Multi-Information Flow CNN and Attribute-Aided Reranking for Person Reidentification

Table 2

Comparison of various methods with the proposed methods on three datasets (%).

Methods	VIPeR			CUHK01			Market1501
	Rank 1	Rank 5	Rank 10	Rank 1	Rank 5	Rank 10	Rank 1	Rank 5	Rank 10	mAP
DNS [32]	42.28	71.46	82.94	64.98	84.96	89.92	67.96	—	—	41.89
DLDA [33]	44.11	72.59	81.66	67.12	89.45	91.68	48.15	—	—	29.94
FT-JSTL + DGD [11]	38.6	—	—	66.60	—	—	—	—	—	—
PDC [34]	51.27	74.05	84.18	—	—	—	84.14	92.73	94.92	63.41
K-means-CNN [35]	46.50	69.30	80.70	53.50	82.50	91.20	—	—	—	—
Spindle [9]	53.80	74.10	83.20	—	—	—	76.90	91.50	94.60	—
PersonNet [36]	—	—	—	71.14	90.07	95.00	37.21	—	—	18.57
CSBT [37]	36.60	66.20	88.30	51.20	76.30	91.80	42.90	—	—	20.30
SDH-CNN [38]	—	—	—	—	—	—	58.12	68.50	80.82	48.20
M3TCP [21]	—	—	—	53.70	84.30	91.00	—	—	—	—
DM^3 [39]	42.70	74.30	85.10	49.70	77.30	86.10	75.80	89.10	92.40	53.20
MiF	40.82	68.35	81.01	66.87	85.39	89.51	78.92	86.46	89.28	66.00
MiF + PARN	46.84	74.68	83.23	71.81	90.33	91.77	79.39	88.95	91.36	66.46

“MiF” represents the MiF-CNN method, and “MiF + PARN” represents the MiF-CNN with the attribute-aided reranking method.