REFERENCES
1. Prychepa, M.; Kovalenko, Y. Waterfowl as indicators of the state of wetland ecosystems. In Innovation in Science: Global Trends and Regional Aspect, Proceedings of the Conference, Riga, Latvia, March 12-13, 2021; Baltija Publishing: Riga, 2021; pp. 9-12.
2. Peng, J.; Wang, D.; Liao, X.; et al. Wild animal survey using UAS imagery and deep learning: modified Faster R-CNN for kiang detection in Tibetan Plateau. ISPRS. J. Photogramm. Remote. Sens. 2020, 169, 364-76.
3. Chen, X.; Pu, H.; He, Y.; et al. An efficient method for monitoring birds based on object detection and multi-object tracking networks. Animals 2023, 13, 1713.
4. Zhang, Z.; Zhang, L.; Lu, B.; et al. Temporal insights into ecological community: advancing waterbird monitoring with dome camera and deep learning. J. Environ. Manage. 2025, 387, 125769.
5. Wu, E.; Wang, H.; Lu, H.; et al. Unlocking the potential of deep learning for migratory waterbirds monitoring using surveillance video. Remote. Sens. 2022, 14, 514.
6. Chalmers, C.; Fergus, P.; Wich, S.; et al. Removing human bottlenecks in bird classification using camera trap images and deep learning. Remote. Sens. 2023, 15, 2638.
7. Mulero-Pérez, D.; Rodriguez-Juan, J.; Ramirez-Gordillo, T.; et al. A federated learning architecture for bird species classification in wetlands. J. Sens. Actuator. Netw. 2025, 14, 71.
8. Oba, Y.; Doi, H. Accelerating ecosystem monitoring through computer vision with deep metric learning. Ecol. Complex. 2025, 62, 101124.
9. Wah, C.; Branson, S.; Welinder, P.; Perona, P.; Belongie, S. The Caltech-UCSD Birds-200-2011 Dataset. 2011. https://authors.library.caltech.edu/records/cvm3y-5hh21. (accessed 2026-06-24).
10. Ma, J.; Guo, J.; Zheng, X.; Fang, C. An improved bird detection method using surveillance videos from Poyang Lake based on YOLOv8. Animals 2024, 14, 3353.
11. Fang, S.; Shen, Y.; Zou, H.; Yin, Y.; Jin, W.; Zhou, H. Birds-YOLO: a bird detection model for Dongting Lake based on modified YOLOv11. Biology 2025, 14, 1515.
12. Huang, Q.; Zhang, C.; Hu, C.; Xie, J.; Wang, Y.; Zhang, J. Waterbird image recognition using lightweight deep learning in wetland environment. Avian. Res. 2025, 16, 100306.
13. He, J.; Chen, J.; Liu, S.; et al. TransFG: a transformer architecture for fine-grained recognition. AAAI 2022, 36, 852-60.
14. Du, R.; Chang, D.; Bhunia, A. K.; et al. Fine-grained visual classification via progressive multi-granularity training of jigsaw patches. arXiv 2020, arXiv:2003.03836. Available online: https://doi.org/10.48550/arXiv.2003.03836. (accessed 2026-06-24).
15. Xie, S.; Xie, J.; Liu, Y.; et al. Step-by-step to success: multi-stage learning driven robust audiovisual fusion network for fine-grained bird species classification. Avian. Res. 2025, 16, 100280.
16. Zha, D.; Bhat, Z. P.; Lai, K. H.; et al. Data-centric artificial intelligence: a survey. arXiv 2023, arXiv:2303.10158. Available online: https://doi.org/10.48550/arXiv.2303.10158. (accessed 2026-06-24).
17. Song, H.; Kim, M.; Park, D.; Shin, Y.; Lee, J. G. Learning from noisy labels with deep neural networks: a survey. IEEE. Trans. Neural. Netw. Learn. Syst. 2023, 34, 8135-53.
18. Northcutt, C.; Jiang, L.; Chuang, I. Confident learning: estimating uncertainty in dataset labels. JAIR 2021, 70, 1373-411.
19. Feuer, B.; Xu, J.; Cohen, N.; et al. SELECT: a large-scale benchmark of data curation strategies for image classification. arXiv 2024, arXiv:2410.05057. Available online: https://doi.org/10.48550/arXiv.2410.05057. (accessed 2026-06-24).
20. Drenkow, N.; Unberath, M. A causal framework for aligning image quality metrics and deep neural network robustness. npj. Artif. Intell. 2025, 1, 24.
21. Liu, Y.; Zhang, H.; Che, X.; Zhang, W.; Lu, G. Deep learning based fine‐grained image classification: recent advances, applications and future outlook. IET. Image. Process. 2025, 19, e70243.
22. Zhang, L.; Zhou, Y.; Gao, F.; et al. Q-Norm: robust representation learning via quality-adaptive normalization. In 2025 IEEE/CVF International Conference on Computer Vision (ICCV), Honolulu, USA, October 19-23, 2025; IEEE: 2025; pp. 13901-11.
23. Jiang, K.; Jiang, J.; Liu, X.; Yao, H.; Lin, C. W. PH-Mamba: enhancing Mamba with position encoding and harmonized attention for image deraining and beyond. IEEE. Trans. Image. Process. 2026, 35, 1727-39.
24. Xiao, Y.; Yuan, Q.; Jiang, K.; Chen, Y.; Wang, S.; Lin, C. W. Multi-axis feature diversity enhancement for remote sensing video super-resolution. IEEE. Trans. Image. Process. 2025, 34, 1766-78.
25. Jiang, K.; Wang, Z.; Yi, P.; et al. Rain-free and residue hand-in-hand: a progressive coupled network for real-time image deraining. IEEE. Trans. Image. Process. 2021, 30, 7404-18.
26. Müller, S. G.; Hutter, F. TrivialAugment: tuning-free yet state-of-the-art data augmentation. arXiv 2021, arXiv:2103.10158. Available online: https://doi.org/10.48550/arXiv.2103.10158. (accessed 2026-06-24).
27. Wang, P.; Zhao, Z.; Wen, H.; et al. LLM-AutoDA: large language model-driven automatic data augmentation for long-tailed problems. In Advances in Neural Information Processing Systems 37 (NeurIPS 2024), Vancouver, Canada, Dec 10-15, 2024; Curran Associates, Inc.: 2024; pp. 115783-814.
28. Pearson, K. LIII. On lines and planes of closest fit to systems of points in space. Lond. Edinb. Dublin. Philos. Mag. J. Sci. 1901, 2, 559-72.
29. Pech-Pacheco, J. L.; Cristobal, G.; Chamorro-Martinez, J.; Fernandez-Valdivia, J. Diatom autofocusing in brightfield microscopy: a comparative study. In Proceedings of the 15th International Conference on Pattern Recognition, Barcelona, Spain, September 3-7, 2000; IEEE: 2000; pp. 3318-21.
30. Moulden, B.; Kingdom, F.; Gatley, L. F. The standard deviation of luminance as a metric for contrast in random-dot images. Perception 1990, 19, 79-101.
31. Duda, R.; Hart, P. Pattern classification and scene analysis. Wiley: 1973. https://books.google.com/books?id=POMGRAAACAAJ&source=gbs_ViewAPI. (accessed 2026-06-24).
32. Hampel, F. R. The influence curve and its role in robust estimation. J. Am. Stat. Assoc. 1974, 69, 383-93.
33. Bao, Y.; Kang, G.; Yang, L.; Duan, X.; Zhao, B.; Zhang, B. Normalizing batch normalization for long-tailed recognition. arXiv 2025, arXiv:2501.03122. Available online: https://doi.org/10.48550/arXiv.2501.03122. (accessed 2026-06-24).
34. Mcinnes, L.; Healy, J.; Astels, S. hdbscan: hierarchical density based clustering. J. Open. Source. Softw. 2017, 2, 205.
35. Gagolewski, M.; Bartoszuk, M.; Cena, A. Are cluster validity measures (in) valid? Inform. Sci. 2021, 581, 620-36.
37. Zhang, H.; Cisse, M.; Dauphin, Y. N.; Lopez-Paz, D. mixup: beyond empirical risk minimization. arXiv 2017, arXiv:1710.09412. Available online: https://doi.org/10.48550/arXiv.1710.09412. (accessed 2026-06-24).
38. Murphy, K. P. Probabilistic machine learning: an introduction. MIT Press: 2022. https://probml.github.io/pml-book/book1.html. (accessed 2026-06-24).
39. Liu, W.; Anguelov, D.; Erhan, D.; et al. SSD: single shot MultiBox detector. In: Leibe B, Matas J, Sebe N, Welling M, editors. Computer Vision - ECCV 2016. Cham: Springer International Publishing; 2016. pp. 21-37.
40. Qin, D.; Leichner, C.; Delakis, M.; et al. MobileNetV4: universal models for the mobile ecosystem. In: Leonardis A, Ricci E, Roth S, Russakovsky O, Sattler T, Varol G, editors. Computer Vision - ECCV 2024. Cham: Springer Nature Switzerland; 2025. pp. 78-96.
41. Wang, J.; Liu, X.; Zhou, X.; et al. Joint asymmetric loss for learning with noisy labels. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Honolulu, USA, October 19-23, 2025; pp. 1947-56.
