Cross-Modal Person Re-Identification Based on Channel Reorganization and Attention Mechanism

Setting	all-search				indoor-search
Method	r=1	r =10	r =20	mAP /%	r=1	r =10	r =20	mAP /%
HOG^［14］	2.76	18.30	31.90	4.24	3.22	24.70	44.50	7.25
BDTR^［33］	17.01	55.43	71.96	19.66
HSME^［23］	20.68	32.74	77.95	23.12
D2RL^［22］	28.90	70.60	82.40	29.20
MAC^［34］	33.26	79.04	90.09	36.22	36.43	62.36	71.63	37.03
MSR^［35］	37.35	83.40	93.34	38.11	39.64	89.29	97.66	50.88
AlignGAN^［11］	42.40	85.00	93.70	40.70	45.90	87.60	94.40	54.30
cmGAN^［26］	26.97	67.51	80.56	31.49	31.63	77.23	89.18	42.19
HPILN^［36］	41.36	84.78	94.31	42.95	45.77	91.82	98.46	56.52
LZM^［37］	45.00	89.06	95.77	45.94	49.66	92.47	97.15	59.81
AGW^［1］	47.50	84.39	92.14	47.65	54.17	91.14	95.98	62.97
X-modal^［38］	49.92	89.79	95.96	50.73
DDAG^［12］	54.75	90.39	95.81	53.02	61.02	94.06	98.41	67.98
Proposed method	59.23	91.83	96.63	56.55	63.22	94.39	98.20	69.54

Table 2. Performance comparison of DCA-Net and current state-of-the-art methods on RegDB dataset

View table

Table 2. Performance comparison of DCA-Net and current state-of-the-art methods on RegDB dataset

Setting	Visible to thermal				Thermal to visible
Method	r=1	r =10	r =20	mAP /%	r=1	r =10	r =20	mAP /%
HCML^［24］	24.44	47.53	56.78	20.08	21.70	45.02	55.58	22.24
BDTR^［33］	33.56	58.61	67.43	32.76	32.92	58.46	68.43	31.96
D2RL^［22］	43.40	66.10	76.30	44.10
HSME^［23］	50.85	73.36	81.66	47.00	50.15	72.40	81.07	46.16
MAC^［39］	36.43	62.36	71.63	37.03	36.20	61.68	70.99	36.63
MSR^［35］	48.43	70.32	79.95	48.67
EDFL^［40］	52.58	72.10	81.47	52.98	51.89	72.09	81.04	52.13
AlignGAN^［11］	57.90			53.60	56.30			53.40
LZM^［37］	57.03	76.10	84.34	58.06
X-modal^［38］	62.21	83.13	91.72	60.18
AGW^［1］	70.05	86.21	91.55	66.37	70.49	87.12	91.84	65.90
DDAG^［12］	69.34	86.19	91.49	63.46	68.06	85.15	90.31	61.80
Proposed method	78.16	91.75	94.66	71.18	77.62	91.60	94.47	70.56

Table 3. Experimental study of ablation on SYSU-MM01 dataset
View table
Table 3. Experimental study of ablation on SYSU-MM01 dataset
Baseline CGSA AFA ICGR SYSU-MM01
Rank-1 mAP
√ 48.18 47.64
√ √ 50.75 49.73
√ √ √ 57.73 54.42
√ √ √ √ 59.23 56.55

Table 4. Experimental results of ICGR inserted different position under SYSU-MM01 dataset
View table
Table 4. Experimental results of ICGR inserted different position under SYSU-MM01 dataset
Baseline Conv2 Conv3 Conv4 SYSU-MM01
Rank-1 mAP
√ 57.73 54.42
√ √ 58.27 54.73
√ √ √ 59.19 56.19
√ √ √ √ 59.23 56.55

Table 5. Effect of different loss functions on model performance
View table
Table 5. Effect of different loss functions on model performance
Loss function SYSU-MM01 RegDB
Rank-1 mAP Rank-1 mAP
$L_{i d}$ 56.89 54.75 70.63 62.03
$L_{t r i} + L_{i d}$ 57.73 54.42 72.18 66.03
$L_{e} + L_{t r i} + L_{i d}$ 59.23 56.55 78.16 71.18

Table 6. Model complexity analysis
View table
Table 6. Model complexity analysis
Model Model memory /MB Training time /s
AGW 273 234.33
DDAG 362.48 299.82
DCA-Net 364 237.07

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Dongdong Huo, Haishun Du. Cross-Modal Person Re-Identification Based on Channel Reorganization and Attention Mechanism[J]. Laser & Optoelectronics Progress, 2023, 60(14): 1410007

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Paper Information

Category: Image Processing

Received: Jun. 15, 2022

Accepted: Aug. 29, 2022

Published Online: Jul. 17, 2023

The Author Email: Du Haishun (jddhs@henu.edu.cn)

DOI:10.3788/LOP221850

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Performance comparison of DCA-Net and current state-of-the-art methods on the SYSU-MM01 dataset

Table 1. Performance comparison of DCA-Net and current state-of-the-art methods on the SYSU-MM01 dataset

Table 2. Performance comparison of DCA-Net and current state-of-the-art methods on RegDB dataset

Table 2. Performance comparison of DCA-Net and current state-of-the-art methods on RegDB dataset

Table 3. Experimental study of ablation on SYSU-MM01 dataset

Table 3. Experimental study of ablation on SYSU-MM01 dataset

Table 4. Experimental results of ICGR inserted different position under SYSU-MM01 dataset

Table 4. Experimental results of ICGR inserted different position under SYSU-MM01 dataset

Table 5. Effect of different loss functions on model performance

Table 5. Effect of different loss functions on model performance

Table 6. Model complexity analysis

Table 6. Model complexity analysis