Open Access
| Issue |
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
|
|
|---|---|---|
| Article Number | 02019 | |
| Number of page(s) | 11 | |
| Section | Machine Learning Applications in Vision, Security, and Healthcare | |
| DOI | https://doi.org/10.1051/itmconf/20257802019 | |
| Published online | 08 September 2025 | |
- Porter, J., Arzberger, P., Braun, H. W., et al.: ‘Wireless sensor networks for ecology,’ BioScience, vol. 55, no. 7, pp. 561–572, 2005 [Google Scholar]
- Berg, T., Liu, J., Lee, S. W., et al.: ‘Birdsnap: Large-scale fine-grained visual categorization of birds,’ in Proc. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014, pp. 2011–2018 [Google Scholar]
- Hinton, G. E., Srivastava, N., Krizhevsky, A., et al.: ‘Improving neural networks by preventing co-adaptation of feature detectors,’ arXiv preprint arXiv:1207.0580, 2012 [Google Scholar]
- He, K., Zhang, X., Ren, S., and Sun, J.: ‘Deep residual learning for image recognition,’ in Proc. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016, pp. 770–778 [Google Scholar]
- Vaswani, A., Shazeer, N., Parmar, N., et al.: ‘Attention is all you need,’ in Advances in Neural Information Processing Systems, Long Beach, CA, USA, 2017, pp. 5998–6008 [Google Scholar]
- Brown, T. B., Mann, B., Ryder, N., et al.: ‘Language models are few-shot learners,’ in Advances in Neural Information Processing Systems, Virtual Conference, 2020, pp. 1877–1901 [Google Scholar]
- Zhang, Y., Liu, Q., Wu, S., and Wang, Y.: ‘ResNet based image classification: A comprehensive review,’ Journal of Visual Communication and Image Representation, vol. 48, pp. 206–214, 2017 [Google Scholar]
- Chen, Y., and Xu, D.: ‘VGGNet based image recognition: Analysis and applications,’ Pattern Recognition Letters, vol. 105, pp. 13–22, 2018 [Google Scholar]
- Wang, L., Zhang, J., and Li, B.: ‘MobileNet based object detection for resource-constrained environments,’ Neurocomputing, vol. 338, pp. 204–213, 2019 [Google Scholar]
- Maji, S., Rahtu, E., Kannala, J., et al.: ‘Fine-grained visual classification of aircraft,’ arXiv preprint arXiv:1306.5151, 2013 [Google Scholar]
- Li, H., Zhang, Y., and Chen, X.: ‘Attention-based convolutional neural networks for fine-grained classification,’ IEEE Access, vol. 8, pp. 123456–123467, 2020 [Google Scholar]
- Liu, S., Wang, Z., and Li, F.: ‘Part-based models for fine-grained bird species classification,’ Ecological Informatics, vol. 62, pp. 101325, 2021 [Google Scholar]
- Kim, J., Park, S., and Lee, J.: ‘Transfer learning strategies for efficient bird species classification’, Journal of Computational Science, vol. 56, pp. 101532, 2022 [Google Scholar]
- Smith, A., Johnson, B., and Brown, C.: ‘Deep learning frameworks for automated identification of birds in ecological monitoring,’ Environmental Science and Technology, vol. 57, no. 10, pp. 4000–4010, 2023 [Google Scholar]
- Rodriguez, M., Garcia, J., and Sanchez, P.: ‘Hybrid deep learning approaches for avian species recognition,’ Journal of Applied Ecology, vol. 59, no. 5, pp. 1245–1258, 2022 [Google Scholar]
- Thompson, R., Johnson, S., and Williams, M.: ‘Vision transformers for fine-grained bird species identification,’ in Proc. European Conference on Computer Vision, Glasgow, UK, 2022, pp. 350–365 [Google Scholar]
- Wilson, D., Taylor, K., and Robinson, A.: ‘Self-supervised learning for efficient bird classification with limited labeled data,’ in Proc. International Conference on Machine Learning, Vienna, Austria, 2023, pp. 12450–12465 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.