Table 2.

Results for scene graph prediction tasks with n = 10 labeled examples per predicate, reported as recall @K. A state-of-the-art scene graph model trained on labels from our method outperforms those trained with labels generated by other baselines, like transfer learning.

		Scene Graph Detection			Scene Graph Classification			Predicate Classification
	Model	R@20	R@50	R@100	R@20	R@50	R@100	R@20	R@50	R@100
Baselines	BASELINE [n = 10]	0.00	0.00	0.00	0.04	0.04	0.04	3.17	5.30	6.61
	FREQ	9.01	11.01	11.64	11.10	11.08	10.92	20.98	20.98	20.80
	FREQ+OVERLAP	10.16	10.84	10.86	9.90	9.91	9.91	20.39	20.90	22.21
	TRANSFER LEARNING	11.99	14.40	16.48	17.10	17.91	18.16	39.69	41.65	42.37
	DECISION TREE [38]	11.11	12.58	13.23	14.02	14.51	14.57	31.75	33.02	33.35
	LABEL PROPAGATION [57]	6.48	6.74	6.83	9.67	9.91	9.97	24.28	25.17	25.41
Ablations	OURS (DEEP)	2.97	3.20	3.33	10.44	10.77	10.84	23.16	23.93	24.17
	OURS (SPAT.)	3.26	3.20	2.91	10.98	11.28	11.37	26.23	27.10	27.26
	OURS (CATEG.)	7.57	7.92	8.04	20.83	21.44	21.57	43.49	44.93	45.50
	OURS (CATEG. + SPAT. + DEEP)	7.33	7.70	7.79	17.03	17.35	17.39	38.90	39.87	40.02
	OURS (CATEG. + SPAT. + WORDVEC)	8.43	9.04	9.27	20.39	20.90	21.21	45.15	46.82	47.32
	OURS (MAJORITY VOTE)	16.86	18.31	18.57	18.96	19.57	19.66	44.18	45.99	46.63
	OURS (CATEG. + SPAT.)	17.67	18.69	19.28	20.91	21.34	21.44	45.49	47.04	47.53
	ORACLE [nORACLE = 108n]	24.42	29.67	30.15	30.15	30.89	31.09	69.23	71.40	72.15