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

Dongdong Huo; Haishun Du

doi:10.3788/LOP221850

Journals >Laser & Optoelectronics Progress >Volume 60 >Issue 14 >Page 1410007 > Article

Laser & Optoelectronics Progress
Vol. 60, Issue 14, 1410007 (2023)

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

Dongdong Huo and Haishun Du^*

Author Affiliations

School of Artificial Intelligence, Henan University, Zhengzhou 450046, Henan, China

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DOI: 10.3788/LOP221850 Cite this Article Set citation alerts

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

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Fig. 1. Overall framework of DCA-Net

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Fig. 2. Intra-modal feature channel grouping and reorganization module

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Fig. 3. Aggregated feature attention mechanism module

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Fig. 4. Channel attention module

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Fig. 5. Spatial attention module

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Fig. 6. Position attention module

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Fig. 7. Comparison of visible images and infrared images

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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 1. Performance comparison of DCA-Net and current state-of-the-art methods on the SYSU-MM01 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 2. Performance comparison of DCA-Net and current state-of-the-art methods on RegDB dataset

Baseline	CGSA	AFA	ICGR	SYSU-MM01
Baseline	CGSA	AFA	ICGR	Rank-1	mAP
√				48.18	47.64
√	√			50.75	49.73
√	√	√		57.73	54.42
√	√	√	√	59.23	56.55

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

Baseline	Conv2	Conv3	Conv4	SYSU-MM01
Baseline	Conv2	Conv3	Conv4	Rank-1	mAP
√				57.73	54.42
√	√			58.27	54.73
√	√	√		59.19	56.19
√	√	√	√	59.23	56.55

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

Loss function	SYSU-MM01		RegDB
Loss function	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 5. Effect of different loss functions on model performance

Model	Model memory /MB	Training time /s
AGW	273	234.33
DDAG	362.48	299.82
DCA-Net	364	237.07

Table 6. Model complexity analysis

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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