Table A3.

Mean values of metrics achieved over 30 runs for vehicle-size classification, using different pairs of $(J,R)$ values. The value $R=-$ denotes the results when the MV-DTF layer was employed, whereas the other values correspond to the LRMV-DTF layer. If the rank R value is not provided, the entire row corresponds to the results of the multitask single-view model.

Method	Metric
	ACCw	F1w	GMw	MCCnw	BMnw
MT-SV (J = 8)	0.9483	0.9282	0.7878	0.8388	0.8087
MT-MV (J = 8, R = −)	0.9514	0.9286	0.8075	0.8494	0.8248
MT-MV (J = 8, R = 1)	0.9501	0.9279	0.8026	0.8455	0.8205
MT-MV (J = 8, R = 2)	0.9504	0.9285	0.8098	0.8476	0.8264
MT-MV (J = 8, R = 3)	0.9516	0.9288	0.8073	0.8498	0.8245
MT-SV (J = 10)	0.9493	0.9290	0.7909	0.8420	0.8112
MT-MV (J = 10, R = −)	0.9504	0.9276	0.8091	0.8471	0.8257
MT-MV (J = 10, R = 1)	0.9499	0.9273	0.8063	0.8456	0.8235
MT-MV (J = 10, R = 2)	0.9503	0.9282	0.8006	0.8490	0.8190
MT-MV (J = 10, R = 3)	0.9508	0.9284	0.8045	0.8476	0.8222
MT-SV (J = 12)	0.9483	0.9285	0.7874	0.8389	0.8084
MT-MV (J = 12, R = −)	0.9510	0.9283	0.8089	0.8487	0.8256
MT-MV (J = 12, R = 1)	0.9512	0.9288	0.8089	0.8492	0.8257
MT-MV (J = 12, R = 2)	0.9498	0.9272	0.8065	0.8455	0.8237
MT-MV (J = 12, R = 3)	0.9511	0.9279	0.7984	0.8469	0.8176

Values highlighted in red denote the worst values achieved on every group. Values highlighted in blue denote the best values achieved on every group.