[1]ZHAO Yafeng,YU Jichao,SUN Qian,et al.Unsupervised framework for Amur tiger re-identification with Cluster Contrast and ViT[J].CAAI Transactions on Intelligent Systems,2026,21(2):435-443.[doi:10.11992/tis.202507012]
Copy

Unsupervised framework for Amur tiger re-identification with Cluster Contrast and ViT

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 2 Page number: 435-443 Column: 学术论文—机器感知与模式识别 Public date: 2026-05-16
Title:
Unsupervised framework for Amur tiger re-identification with Cluster Contrast and ViT
Author(s):
ZHAO Yafeng1;  YU Jichao1;  SUN Qian2;  KANG Jialu1;  WANG Zicheng1
1. College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China;
2. College of Information And Communication Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
Amur tiger; re-identification; deep learning; unsupervised learning; ATRW dataset; Cluster Contrast mechanism; vision Transformer; coordinate attention
CLC:
TP391.4
DOI:
10.11992/tis.202507012
Abstract:
To address challenges like difficult annotation of wild data and sample imbalance in Amur tiger individual re-identification, a collaborative framework based on the ATRW dataset was developed for unsupervised re-identification. The ViT self-attention mechanism was used to capture global long-distance dependent features of stripes. Combined with the coordinate attention mechanism, it enhanced spatial position analysis of stripes, making up for missing feature correlations caused by the locality of traditional CNNs. The Cluster Contrast mechanism was introduced to build a cluster-level memory dictionary. Momentum update balanced feature optimization rates of individuals with different sample sizes, alleviating feature biases from sample imbalance in unsupervised learning. Experiments show the model achieves an mAP of86.4% on the ATRW (r+i) dataset, outperforming the original feature extraction models (ViT and Resnet50_ibn). It exhibits good generalization ability across different data distributions and data volumes, and is suitable for the demand of collaborative monitoring with visible light/infrared multi-devices in the wild. The method proposed in this study provides a technical solution with both accuracy and robustness for Amur tiger individual identification.
References:
[1] 国家林业和草原局, 农业农村部. 《国家重点保护野生动物名录》(2021年2月1日修订)[J]. 野生动物学报, 2021, 42(2): 605-640 National Forestry and Grassland Bureau, Ministry of Agriculture and Rural Affairs. List of national key protected wild animals (revised on February 1, 2021)[J]. Chinese journal of wildlife, 2021, 42(2): 605-640
[2] QI Jinzhe, HOLYOAK M, NING Yao, et al. Ecological thresholds and large carnivores conservation: implications for the Amur tiger and leopard in China[J]. Global ecology and conservation, 2020, 21: e00837
[3] 王凤昆, 李艳, 姜广顺. 中俄东北虎自然保护地建设进展[J]. 自然保护地, 2024, 4(4): 38-52 WANG Fengkun, LI Yan, JIANG Guangshun. Progress in the construction of Sino-Russia Amur Tiger(Panthera Tigris altaica) Protected Areas[J]. Natural protected areas, 2024, 4(4): 38-52
[4] 吴峰, 温佩颖, 唐志珍, 等. 东北虎豹国家公园植物DNA微条形码数据库[J]. 北京师范大学学报(自然科学版), 2023, 59(4): 623-628 WU Feng, WEN Peiying, TANG Zhizhen, et al. A DNA mini-barcode reference library for environmental DNA study in the Northeast Tiger and Leopard National Park[J]. Journal of Beijing Normal University (natural science edition), 2023, 59(4): 623-628
[5] 韦怡, 姜广顺. 虎豹及有蹄类猎物种群数量监测方法概述[J]. 生物多样性, 2022, 30(9): 129-146 WEI Yi, JIANG Guangshun. Overview of monitoring methods for tigers, leopards and ungulate prey[J]. Biodiversity science, 2022, 30(9): 129-146
[6] HE Fengping, LIU Dan, ZHANG Le, et al. Metagenomic analysis of captive Amur tiger faecal microbiome[J]. BMC veterinary research, 2018, 14(1): 379
[7] KERLEY L L. Using dogs for tiger conservation and research[J]. Integrative zoology, 2010, 5(4): 390-396
[8] TUIA D, KELLENBERGER B, BEERY S, et al. Perspectives in machine learning for wildlife conservation[J]. Nature communications, 2022, 13: 792
[9] YE Mang, CHEN Shuoyi, LI Chenyue, et al. Transformer for object re-identification: a survey[J]. International journal of computer vision, 2025, 133(5): 2410-2440
[10] YE Mang, SHEN Jianbing, LIN Gaojie, et al. Deep learning for person re-identification: a survey and outlook[J]. IEEE transactions on pattern analysis and machine intelligence, 2022, 44(6): 2872-2893
[11] 张国印, 王传博, 高伟. 抗遮挡的行人多目标跟踪算法[J]. 智能系统学报, 2024, 19(5): 1248-1256 ZHANG Guoyin, WANG Chuanbo, GAO Wei. Pedestrian multiobject tracking algorithm with anti-occlusion[J]. CAAI transactions on intelligent systems, 2024, 19(5): 1248-1256
[12] 王路遥, 王凤随, 闫涛, 等. 结合多尺度特征与混淆学习的跨模态行人重识别[J]. 智能系统学报, 2024, 19(4): 898-908 WANG Luyao, WANG Fengsui, YAN Tao, et al. Cross-modal person re-identification combining multi-scale features and confusion learning[J]. CAAI transactions on intelligent systems, 2024, 19(4): 898-908
[13] 赵玲, 周桂红, 任力生. 基于牛脸和躯干综合信息的奶牛个体识别研究[J]. 河北农业大学学报, 2024, 47(2): 112-118 ZHAO Ling, ZHOU Guihong, REN Lisheng. Individual identification of dairy cows based on comprehensive face and trunk information[J]. Journal of Agricultural University of Hebei, 2024, 47(2): 112-118
[14] FREYTAG A, RODNER E, SIMON M, et al. Chimpanzee faces in the wild: log-euclidean CNNs for predicting identities and attributes of Primates[C]//Pattern Recognition. Cham: Springer, 2016: 51–63.
[15] 顾佳音, 刘辉, 姜广顺. 东北虎(Panthera tigris altaica)个体识别技术研究进展[J]. 野生动物, 2013, 34(4): 229-237, 248 GU Jiayin, LIU Hui, JIANG Guangshun. A review of potential techniques for indentifying individual Amur tigers (Panthera tigris altaica)[J]. Chinese wildlife, 2013, 34(4): 229-237, 248
[16] GLOVER J D, SUDDERICK Z R, SHIH B B, et al. The developmental basis of fingerprint pattern formation and variation[J]. Cell, 2023, 186(5): 940–956. e20.
[17] SHI Chunmei, LIU Dan, CUI Yonglu, et al. Amur tiger stripes: individual identification based on deep convolutional neural network[J]. Integrative zoology, 2020, 15(6): 461-470
[18] 马光凯, 张静, 戴文锐, 等. 基于Transformer的东北虎体侧条纹个体识别[J]. 野生动物学报, 2024, 45(4): 734-743 MA Guangkai, ZHANG Jing, DAI Wenrui, et al. Body stripes individual identification of Amur tigers based on Transformer[J]. Chinese journal of wildlife, 2024, 45(4): 734-743
[19] CHMIELA S, SAUCEDA H E, MULLER K, et al. Addressing the elephant in the room: uncertainties in physical predictions from machine-learned force fields[J]. Bulletin of the American physical society, 2020.
[20] LI Shuyuan, LI Jianguo, TANG Hanlin, et al. ATRW: a benchmark for Amur tiger re-identification in the wild[C]//Proceedings of the 28th ACM International Conference on Multimedia. Seattle: ACM, 2020: 2590–2598.
[21] PARIKH A, T?CKSTR?M O, DAS D, et al. A decomposable attention model for natural language inference[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2016: 2249–2255.
[22] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16×16 words: transformers for image recognition at scale[EB/OL]. (2020-10-22)[2025-08-03]. https://arxiv.org/abs/2010.11929.
[23] HOU Qibin, ZHOU Daquan, FENG Jiashi. Coordinate attention for efficient mobile network design[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 13708–13717.
[24] DAI Zuozhuo, WANG Guangyuan, YUAN Weihao, et al. Cluster contrast forUnsupervised person re-identification[C]//Computer Vision–ACCV 2022. Cham: Springer, 2023: 319–337.
[25] WANG Xiaogang, DORETTO G, SEBASTIAN T, et al. Shape and appearance context modeling[C]//2007 IEEE 11th International Conference on Computer Vision. Rio de Janeiro: IEEE, 2007: 1–8.
[26] ZHENG Liang, SHEN Liyue, TIAN Lu, et al. Scalable person re-identification: a benchmark[C]//2015 IEEE International Conference on Computer Vision. Santiago: IEEE, 2016: 1116–1124.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems