Deep learning‐based methods for individual recognition in small birds
Open accessPublished version of a preprint
Abstract
Individual identification is a crucial step to answer many questions in evolutionary biology and is mostly performed by marking animals with tags. Such methods are well‐established, but often make data collection and analyses time‐consuming, or limit the contexts in which data can be collected. Recent computational advances, specifically deep learning, can help overcome the limitations of collecting large‐scale data across contexts. However, one of the bottlenecks preventing the application of deep learning for individual identification is the need to collect and identify hundreds to thousands of individually labelled pictures to train convolutional neural networks (CNNs). Here we describe procedures for automating the collection of training data, generating training datasets, and training CNNs to allow identification of individual birds. We apply our procedures to three small bird species, the sociable weaver Philetairus socius, the great tit Parus major and the zebra finch Taeniopygia guttata , representing both wild and captive contexts. We first show how the collection of individually labelled images can be automated, allowing the construction of training datasets consisting of hundreds of images per individual. Second, we describe how to train a CNN to uniquely re‐identify each individual in new images. Third, we illustrate the general applicability of CNNs for studies in animal biology by showing that trained CNNs can re‐identify individual birds in images collected in contexts that differ from the ones originally used to train the CNNs. Finally, we present a potential solution to solve the issues of new incoming individuals. Overall, our work demonstrates the feasibility of applying state‐of‐the‐art deep learning tools for individual identification of birds, both in the laboratory and in the wild. These techniques are made possible by our approaches that allow efficient collection of training data. The ability to conduct individual recognition of birds without requiring external markers that can be visually identified by human observers represents a major advance over current methods.
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Deep learning models, such as Convolutional Neural Networks (CNN), have been developed to identify birds, bats or amphibians.
“More recently, artificial intelligence deep learning models, such as Convolutional Neural Networks (CNN), have been developed to identify birds, bats or amphibians36–40.”
From Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests (2023) · cites this paper as [37]
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“The task of tracking and identification is often resolved together using markers [14,30] or marker-less [7,11,43] methods.”
BackgroundSimilarity learning networks for animal individual re-identification: an ecological perspective
Schneider, S., Taylor, G. W., & Kremer, S. C. (2022) · Mammalian Biology · doi:10.1007/s42991-021-00215-1 · Cited by 30 · see citing works
“These could include, but are not exclusive to, national parks, zoos, or camera traps in combination with individuals being tracked by pit tags or GPS similar to Ferreira et al. (2020).”
BackgroundSeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification
Adam, L., Čermák, V., Papafitsoros, K., & Picek, L. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00699 · Cited by 31 · see citing works
“Evaluation of these methods is performed on bench-2014 mark databases, covering several animal groups like mammals [1, 30, 35, 46, 47, 52], reptiles [4, 16], and smaller organisms [10,20,40].”
MethodLeveraging social media and deep learning to detect rare megafauna in video surveys
Mannocci, L., Villon, S., Chaumont, M., Guellati, N., Mouquet, N., Iovan, C., Vigliola, L., & Mouillot, D. (2021) · Conservation Biology · doi:10.1111/cobi.13798 · Cited by 30 · see citing works
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Benchmarking wild bird detection in complex forest scenes
Song, Q., Guan, Y., Guo, X., Guo, X., Chen, Y., Wang, H., Ge, J., Wang, T., & Bao, L. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102466 · Cited by 30 · see citing works
3D-MuPPET: 3D Multi-Pigeon Pose Estimation and Tracking
Waldmann, U., Chan, A. H. H., Naik, H., Nagy, M., Couzin, I. D., Deußen, O., Goldlüecke, B., & Kano, F. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02074-y · Cited by 32 · see citing works
“This is not surprising since ViTPose* already performs the best in 2D, cf. Tab.”
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“Botanical collecting is a delicate process that can be challenging to implement (Funk et al. 2017 and references therein) alongside the many other methods often employed by ethnobotanists.”
BackgroundAutomatic identification of bird females using egg phenotype
Šulc, M., Hughes, A. E., Troscianko, J. T., Štětková, G., Procházka, P., Požgayová, M., Piálek, L., Piálková, R., Brlík, V., & Honza, M. (2021) · Zoological Journal of the Linnean Society · doi:10.1093/zoolinnean/zlab051 · Cited by 28 · see citing works
Identification of free-ranging mugger crocodiles by applying deep learning methods on UAV imagery
Desai, B., Patel, A., Patel, V., Shah, S., Raval, M. S., & Ghosal, R. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101874 · Cited by 26 · see citing works
Opportunities and Challenges in Applying AI to Evolutionary Morphology
He, Y., Mulqueeney, J. M., Watt, E. C., Salili‐James, A., Barber, N. S., Camaiti, M., Hunt, E. S. E., Kippax-Chui, O., Knapp, A., Lanzetti, A., et al. (2024) · Integrative Organismal Biology · doi:10.1093/iob/obae036 · Cited by 28 · see citing works
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“Examples of CNN applications in ecology include the identification of species and other taxa from images (Weinstein 2018), such as those gathered via citizen science platforms (Terry et al. 2020) and camera traps (Ferreira et al. 2020).”
BackgroundA semi-automatic workflow to process images from small mammal camera traps
Böhner, H., Kleiven, E. F., Ims, R. A., & Soininen, E. M. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102150 · Cited by 21 · see citing works
Advanced intelligent monitoring technologies for animals: A survey
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PetFace: A Large-Scale Dataset and Benchmark for Animal Identification
Shinoda, R., & Shiohara, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72649-1_2 · Cited by 20 · see citing works
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Deep learning‐based pose estimation for African ungulates in zoos
Hahn‐Klimroth, M., Kapetanopoulos, T., Gübert, J., & Dierkes, P. W. (2021) · Ecology and Evolution · doi:10.1002/ece3.7367 · Cited by 19 · see citing works
Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research
Hentati‐Sundberg, J., Olin, A. B., Reddy, S., Berglund, P., SVENSSON, E. I., Reddy, M. J., Kasarareni, S., Carlsen, A. A., Hanes, M., Kad, S., et al. (2023) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.329 · Cited by 18 · see citing works
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Sensory collectives in natural systems
Williams, H. J., Sridhar, V. H., Hurme, E., Gall, G. E. C., Borrego, N., Finerty, G. E., Couzin, I. D., Galizia, C. G., Dominy, N. J., Rowland, H. M., et al. (2023) · eLife · doi:10.7554/elife.88028 · Cited by 20 · see citing works
“Using bio-logging/ identification technologies at social sites to record sensory and collective data simultaneously also holds promise (e.g. Ferreira et al., 2020 ); these new perspectives may allow us to better understand the role of inter-individual differences, group structure, or cohesion in…”
BackgroundAdvancing animal farming with deep learning: A systematic review
Rahman, Z. U., Asaari, M. S. M., & Ibrahim, H. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110674 · Cited by 17 · see citing works
The use of gaze to study cognition: limitations, solutions, and applications to animal welfare
Wilson, V. A. D., Bethell, E., & Nawroth, C. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1147278 · Cited by 16 · see citing works
“Machine Learning algorithms now exist for identifying individuals by their facial characteristics in facially heterogeneous species ranging from birds (Ferreira et al., 2020) to primates (Witham, 2018) and online databases are becoming available [e.”
BackgroundLi, J., Liu, C., Wang, L., Liu, Y., Li, R., Lu, X., Lü, J., & Shen, J. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102704 · Cited by 16 · see citing works
Video Recording and Analysis of Avian Movements and Behavior: Insights from Courtship Case Studies
Janisch, J., Mitoyen, C., Perinot, E., Spezie, G., Fusani, L., & Quigley, C. (2021) · Integrative and Comparative Biology · doi:10.1093/icb/icab095 · Cited by 15 · see citing works
“One possible solution for sexually monomorphic individuals are markers, which can help create a visual distinction that can be trained in the algorithm and thus identify subjects regardless of their relative position (e.g., Ferreira et al. 2020).”
BackgroundMethodWildFusion: Individual Animal Identification with Calibrated Similarity Fusion
Čermák, V., Picek, L., Adam, L., Neumann, L., & Matas, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92387-6_2 · Cited by 16 · see citing works
Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks
Wang, Z., Wang, J., Lin, C., Yan, H., Wang, Z., & Ji, L. (2021) · Animals · doi:10.3390/ani11051263 · Cited by 15 · see citing works
“To assist ecologists and zoologists in rapidly and effectively processing large-scale bird image data, computer vision research has long dealt with bird image analysis-related problems, such as bird detection [24], the counting of crowded birds [25,26], fine-grained classification [27–30] of birds, and even individual…”
BackgroundWildlifeReID-10k: Wildlife Re-Identification Dataset with 10k Individual Animals
Adam, L., Čermák, V., Papafitsoros, K., & Picek, L. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00197 · Cited by 18 · see citing works
Incorporating animal agency into research design could improve behavioral and neuroscience research.
Sueur, C., Zanaz, S., & Pelé, M. (2023) · Journal of comparative psychology · doi:10.1037/com0000335 · Cited by 15 · see citing works
Identifying and Counting Avian Blood Cells in Whole Slide Images via Deep Learning
Vogelbacher, M., Strehmann, F., Bellafkir, H., Mühling, M., Korfhage, N., Schneider, D. M., Rösner, S., Schabo, D. G., Farwig, N., & Freisleben, B. (2024) · Birds · doi:10.3390/birds5010004 · Cited by 14 · see citing works
“…visual and acoustic monitoring methods for birds can provide information about the presence and the number of bird species [1] or individuals [2] in certain areas, but analyzing the physiological conditions of individual birds allows us to understand potential causes of negative population…”
BackgroundRecognition of bird species with birdsong records using machine learning methods
Tang, Y., Liu, C., & Yuan, X. (2024) · PLoS ONE · doi:10.1371/journal.pone.0297988 · Cited by 14 · see citing works
Recognition of European mammals and birds in camera trap images using deep neural networks
Schneider, D. M., Lindner, K., Vogelbacher, M., Bellafkir, H., Farwig, N., & Freisleben, B. (2024) · IET Computer Vision · doi:10.1049/cvi2.12294 · Cited by 14 · see citing works
Evaluating the accuracy and biological meaning of visits to RFID‐enabled bird feeders using video
Hughes, E. J., Mady, R. P., & Bonter, D. N. (2021) · Ecology and Evolution · doi:10.1002/ece3.8352 · Cited by 14 · see citing works
An Improved Transfer-Learning for Image-Based Species Classification of Protected Indonesians Birds
Yang, C.-L., Harjoseputro, Y., Hu, Y., & Chen, Y.-Y. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.031305 · Cited by 14 · see citing works
Revisiting animal photo-identification using deep metric learning and network analysisPreprint
Miele, V., Dussert, G., Spataro, B., Chamaillé‐Jammes, S., Allainé, D., & Bonenfant, C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.25.007377 · Cited by 14 · see citing works
Rahman, M. A., Berhe, T. M., Borgianni, L., Ahmed, M. S., Bua, C., Adami, D., & Giordano, S. (2025) · IEEE Access · doi:10.1109/access.2025.3598927 · Cited by 13 · see citing works
“These sources ensured diversity in species representation, environmental conditions, and image quality, which are crucial to building a high-performance detection model [31].”
Multi-Object Tracking in Heterogeneous environments (MOTHe) for animal video recordings
Rathore, A., Sharma, A., Shah, S., Sharma, N., Torney, C. J., & Guttal, V. (2023) · PeerJ · doi:10.7717/peerj.15573 · Cited by 14 · see citing works
“Ferreira et al. (2020) proposed a CNN-based deep learning tracking tool for individual recognition of birds in semi-natural settings (birds kept in cages outdoors).”
Paulet, J., Molina, A., Beltzung, B., Suzumura, T., Yamamoto, S., & Sueur, C. (2024) · Primates · doi:10.1007/s10329-024-01137-5 · Cited by 13 · see citing works
“…(Hou et al. 2020), in Amur tigers, Panthera tigris altaica (Shi et al. 2020), in the small birds Philetairus socius , Parus major and Taeniopygia guttata (Ferreira et al. 2020), in giraffes, Giraffa camelopardalis (Miele et al. 2021), and in Asian elephants, Elephas maximus (de Silva et al. 2022).”
Deep learning object detection to estimate the nectar sugar mass of flowering vegetation
Hicks, D. M., Baude, M., Kratz, C. C., Ouvrard, P., & Stone, G. N. (2021) · Ecological Solutions and Evidence · doi:10.1002/2688-8319.12099 · Cited by 13 · see citing works
A Benchmark Database for Animal Re-Identification and Tracking
Kuncheva, L. I., Williams, F. E., Hennessey, S. L., & Rodríguez, J. J. (2022) · IEEE International Conference on Image Processing, Applications and Systems (IPAS) · doi:10.1109/ipas55744.2022.10052988 · Cited by 13 · see citing works
“[21] Sociable weaver 17,500 50 https://besjournals.”
Yovel, G., Grosbard, I. D., & Abudarham, N. (2023) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2023.0093 · Cited by 12 · see citing works
Martin, K., Adam, O., Obin, N., & Dufour, V. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101818 · Cited by 12 · see citing works
Drivers of alloparental provisioning of fledglings in a colonially breeding bird
Ogino, M., Maldonado‐Chaparro, A. A., & Farine, D. R. (2020) · Behavioral Ecology · doi:10.1093/beheco/araa137 · Cited by 12 · see citing works
Self-citationLonati, M., Jahanbakht, M., Atkins, D., Bierwagen, S. L., Chin, A., Barnett, A., & Rummer, J. L. (2024) · Journal of Fish Biology · doi:10.1111/jfb.15887 · Cited by 11 · see citing works
“Importantly, epaulette sharks change patterns as they mature from their neonatal stage, allowing this study to assess the implications of unstable patterns during early ontogeny (Ferreira et al., 2020).”
Computerized assessment of dominance hierarchy in baboons (Papio papio)
Gullstrand, J., Claidière, N., & Fagot, J. (2021) · Behavior Research Methods · doi:10.3758/s13428-021-01539-z · Cited by 11 · see citing works
Face recognition of a Lorisidae species based on computer vision
Guan, Y., Lei, Y., Zhu, Y., Li, T., Xiang, Y., Dong, P., Jiang, R., Luo, J., Huang, A., YuMai, F., et al. (2023) · Global Ecology and Conservation · doi:10.1016/j.gecco.2023.e02511 · Cited by 18 · see citing works
Takaya, K., Taguchi, Y., & Ise, T. (2023) · Scientific Reports · doi:10.1038/s41598-023-40814-1 · Cited by 10 · see citing works
Automatic individual recognition of Japanese macaques (Macaca fuscata) from sequential images
Ueno, M., Kabata, R., Hayashi, H., Terada, K., & Yamada, K. (2022) · Ethology · doi:10.1111/eth.13277 · Cited by 10 · see citing works
Using pose estimation to identify regions and points on natural history specimens
He, Y., Cooney, C. R., Maddock, S., & Thomas, G. H. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010933 · Cited by 10 · see citing works
Ramos‐Gonzalez, D., & Davison, A. (2021) · Ecology and Evolution · doi:10.1002/ece3.7443 · Cited by 10 · see citing works
“Similar methods have already been implemented to identify, count, and describe the behaviors of animals in a images from motion- sensor cameras in the Serengeti, and to recognize individual song- birds (Ferreira et al., 2020; Norouzzadeh et al., 2018).”
MethodAges of giant panda can be accurately predicted using facial images and machine learning
Zang, H.-X., Su, H., Yu, Q., Feng, L., Hou, R., He, M., Liu, P., Xu, P., Yu, Y., & Chen, P. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101892 · Cited by 10 · see citing works
Knowing a fellow by their bellow: acoustic individuality in the bellows of the American alligator
Jensen, T. R., Anikin, A., Osvath, M., & Reber, S. A. (2023) · Animal Behaviour · doi:10.1016/j.anbehav.2023.11.009 · Cited by 9 · see citing works
“…deep learning arti fi cial neural networks have been shown to be adept at identifying and differentiating between species and/or individuals (Ferreira et al., 2020; Martin et al., 2022; Parsons & Jones, 2000; Trapanotto et al., 2022), and they may prove especially useful for analysing large…”
BackgroundDeep learning‐ and image processing‐based methods for automatic estimation of leaf herbivore damage
Wang, Z., Jiang, Y., Diallo, A. B., & Kembel, S. W. (2024) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14293 · Cited by 9 · see citing works
“Training GAN models requires a huge amount of training data of paired images that include damaged and intact leaves (Ferreira et al., 2020).”
BackgroundMethodAutomatic Bird Species Recognition using Audio and Image Data: A Short Review
Kumar, S., Kondaveeti, H. K., Simhadri, C. G., & Reddy, M. Y. (2023) · IEEE International Conference on Contemporary Computing and Communications (InC4) · doi:10.1109/inc457730.2023.10262973 · Cited by 9 · see citing works
“[52] demonstrated individual bird recognition using deep learning without visual indicators.”
BackgroundSeimandi‐Corda, G., Hood, T., & Cook, S. M. (2024) · Agricultural and Forest Entomology · doi:10.1111/afe.12646 · Cited by 8 · see citing works
“Studies on mammals and birds can usually identify individuals at species level or even at the individual level (Ferreira et al., 2020; Schneider et al., 2019).”
Kern, D., Schiele, T., Klauck, U., & Ingabire, W. (2024) · Animals · doi:10.3390/ani15010001 · Cited by 9 · see citing works
A Transfer Learning Approach to Bird Species Recognition using MobileNetV2
Kondaveeti, H. K., Guturu, S. V. N. S. V., Praveen, K. S. J., & Kumar, S. V. S. (2023) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs56967.2023.10142795 · Cited by 8 · see citing works
“[9] showed recognition of birds in both field and lab settings through deep learning.”
BackgroundBanerjee, S., Gupta, R., Pandian, J. A., Saranya, S., & Mariappan, L. T. (2024) · International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE) · doi:10.1109/iitcee59897.2024.10467695 · Cited by 8 · see citing works
MEWC: A user-friendly AI workflow for customised wildlife-image classification
Brook, B. W., Buettel, J. C., van Lunteren, P., Rajmohan, P. P., & Aandahl, Z. (2025) · Peer Community Journal · doi:10.24072/pcjournal.565 · Cited by 8 · see citing works
An Improved Deep Learning Model of Chili Disease Recognition with Small Dataset
Aminuddin, N. F., Tukiran, Z., Joret, A., Tomari, R., & Morsin, M. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130750 · Cited by 8 · see citing works
“In order to do so, manual data collection and labelling [6] are required to get enormous data, which is resource-intensive and time-consuming.”
BackgroundApplication of multi-species photo-ID database management systems: a user’s perspective
Chan, S. C. Y., Chui, S. Y. S., & Karczmarski, L. (2022) · Mammalian Biology · doi:10.1007/s42991-023-00345-8 · Cited by 8 · see citing works
Image-Based Bird Species Identification Using Machine Learning
Persia Abishal, B., & Juliet, S. (2023) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs57279.2023.10113103 · Cited by 8 · see citing works
“[5] Deep Learning algorithms are used for individual recognition in small birds, which can help overcome the difficulties of collecting large volumes of data from varied contexts.”
Backgroundda Fonseca, E. M., Pope, N. S., Peterman, W. E., Werneck, F. P., Colli, G. R., & Carstens, B. C. (2024) · Heredity · doi:10.1038/s41437-024-00682-5 · Cited by 7 · see citing works
“…applied to several biological questions, ranging from detecting natural selection (Flagel et al. 2019; Torada et al. 2019) and reconstructing phylogenetic and phylogeographic history (Suvorov et al. 2020; Fonseca et al. 2021) to song annotation and individual recognition (Ferreira et al. 2020).”
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“Ferreira et al. [13] developed a method for identifying individual birds using data on captive birds.”
Evaluating the method reproducibility of deep learning models in biodiversity research
Ahmed, W., Kommineni, V. K., König‐Ries, B., Gaikwad, J., Gadelha, L. M. R., Jr., & Samuel, S. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2618 · Cited by 7 · see citing works
Chimento, M., Chan, A. H. H., Aplin, L. M., & Kano, F. (2025) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.70051 · Cited by 7 · see citing works
“…limitation involves the use of computer vision and machine learning tools (A. Mathis et al. 2020; Mackenzie Weygandt Mathis et al. 2020; Luxem et al. 2023), a development facilitated by a concomitant increase in the volume and resolution of image data (Griebling et al. 2022; Ehlman et al. 2023).”
Finding Nemo’s Giant Cousin: Keypoint Matching for Robust Re-Identification of Giant Sunfish
Pedersen, M., Nyegaard, M., & Moeslund, T. B. (2023) · Journal of Marine Science and Engineering · doi:10.3390/jmse11050889 · Cited by 7 · see citing works
A simple tool for linking photo-identification with multimedia data to track mammal behaviour
Machado, A. M. S., & Cantor, M. (2021) · Mammalian Biology · doi:10.1007/s42991-021-00189-0 · Cited by 7 · see citing works
“…as photographic, acoustic and video recording (Karczmarski et al. 2022a, b), rely on systematic comparison of natural marks or behaviours (e.g. Karanth and Nichols 1998; Muller et al. 2018; Longden et al. 2020) to track individuals from a distance (e.g. Clapham et al. 2020; Ferreira et al. 2020).”
BackgroundLü, Y., Li, J., Zhao, Z., Zhang, Y., Tong, Y., Teng, B., Liu, N., Shaw, J. J., & Bai, M. (2024) · Research Square · doi:10.21203/rs.3.rs-3832815/v1 · Cited by 7 · see citing works
Similarity learning networks uniquely identify individuals of four marine and terrestrial species
Kabuga, E., Langley, I., Civil, M. A., Measey, J. G., Bah, B., & Durbach, I. N. (2024) · Ecosphere · doi:10.1002/ecs2.70012 · Cited by 6 · see citing works
BEHAVE - facilitating behaviour coding from videos with AI-detected animals
Elhorst, R., Syposz, M., & Wojczulanis‐Jakubas, K. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103106 · Cited by 6 · see citing works
- Authors
- Ferreira, A. C., Silva, L. R., Renna, F., Brandl, H. B., Renoult, J. P., Farine, D. R., Covas, R., & Doutrelant, C.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Universidade do Porto (PT)
- University of Konstanz (DE)
- University of Cape Town (ZA)
- Corresponding author
- Ferreira, André C.
- ORCID
- Venue
- Methods in Ecology and Evolution
- Publisher
- Wiley
- Published
- 2020-07-26
- Volume
- 11
- Issue
- 9
- Pages
- 1072-1085
- ISSN
- 2041-210X (ISSN-L)
- OpenAlex
- W3038699438
- Cited by
- 203
- References
- 47
- Citations by year
- 2026: 15 · 2025: 38 · 2024: 43 · 2023: 39 · 2022: 27 · 2021: 35 · 2020: 6
- FWCI
- 10.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Methods in Ecology and Evolution (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- HAL (submittedVersion, cc-by)
- KOPS (University of Konstanz) (submittedVersion)
- KOPS (University of Konstanz) (submittedVersion, cc-by)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Max-Planck-Gesellschaft
- Fundação para a Ciência e a Tecnologia (CEECIND/01970/2017, IF/01411/2014/CP1256/CT0007, PTDC/BIA‐EVF/5249/201, SFRH/BD/122106/2016)
- Agence Nationale de la Recherche (ANR‐ 15‐CE32‐0012‐02, ANR 19‐CE02‐0014‐02)
- Deutsche Forschungsgemeinschaft (FA 1402/4‐1, 422037984)
- University of Cape Town
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Keywords
- individual recognition, small birds, convolutional neural networks, sociable weaver, Parus major, zebra finch, re-identification
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 ›
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