Camera-aware unsupervised person re-identification method guided by pseudo-label refinement

Siyu Cheng; Ying Chen

doi:10.12086/oee.2023.230239

Journals >Opto-Electronic Engineering >Volume 50 >Issue 12 >Page 230239-1 > Article

Opto-Electronic Engineering
Vol. 50, Issue 12, 230239-1 (2023)

Camera-aware unsupervised person re-identification method guided by pseudo-label refinement

Siyu Cheng and Ying Chen^*

Author Affiliations

Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China

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DOI: 10.12086/oee.2023.230239 Cite this Article

Siyu Cheng, Ying Chen. Camera-aware unsupervised person re-identification method guided by pseudo-label refinement[J]. Opto-Electronic Engineering, 2023, 50(12): 230239-1 Copy Citation Text

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Fig. 1. The overall framework of our method

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Fig. 2. Neighborhood pseudo label refinement module

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Fig. 3. Schematic diagram of camera-aware guided by refined pseudo-labels. (a) Original intra-camera contrast; (b) Corrected intra-camera contrast; (c) Original inter-camera contrast; (d) Corrected inter-camera contrast

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Fig. 4. Comparison of Top-10 ranking lists between on Market-1501 dataset among different methods. (a) Baseline method; (b) CAP^[9] method; (c) PPLR^[15] method; (d) Our method

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Fig. 5. Feature T-SNE visualization results of different methods on Market-1501 dataset. (a) Baseline method; (b) CAP^[9] method; (c) PPLR^[15] method; (d) Our method

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Fig. 6. The impact of each hyperparameter to our model on Market-1501. (a)

α

; (b)

k

; (c)

τ_{intra}

; (d)

τ_{inter}

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Methods		Market-1501		MSMT17		Personx
Methods		mAP/%	Rank-1/%	mAP/%	Rank-1/%	mAP/%	Rank-1/%
Unsupervised domain adaptation (UDA)
ECN^[2]	CVPR’19	43.0	75.1	10.2	30.2	-	-
SPCL^[3]	NeurIPS’20	77.5	89.7	26.8	53.7	78.5	91.1
MEB-Net^[13]	ECCV’20	76.0	89.9	-	-	-	-
MMT^[12]	CVPR’21	71.2	87.7	23.5	50.0	78.9	90.6
GLT^[31]	CVPR’21	79.5	92.2	27.7	59.5	-	-
MCL^[32]	JBUAA’22	80.6	93.2	28.5	58.5	-	-
CACHE^[33]	TCSVT’22	83.1	93.4	31.3	58.0	-	-
CIFL^[20]	TMM’22	83.3	93.9	39.0	70.5	-	-
MCRN^[16]	AAAI’22	83.8	93.8	35.7	67.5	-	-
IICM^[34]	JCRD’23	74.9	89.0	27.2	52.3	-	-
NPSS^[21]	TIFS’23	84.6	94.1	38.9	69.4	-	-
CaCL^[11]	ICCV’23	84.7	93.8	40.3	70.0	-	-
Unsupervised learning (USL)
SPCL^[3]	NeurIPS’20	73.1	88.1	19.1	42.3	72.3	88.1
MetaCam^[7]	CVPR’21	61.7	83.9	15.5	35.2	-	-
IICS^[8]	CVPR’21	72.9	89.5	26.9	56.4	-	-
RLCC^[14]	CVPR’21	77.7	90.8	27.9	56.5	-	-
CAP^[9]	AAAI’21	79.2	91.4	36.9	67.4	-	-
ICE^[17]	ICCV’21	82.3	93.8	38.9	70.2	-	-
CACHE^[33]	TCSVT’22	81.0	92.0	31.8	58.2	-	-
CIFL^[20]	TMM’22	82.4	93.9	38.8	70.1	-	-
GRACL^[35]	TCSVT’22	83.7	93.2	34.6	64.0	87.9	95.3
PPLR^[15]	CVPR’22	84.4	94.3	42.2	73.3	-	-
CA-UReID^[10]	ICME’22	84.5	94.1	-	-	-
NPSS^[21]	TIFS’23	82.3	94.0	36.7	68.8	-	-
LRMGFS^[36]	JEMI’23	83.3	93.3	27.4	58.4	-	-
PLRIS^[22]	ICIP’23	83.2	93.1	43.3	71.5	-	-
AdaMG^[37]	TCSVT’23	84.6	93.9	38.0	66.3	87.6	95.0
LP^[18]	TIP’23	85.8	94.5	39.5	67.9	-	-
DCCT^[19]	TCSVT’’23	86.3	94.4	41.8	68.7	87.6	95.0
CC^[4]	CoRR’21	82.1	92.3	27.6	56.0	84.7	94.4
Ours	-	85.2	94.4	44.3	74.1	88.7	95.9

Table 1. The comparison between the our method and the latest methods

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	${\tilde{L}}_{ce}$	${\tilde{L}}_{intra}$	${\tilde{L}}_{inter}$	$Market - 1501$				$Personx$
	${\tilde{L}}_{ce}$	${\tilde{L}}_{intra}$	${\tilde{L}}_{inter}$	mAP/%	Rank-1/%	Rank-5/%	Rank-10/%	mAP/%	Rank-1/%	Rank-5/%	Rank-10/%
M1(CC^[4])	-	-	-	82.1	92.3	96.7	97.9	84.7	94.4	98.3	99.3
M2	-	√	-	82.9	93.2	97.3	98.2	87.9	95.7	98.8	99.5
M3	-	-	√	83.9	93.6	97.5	98.3	87.3	95.9	98.8	99.4
M4	-	√	√	84.1	93.6	97.6	98.5	88.5	95.8	98.9	99.6
M5	√	-	-	84.7	94.2	97.9	98.7	87.5	95.5	98.9	99.5
M6(Ours)	√	√	√	85.2	94.4	98.1	98.6	88.7	95.9	99.2	99.7

Table 2. Results of ablation studies on Market-1501

Siyu Cheng, Ying Chen. Camera-aware unsupervised person re-identification method guided by pseudo-label refinement[J]. Opto-Electronic Engineering, 2023, 50(12): 230239-1

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