Table 4.

Results on VLCS-MLT.

Algorithm	F1-Score (by domain)		Accuracy (by domain)		Accuracy (by shot)
	Average	Worst	Average	Worst	Many	Medium	Few	Zero
IRM	0.758	0.512	0.765	0.527	0.842	0.760	− 1	0.395
DANN	0.710	0.413	0.726	0.453	0.814	0.713	− 1	0.307
CDANN	0.726	0.477	0.743	0.493	0.828	0.740	− 1	0.302
CORAL	0.779	0.559	0.788	0.568	0.867	0.787	− 1	0.390
MMD	0.778	0.554	0.788	0.554	0.861	0.813	− 1	0.369
Focal	0.785	0.589	0.794	0.601	0.867	0.813	− 1	0.390
CBLoss	0.785	0.557	0.794	0.568	0.864	0.807	− 1	0.414
LDAM	0.774	0.558	0.781	0.554	0.861	0.787	− 1	0.362
Bsoftmax	0.818	0.647	0.824	0.655	0.867	0.873	− 1	0.524
CRT (2-stage training)	0.793	0.567	0.803	0.574	0.881	0.800	− 1	0.407
BoDA	0.779	0.578	0.789	0.595	0.869	0.800	− 1	0.362
BoDA (2-stage training)	0.804	0.629	0.812	0.635	0.881	0.833	− 1	0.426
ERM	0.775	0.530	0.785	0.547	0.869	0.760	− 1	0.407
GroupDRO	0.781	0.549	0.790	0.568	0.867	0.787	− 1	0.419
Mixup	0.769	0.522	0.778	0.520	0.861	0.780	− 1	0.350
SagNet	0.766	0.542	0.771	0.547	0.861	0.747	− 1	0.374
MLDG	0.777	0.524	0.782	0.520	0.864	0.753	− 1	0.419
MTL	0.776	0.543	0.782	0.554	0.861	0.787	− 1	0.383
Fish	0.774	0.549	0.782	0.554	0.872	0.767	− 1	0.362
BRL (ours)	0.813	0.641	0.809	0.635	0.847	0.840	− 1	0.581
BRL (2-stage training)	0.807	0.619	0.803	0.615	0.850	0.820	− 1	0.564