Applying Few-Shot Learning for In-the-Wild Camera-Trap Species Classification
Few-shot learning (FSL) describes the challenge of learning a new task using a minimum amount of labeled data, and we have observed significant progress made in this area. In this paper, we explore the effectiveness of the FSL theory by considering a real-world problem where labels are hard to obtai...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/4/3/31 |
| _version_ | 1827727604945256448 |
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| author | Haoyu Chen Stacy Lindshield Papa Ibnou Ndiaye Yaya Hamady Ndiaye Jill D. Pruetz Amy R. Reibman |
| author_facet | Haoyu Chen Stacy Lindshield Papa Ibnou Ndiaye Yaya Hamady Ndiaye Jill D. Pruetz Amy R. Reibman |
| author_sort | Haoyu Chen |
| collection | DOAJ |
| description | Few-shot learning (FSL) describes the challenge of learning a new task using a minimum amount of labeled data, and we have observed significant progress made in this area. In this paper, we explore the effectiveness of the FSL theory by considering a real-world problem where labels are hard to obtain. To assist a large study on chimpanzee hunting activities, we aim to classify various animal species that appear in our in-the-wild camera traps located in Senegal. Using the philosophy of FSL, we aim to train an FSL network to learn to separate animal species using large public datasets and implement the network on our data with its novel species/classes and unseen environments, needing only to label a few images per new species. Here, we first discuss constraints and challenges caused by having in-the-wild uncurated data, which are often not addressed in benchmark FSL datasets. Considering these new challenges, we create two experiments and corresponding evaluation metrics to determine a network’s usefulness in a real-world implementation scenario. We then compare results from various FSL networks, and describe how factors may affect a network’s potential real-world usefulness. We consider network design factors such as distance metrics or extra pre-training, and examine their roles in a real-world implementation setting. We also consider additional factors such as support set selection and ease of implementation, which are usually ignored when a benchmark dataset has been established. |
| first_indexed | 2024-03-10T23:07:43Z |
| format | Article |
| id | doaj.art-a0b75d9c4bc743efa188988540a2a70f |
| institution | Directory Open Access Journal |
| issn | 2673-2688 |
| language | English |
| last_indexed | 2024-03-10T23:07:43Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj.art-a0b75d9c4bc743efa188988540a2a70f2023-11-19T09:12:26ZengMDPI AGAI2673-26882023-07-014357459710.3390/ai4030031Applying Few-Shot Learning for In-the-Wild Camera-Trap Species ClassificationHaoyu Chen0Stacy Lindshield1Papa Ibnou Ndiaye2Yaya Hamady Ndiaye3Jill D. Pruetz4Amy R. Reibman5Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USADepartment of Anthropology, Purdue University, West Lafayette, IN 47907, USADepartment of Animal Biology, University Cheikh Anta Diop of Dakar, BP 5005, Dakar 10700, SenegalDepartment of Animal Biology, University Cheikh Anta Diop of Dakar, BP 5005, Dakar 10700, SenegalDepartment of Anthropology, Texas State University, San Marcos, TX 78666, USAElmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USAFew-shot learning (FSL) describes the challenge of learning a new task using a minimum amount of labeled data, and we have observed significant progress made in this area. In this paper, we explore the effectiveness of the FSL theory by considering a real-world problem where labels are hard to obtain. To assist a large study on chimpanzee hunting activities, we aim to classify various animal species that appear in our in-the-wild camera traps located in Senegal. Using the philosophy of FSL, we aim to train an FSL network to learn to separate animal species using large public datasets and implement the network on our data with its novel species/classes and unseen environments, needing only to label a few images per new species. Here, we first discuss constraints and challenges caused by having in-the-wild uncurated data, which are often not addressed in benchmark FSL datasets. Considering these new challenges, we create two experiments and corresponding evaluation metrics to determine a network’s usefulness in a real-world implementation scenario. We then compare results from various FSL networks, and describe how factors may affect a network’s potential real-world usefulness. We consider network design factors such as distance metrics or extra pre-training, and examine their roles in a real-world implementation setting. We also consider additional factors such as support set selection and ease of implementation, which are usually ignored when a benchmark dataset has been established.https://www.mdpi.com/2673-2688/4/3/31applied machine learningfew-shot learningin-the-wild data processingecology applications |
| spellingShingle | Haoyu Chen Stacy Lindshield Papa Ibnou Ndiaye Yaya Hamady Ndiaye Jill D. Pruetz Amy R. Reibman Applying Few-Shot Learning for In-the-Wild Camera-Trap Species Classification AI applied machine learning few-shot learning in-the-wild data processing ecology applications |
| title | Applying Few-Shot Learning for In-the-Wild Camera-Trap Species Classification |
| title_full | Applying Few-Shot Learning for In-the-Wild Camera-Trap Species Classification |
| title_fullStr | Applying Few-Shot Learning for In-the-Wild Camera-Trap Species Classification |
| title_full_unstemmed | Applying Few-Shot Learning for In-the-Wild Camera-Trap Species Classification |
| title_short | Applying Few-Shot Learning for In-the-Wild Camera-Trap Species Classification |
| title_sort | applying few shot learning for in the wild camera trap species classification |
| topic | applied machine learning few-shot learning in-the-wild data processing ecology applications |
| url | https://www.mdpi.com/2673-2688/4/3/31 |
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