The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
ATRW: A Benchmark for Amur Tiger Re-identification in the Wild
Open accessAbstract
Monitoring the population and movements of endangered species is an important task to wildlife conversation. Traditional tagging methods do not scale to large populations, while applying computer vision methods to camera sensor data requires re-identification (re-ID) algorithms to obtain accurate counts and moving trajectory of wildlife. However, existing re-ID methods are largely targeted at persons and cars, which have limited pose variations and constrained capture environments. This paper tries to fill the gap by introducing a novel large-scale dataset, the Amur Tiger Re-identification in the Wild (ATRW) dataset. ATRW contains over 8,000 video clips from 92 Amur tigers, with bounding box, pose keypoint, and tiger identity annotations. In contrast to typical re-ID datasets, the tigers are captured in a diverse set of unconstrained poses and lighting conditions. We demonstrate with a set of baseline algorithms that ATRW is a challenging dataset for re-ID. Lastly, we propose a novel method for tiger re-identification, which introduces precise pose parts modeling in deep neural networks to handle large pose variation of tigers, and reaches notable performance improvement over existing re-ID methods. The ATRW dataset is public available at https://cvwc2019.github.io/challenge.html
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“for example, accuracy exceeded 90% in a study of 92 tigers across 8000 video clips103.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [103]
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
(2016) · Cited by 31,590
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00474 · Cited by 28,755
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015
YOLOv3: An Incremental Improvement
Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267
Squeeze-and-Excitation Networks
Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2913372 · Cited by 12,157
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
Flagged first, then by citations
Research works printed but not linked: 1
- Liang Zheng Liyue Shen Lu Tian Shengjin Wang Jingdong Wang and Qi Tian. 2015b. Scalable Person Re-identification: A Benchmark. In ICCV. Liang Zheng Liyue Shen Lu Tian Shengjin Wang Jingdong Wang and Qi Tian. 2015b. Scalable Person Re-identification: A Benchmark. In ICCV. · DOI
Cited by
Sorted by citations
Items 101–128 of 128
Pose‐Guided Re‐Identification of Amur Tigers Under Wild Environmental Constraints
Wang, T., Ma, B., Zhao, X., Mou, C., & Fan, J. (2025) · IET Image Processing · doi:10.1049/ipr2.70160 · Cited by 1
Cross-species animal pose estimation via feature map orthogonal decomposition decoder
Wu, X., Wang, Y., Wang, L., & Huang, J. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112749 · Cited by 1
Exploiting Class-agnostic Visual Prior for Few-shot Keypoint Detection
Lu, C., Zhu, H., & Koniusz, P. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-025-02671-5 · Cited by 1
Schmidt, J. E., Mäkinen, O. E., Nielsen, S. G., Johansen, A. S., Nasrollahi, K., & Moeslund, T. B. (2022) · International Conference on Machine Vision · doi:10.1117/12.2622634
Learning from limited annotated data for re-identification problem
Moskvyak, O. (2021) · Queensland University of Technology · doi:10.5204/thesis.eprints.226866
OpenApePose: a database of annotated ape photographs for pose estimationPreprint
Desai, N. P., Bala, P., Richardson, R., Raper, J., Zimmermann, J., & Hayden, B. Y. (2023) · eLife · doi:10.7554/elife.86873.2 · Cited by 1
eLife assessment: OpenApePose, a database of annotated ape photographs for pose estimation
Kalan, A. K. (2023) · eLife · doi:10.7554/elife.86873.3.sa0
Author Response: OpenApePose: a database of annotated ape photographs for pose estimation
Desai, N. P., Bala, P., Richardson, R., Raper, J., Zimmermann, J., & Hayden, B. Y. (2023) · eLife · doi:10.7554/elife.86873.1.sa3
Feature-Aware Noise Contrastive Learning For Unsupervised Red Panda Re-Identification
Zhang, J., Zhao, Q., & Li, S. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651256
Self-citationSW-YOLO: Wildlife Object Detection Based on Segment Anything and YOLO Models
Yang, Q., Liu, G., Zhao, X., Ma, B., & Mou, C. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-1233-1_20
Sivaraman, A. R., Chandrashekar, C., Sivaraman, A. K., Battina, M. R., Ajmi, A. I. Q. A., & Shakthi, S. (2025) · International Conference on Business Analytics for Technology and Security (ICBATS) · doi:10.1109/icbats66542.2025.11258221
Making Animal Re-Identification Accessible: A Web-Based Giraffe ID System for Zoos
Don, N. L. S., Rogers, M., Zhao, J., Xue, B., & Zhang, M. (2026) · Information · doi:10.3390/info17030266
Sun, Z., Dong, J., Zhou, X., Zhou, H., & Fan, H. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132015
Re-identification and cross-domain adaptability
Dubourvieux, F. (2022) · theses.fr (ABES) · doi:10.70675/0efb5138zdb71z4a53z9b82z85deb87ea62c
Schall, M., Knöfel, F., König, N. E., Kubeler, J. J., von Klinski, M., Linnemann, J. W., Liu, X., Schlegelmilch, I. J., Woyciniuk, O., Schild, A., et al. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00807
Rafandi, R., Nabilla, A., Siregar, D. A., & Hariyanto, E. (2026) · SinkrOn · doi:10.33395/sinkron.v10i2.16146
MambaReID: A Resource-Efficient Computer Vision Approach for Generalized Animal Re-Identification
Rahman, S. M. S., Niamatullah, M., Baishnab, S., Saad, M. A.-A. A., Rayhan, S., Chakrabarty, A., & Aziz, A. (2025) · IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things (RAAICON) · doi:10.1109/raaicon69033.2025.11502132
GKDT: General Keypoint Detection Transformer
Lu, C., Chen, Y., Gui, H., Wang, R., Yang, J., Yang, H., van den Hengel, A., & Jia, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-36846-1_11
Spotted: Location-informed Reidentification of Hyenas and Leopards in Camera Trap SurveysPreprint
Kelebek, H. S., Hindel, J., Hoffman, K., Hoffman, L., Loveridge, A. J., Mandinyenya, B., Ncube, K., Seymour‐Smith, J. L., Sibanda, A. L., Valada, A., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-9986953/v1
Fernigrini, I. M., & Yun, B. (2026) · Journal of Imaging · doi:10.3390/jimaging12080354
KAYA¹, K., BAYRAM², A., & PAŞALI³, H. (2026) · Research Square · doi:10.21203/rs.3.rs-10336158/v1
Eurasian Badger Re-Identification Using AI: New Dataset and Fine-Tuned Foundation Model
Jones, H., Tang, C. M., Littlemore, J., & Rosa, M. J. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-31755-1_29
Region-Aware Multimodal Interleaving for Animal Re-Identification
Wu, Y., Zhao, D., Getz, W. M., Liu, L., Dobbie, G., Wilson, D., & Koh, Y. S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37383-0_5
PosePAL: Efficient Animal Pose Labeling Using Point Trackers
Pan, Z., Pan, B., Yang, G., Harley, A. W., & Guibas, L. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-02976-z
Mahamid, M., Yagur, E., & Shimshoni, I. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-39518-4_22
Rajput, S., Tiwari, B., & Sharma, T. (2026) · International Conference on Natural Language Processing and Computer Vision (ICNPCV) · doi:10.1109/icnpcv69434.2026.11708976
Sketch-Market1501: A Sketch-to-RGB Cross-Modal Person Re-Identification Dataset and Benchmark
Wang, W., & Zhu, D. (2026) · Computers · doi:10.3390/computers15100685
Metadata
- Authors
- Li, S., Li, J., Tang, H., Qian, R., & Lin, W.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Ant Group (China) (CN)
- Intel (United States) (US)
- Venue
- Proceedings of the 28th ACM International Conference on Multimedia
- Publisher
- ACM
- Published
- 2020-10-12
- Pages
- 2590-2598
- OpenAlex
- W3093326439
- Cited by
- 121
- References
- 45
- Citations by year
- 2026: 15 · 2025: 20 · 2024: 27 · 2023: 26 · 2022: 21 · 2021: 9 · 2020: 2
- FWCI
- 4.47
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM International Conference on Multimedia (ACM MM) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Keywords
- Amur tiger, large pose variation, endangered species monitoring
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
