.7
Modell 5	4.32	13.43	0.69	0.55	0.61
Modell 6	4.49	12.22	0.67	0.54	0.59
Modell 7	4.96	12.51	0.61	0.48	0.54
Modell 8	3.61	14.6	0.77	0.59	0.7
Modell 9	4.57	10.99	0.65	0.5	0.57
vision-language-navigation-with-random	3.87	13.11	0.72	0.59	0.65
Modell 11	3.38	15.47	0.79	0.59	0.71
self-monitoring-navigation-agent-via	5.67	18.04	0.59	0.35	0.48
Modell 13	6.46	13.08	0.45	0.3	0.37
Modell 14	5.37	10.0	0.59	0.5	0.53
Modell 15	3.31	786.35	0.99	0.01	0.69
Modell 16	4.65	12.19	0.62	0.52	0.56
Modell 17	4.71	10.31	0.64	0.55	0.59
Modell 18	3.73	15.89	0.73	0.6	0.66
Modell 19	6.12	11.97	0.5	0.38	0.43
Modell 20	3.44	16.14	0.79	0.6	0.72
Modell 21	5.42	14.07	0.55	0.4	0.47
Modell 22	5.63	12.13	0.61	0.49	0.53
Modell 23	12.29	45.13	0.49	0.02	0.07
improving-vision-and-language-navigation-with	3.09	686.62	0.99	0.01	0.73
Modell 25	4.57	1214.94	0.96	0.01	0.56
Modell 26	5.5	14.42	0.57	0.41	0.49
Modell 27	3.77	11.89	0.72	0.63	0.66
Modell 28	7.85	8.13	0.27	0.18	0.2
Modell 29	4.61	13.16	0.65	0.5	0.57
Modell 30	4.21	9.48	0.67	0.59	0.6
Modell 31	4.45	13.0	0.67	0.53	0.58
Modell 32	4.27	12.13	0.67	0.56	0.61
Modell 33	4.52	10.21	0.64	0.56	0.59
Modell 34	6.01	10.47	0.53	0.4	0.45
Modell 35	5.35	11.74	0.64	0.5	0.54
neighbor-view-enhanced-model-for-vision-and	4.37	12.98	0.66	0.54	0.58
Modell 37	4.09	12.35	0.7	0.57	0.63
Modell 38	4.53	10.52	0.63	0.53	0.57
environment-agnostic-multitask-learning-for	6.03	13.35	0.56	0.4	0.45
Modell 40	3.78	15.9	0.71	0.59	0.65
Modell 41	5.3	10.51	0.61	0.51	0.54
Modell 42	7.47	10.92	0.34	0.21	0.26
Modell 43	5.14	13.05	0.6	0.45	0.51
Modell 44	5.3	10.4	0.61	0.5	0.53
Modell 45	4.26	12.73	0.67	0.55	0.61
Modell 46	4.48	373.09	0.97	0.02	0.61
Modell 47	4.75	10.29	0.61	0.52	0.55
Modell 48	4.18	10.22	0.67	0.58	0.62
Modell 49	3.05	1250.89	1.0	0.01	0.72
Modell 50	3.52	11.79	0.75	0.65	0.7
Modell 51	6.61	13.85	0.46	0.3	0.36
vision-language-navigation-with-self	3.69	10.43	0.75	0.65	0.68
Modell 53	9.79	9.89	0.18	0.12	0.13
Modell 54	3.65	14.73	0.76	0.59	0.69
Modell 55	4.02	10.66	0.72	0.55	0.61
Modell 56	3.13	15.87	0.81	0.62	0.73
transferable-representation-learning-in	5.49	10.27	0.56	0.45	0.48
Modell 58	3.99	12.62	0.71	0.58	0.63
Modell 59	5.17	10.58	0.63	0.52	0.56
Modell 60	6.55	9.81	0.45	0.31	0.35
Modell 61	5.29	11.39	0.65	0.53	0.56
Modell 62	4.59	12.34	0.65	0.53	0.57
Modell 63	4.09	12.35	0.7	0.57	0.63
Modell 64	4.53	13.07	0.66	0.53	0.59
Modell 65	3.55	14.39	0.76	0.6	0.7
Modell 66	3.82	12.71	0.72	0.6	0.65
Modell 67	4.34	21.03	0.71	0.43	0.6
Modell 68	4.9	12.96	0.62	0.5	0.55
Modell 69	3.07	176.22	0.94	0.05	0.71
tactical-rewind-self-correction-via	5.14	22.08	0.64	0.41	0.54
Modell 71	3.26	10.21	0.77	0.67	0.71
learning-to-navigate-unseen-environments-back	5.23	11.66	0.59	0.47	0.51
Modell 73	5.32	10.9	0.63	0.51	0.55
Modell 74	6.89	8.32	0.41	0.32	0.34
Modell 75	3.55	625.27	0.99	0.01	0.74
the-regretful-agent-heuristic-aided	5.69	13.69	0.56	0.4	0.48
Modell 77	4.09	12.35	0.7	0.57	0.63
Modell 78	3.86	12.31	0.71	0.59	0.64
Modell 79	3.94	12.3	0.71	0.59	0.64
Modell 80	3.77	16.41	0.71	0.59	0.66
Modell 81	5.8	16.73	0.57	0.36	0.47
Modell 82	8.82	9.56	0.24	0.16	0.18
Modell 83	7.63	22.14	0.61	0.2	0.3
Modell 84	7.53	9.15	0.32	0.23	0.25
Modell 85	3.41	12.07	0.76	0.6	0.67
Modell 86	7.2	15.9	0.4	0.23	0.29
Modell 87	5.64	10.42	0.53	0.43	0.47
airbert-in-domain-pretraining-for-vision-and	2.58	686.54	0.99	0.01	0.78
Modell 89	6.95	10.44	0.43	0.31	0.36
Modell 90	3.68	14.78	0.72	0.6	0.66
Modell 91	4.27	16.94	0.72	0.49	0.62
Modell 92	3.97	9.79	0.7	0.61	0.64
Modell 93	4.03	357.62	0.96	0.02	0.63
Modell 94	8.56	9.48	0.32	0.22	0.24
learning-to-navigate-unseen-environments-back	3.26	686.82	0.99	0.01	0.69
Modell 96	3.67	13.07	0.73	0.6	0.66
Modell 97	5.24	14.31	0.64	0.46	0.54
Modell 98	4.0	16.44	0.69	0.58	0.63
Modell 99	6.17	10.17	0.47	0.36	0.4
Modell 100	4.95	10.71	0.65	0.55	0.58
Modell 101	3.9	12.84	0.7	0.58	0.64
Modell 102	4.02	13.57	0.71	0.57	0.63
tactical-rewind-self-correction-via	4.29	196.53	0.9	0.03	0.61
Modell 104	4.22	13.2	0.69	0.56	0.61
Modell 105	1.61	11.85	0.9	0.76	0.86
Modell 106	3.59	11.9	0.73	0.64	0.68
Modell 107	4.3	15.83	0.68	0.55	0.61
Modell 108	3.86	12.75	0.72	0.6	0.66
Modell 109	4.89	10.18	0.63	0.53	0.56
Modell 110	4.84	15.74	0.69	0.48	0.56
Modell 111	3.76	10.24	0.71	0.62	0.65
Modell 112	3.0	10.2	0.8	0.69	0.73
Modell 113	4.0	11.91	0.7	0.6	0.65
Modell 114	3.78	15.9	0.71	0.59	0.65
Modell 115	7.99	8.26	0.26	0.18	0.2
Modell 116	3.85	19.62	0.74	0.55	0.66
Modell 117	3.35	14.43	0.8	0.62	0.73
Modell 118	3.74	13.55	0.73	0.61	0.67
Modell 119	5.56	16.03	0.57	0.35	0.47
Modell 120	5.45	11.15	0.57	0.47	0.51
Modell 121	4.87	1257.38	0.96	0.01	0.53
Modell 122	6.67	10.83	0.44	0.33	0.36
Modell 123	9.8	9.91	0.19	0.12	0.14
Modell 124	6.68	15.75	0.43	0.25	0.33
Modell 125	7.03	15.1	0.4	0.25	0.31
Modell 126	9.93	0.0	0.0	0.0	0.0
Modell 127	3.21	690.61	0.99	0.01	0.7
Modell 128	3.96	12.77	0.7	0.57	0.63
Modell 129	2.5	686.45	0.99	0.01	0.79
Modell 130	4.04	13.02	0.7	0.58	0.63
Modell 131	4.16	12.51	0.69	0.58	0.63
vision-language-navigation-with-self	3.24	40.85	0.81	0.21	0.71
Modell 133	4.57	20.39	0.7	0.46	0.61
Modell 134	3.33	15.24	0.78	0.61	0.72
Modell 135	3.86	12.22	0.71	0.61	0.65
Modell 136	3.93	12.27	0.72	0.6	0.65
Modell 137	5.11	10.06	0.62	0.52	0.54
Modell 138	3.88	12.6	0.71	0.59	0.64
Modell 139	5.7	11.47	0.57	0.47	0.51
Modell 140	3.57	14.25	0.77	0.61	0.71
Modell 141	4.07	12.3	0.7	0.58	0.63
Modell 142	3.24	11.86	0.76	0.62	0.69
Modell 143	4.26	14.29	0.66	0.55	0.59
Modell 144	5.34	15.02	0.61	0.42	0.53
Modell 145	3.98	13.54	0.7	0.58	0.63