Paper Citation Record · LEDGER

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time

As of 9 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 1 inbound Pith citation observation for arXiv:2603.20509.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2603.20509 v2

Coverage vector

measured 62 of 62 reference resolution

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measured 63 of 63 standing notices

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T13:27:06.309011Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T05:07:38.762620Z

Reference resolution

62 of 62 outbound references displayed

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Outbound references

Observation 4bb5e127-5dc8-42c8-b28d-3a67686c6b02 · outbound

This paper cites an unresolved cited work.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Unresolved cited work

Reference 1

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Observation ab915d3a-8b5e-46a2-8622-309ee9b89bdb · outbound

This paper cites Gradient based sample selection for online continual learning.Advances in neural information processing sys- tems, 32, 2019.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Gradient based sample selection for online continual learning.Advances in neural information processing sys- tems, 32, 2019

Reference 2

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Observation ca5cf78d-3827-4e50-bfec-5b03662d253e · outbound

This paper cites Monitoring the mammalian fauna of ur- ban areas using remote cameras and citizen science.Journal of Urban Ecology, 4(1):juy002, 2018.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Monitoring the mammalian fauna of ur- ban areas using remote cameras and citizen science.Journal of Urban Ecology, 4(1):juy002, 2018

Reference 3

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Observation 49d54799-0dfa-4c60-8e45-e33a8f34458d · outbound

This paper cites The MegaDetector: Large-scale deployment of computer vision for conservation and biodiversity monitor- ing.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time The MegaDetector: Large-scale deployment of computer vision for conservation and biodiversity monitor- ing

Reference 4

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Observation fb6dc012-7c8d-455d-8712-1dc2be661a1d · outbound

This paper cites Recognition in terra incognita.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Recognition in terra incognita

Reference 5

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Observation c0b98bb9-a2d9-4193-91d0-9b4c334fa68c · outbound

This paper cites Recognition in terra incognita.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Recognition in terra incognita

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T17:52:55.447627Z

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Observation 8e7f3457-577d-4b41-9aa9-ac021eade626 · outbound

This paper cites The iWildCam 2018 Challenge Dataset.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time The iWildCam 2018 Challenge Dataset

Reference 7

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Observation 0aeffb56-2af3-4ae9-a3bd-16fd2a61c9a6 · outbound

This paper cites The iWildCam 2021 Competition Dataset.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time The iWildCam 2021 Competition Dataset

Reference 8

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Observation a1b81f20-8472-4789-a9f2-5b9145edf557 · outbound

This paper cites Deep learning-based ecological analysis of camera trap images is impacted by training data quality and quantity.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Deep learning-based ecological analysis of camera trap images is impacted by training data quality and quantity

Reference 9

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Observation ceb6f3a3-9b5d-4160-bbd9-98f7186a3534 · outbound

This paper cites Pelagic Publishing Ltd, 2016.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Pelagic Publishing Ltd, 2016

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T17:52:55.460251Z

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Observation f751c813-3d62-4922-b621-7328867ab26e · outbound

This paper cites Automated wildlife image classification: An active learning tool for ecological applica- tions.Ecological Informatics, 77:102231, 2023.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Automated wildlife image classification: An active learning tool for ecological applica- tions.Ecological Informatics, 77:102231, 2023

Reference 11

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Observation 8b2ca652-9bf1-4d22-86a2-c85be5029ffd · outbound

This paper cites Class-balanced loss based on effective number of samples, 2019.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Class-balanced loss based on effective number of samples, 2019

Reference 12

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Observation 0264a087-1da5-469c-a93a-f118621bcb83 · outbound

This paper cites Class-balanced loss based on effective number of samples.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Class-balanced loss based on effective number of samples

Reference 13

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Observation 9578bc4b-6fc0-4aed-8884-3ceb4789a418 · outbound

This paper cites A continual learning survey: Defying for- getting in classification tasks.IEEE transactions on pattern analysis and machine intelligence, 44(7):3366–3385, 2021.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time A continual learning survey: Defying for- getting in classification tasks.IEEE transactions on pattern analysis and machine intelligence, 44(7):3366–3385, 2021

Reference 14

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Observation 73235891-3539-47af-af31-3f56bc90d390 · outbound

This paper cites Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images

Reference 15

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Observation d4102ccb-582c-4a89-9491-ebe0109eb9e1 · outbound

This paper cites A brief review of domain adaptation.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time A brief review of domain adaptation

Reference 16

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Observation fb91df98-c8fc-4eed-873d-37a519655cf5 · outbound

This paper cites Wildclip: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models.International Journal of Computer Vision, 132(9): 3770–3786, 2024.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Wildclip: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models.International Journal of Computer Vision, 132(9): 3770–3786, 2024

Reference 17

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Observation c877344c-67cf-4e6d-85a0-2a898bf53d60 · outbound

This paper cites Geodesic flow kernel for unsupervised domain adaptation.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Geodesic flow kernel for unsupervised domain adaptation

Reference 18

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Observation cbb2deeb-214e-4361-bb15-c367b5a34ac0 · outbound

This paper cites Bioclip 2: Emergent properties from scaling hierarchi- cal contrastive learning.arXiv preprint arXiv:2505.23883,.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Bioclip 2: Emergent properties from scaling hierarchi- cal contrastive learning.arXiv preprint arXiv:2505.23883,

Reference 19

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Observation ba4b05fb-18b2-4beb-bff0-d26e57f07e64 · outbound

This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 20

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Observation 4e43f141-7e1a-4dde-9eb7-40c07361dc07 · outbound

This paper cites Parameter-efficient transfer learning for nlp, 2019.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Parameter-efficient transfer learning for nlp, 2019

Reference 21

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Observation 0e9fd82c-9a19-4394-8ea3-3378b000aa33 · outbound

This paper cites Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022

Reference 22

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Observation 1fea89b5-f6e5-4962-bed3-05bd20c93dd2 · outbound

This paper cites Idaho camera traps.https://lila.science/datasets/idaho- camera-traps/.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Idaho camera traps.https://lila.science/datasets/idaho- camera-traps/

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T17:52:55.496873Z

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Observation 26c226be-10ed-4977-b293-4eaf0f5e17c9 · outbound

This paper cites Northern and central annamites camera traps 2.0.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Northern and central annamites camera traps 2.0

Reference 24

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Observation 22e3aa9e-b977-4b96-8879-02adc6e3b4f3 · outbound

This paper cites Vi- sual prompt tuning, 2022.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Vi- sual prompt tuning, 2022

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Observation ae49ebbd-8567-42f2-8ae2-6c7686ad3473 · outbound

This paper cites Wilds: A benchmark of in-the- wild distribution shifts.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Wilds: A benchmark of in-the- wild distribution shifts

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This paper cites Microsoft coco: Common objects in context.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Microsoft coco: Common objects in context

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Observation cc7f6de9-073e-43aa-a99d-05681ed3f283 · outbound

This paper cites Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning

Reference 28

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Observation 24d4f7d2-8246-4bb4-b764-f99df77b5d11 · outbound

This paper cites Online continual learning in image classification: An empirical survey.Neurocomputing, 469:28–51, 2022.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Online continual learning in image classification: An empirical survey.Neurocomputing, 469:28–51, 2022

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Observation 95004428-8976-456a-b4e6-7715708a82ae · outbound

This paper cites Fine-tuning is fine, if cali- brated.Advances in Neural Information Processing Systems, 37:136084–136119, 2024.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Fine-tuning is fine, if cali- brated.Advances in Neural Information Processing Systems, 37:136084–136119, 2024

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Observation 5d973289-f1f4-4be1-87d6-4289ab69561f · outbound

This paper cites Lessons and insights from a unifying study of parameter-efficient fine-tuning (peft) in visual recognition.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Lessons and insights from a unifying study of parameter-efficient fine-tuning (peft) in visual recognition

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Observation 2eae9983-58e4-45e2-9f0d-16be393d327d · outbound

This paper cites Two-phase training mitigates class imbalance for camera trap image classification with CNNs.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Two-phase training mitigates class imbalance for camera trap image classification with CNNs

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Observation b7b58619-9586-42b2-8e6e-6a0f2b873703 · outbound

This paper cites Trail camera images of new zealand animals.https://lila.science/datasets/nz- trailcams.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Trail camera images of new zealand animals.https://lila.science/datasets/nz- trailcams

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Resolution: unresolved; no resolver link, observed 2026-08-02T17:52:55.524918Z

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Observation 9df4cb2c-64f5-43d2-8743-3085bc6fe4fa · outbound

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Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Unresolved cited work

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Observation a2222ea2-663a-4e23-a126-bbaefe064b8a · outbound

This paper cites Snap- shot safari: A large-scale collaborative to monitor africa’s remarkable biodiversity.South African Journal of Science, 117(1-2):1–4, 2021.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Snap- shot safari: A large-scale collaborative to monitor africa’s remarkable biodiversity.South African Journal of Science, 117(1-2):1–4, 2021

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This paper cites Har- nessing artificial intelligence to fill global shortfalls in biodi- versity knowledge.Nature Reviews Biodiversity, pages 1–17,.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Har- nessing artificial intelligence to fill global shortfalls in biodi- versity knowledge.Nature Reviews Biodiversity, pages 1–17,

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Observation 9b68f321-3fe7-4354-bffe-d0b3b92b4fe5 · outbound

This paper cites Learning transferable visual models from natural language supervision, 2021.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Learning transferable visual models from natural language supervision, 2021

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Observation c4989219-fbc7-4b7e-8256-b325504195b6 · outbound

This paper cites Balanced meta-softmax for long-tailed visual recog- nition.Advances in neural information processing systems, 33:4175–4186, 2020.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Balanced meta-softmax for long-tailed visual recog- nition.Advances in neural information processing systems, 33:4175–4186, 2020

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Observation 1dcfe0b2-22ff-442b-ae14-3c0c5586f09b · outbound

This paper cites Balanced meta-softmax for long-tailed visual recognition, 2020.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Balanced meta-softmax for long-tailed visual recognition, 2020

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Observation 59575530-4c03-4597-80e4-b4901dacdec7 · outbound

This paper cites A broad review on class imbalance learning techniques.Applied Soft Computing, 143:110415, 2023.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time A broad review on class imbalance learning techniques.Applied Soft Computing, 143:110415, 2023

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Observation 54c13cca-6f25-4584-ab3c-e825f2034cc8 · outbound

This paper cites Extending the WILDS Benchmark for Unsupervised Adaptation.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Extending the WILDS Benchmark for Unsupervised Adaptation

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This paper cites Catalog: A camera trap language-guided contrastive learning model.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Catalog: A camera trap language-guided contrastive learning model

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This paper cites Online class- incremental continual learning with adversarial shapley value.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Online class- incremental continual learning with adversarial shapley value

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Observation ea9eaad3-6544-40c4-b196-2bb1099cfe33 · outbound

This paper cites Domain adaptation: challenges, methods, datasets, and applications.IEEE access, 11:6973–7020,.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Domain adaptation: challenges, methods, datasets, and applications.IEEE access, 11:6973–7020,

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Observation 94db5f6e-a18f-4d95-b622-8b2c24e19c27 · outbound

This paper cites Bioclip: A vision foundation model for the tree of life.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Bioclip: A vision foundation model for the tree of life

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This paper cites Snapshot serengeti, high-frequency annotated camera trap images of 40 mammalian species in an african savanna.Scientific data, 2(1):1–14, 2015.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Snapshot serengeti, high-frequency annotated camera trap images of 40 mammalian species in an african savanna.Scientific data, 2(1):1–14, 2015

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Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Machine learning to classify ani- mal species in camera trap images: Applications in ecology

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This paper cites Use of camera traps for wildlife studies: a review.Biotechnologie, Agronomie, Soci ´et´e et En- vironnement, 18(3), 2014.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Use of camera traps for wildlife studies: a review.Biotechnologie, Agronomie, Soci ´et´e et En- vironnement, 18(3), 2014

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Observation d767ad1f-8ced-43c7-a926-31d0f2b06eba · outbound

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Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Holistic trans- fer: towards non-disruptive fine-tuning with partial target data.Advances in Neural Information Processing Systems, 36:29149–29173, 2023

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Observation 89377453-d3f3-46c0-b5bf-03c7d90a577b · outbound

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Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Perspectives in machine learning for wildlife conservation.Nature communications, 13(1):792,

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Observation 839a42c7-dd57-4003-ae21-8d01e3e3b332 · outbound

This paper cites The inaturalist species classification and de- tection dataset.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time The inaturalist species classification and de- tection dataset

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Observation adcc43b9-d8a9-4df8-951e-dca2e4cdeae3 · outbound

This paper cites Reliable and efficient integration of ai into camera traps for smart wildlife monitoring based on continual learning.Eco- logical Informatics, 83:102815, 2024.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Reliable and efficient integration of ai into camera traps for smart wildlife monitoring based on continual learning.Eco- logical Informatics, 83:102815, 2024

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-02T17:52:55.577964Z

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Observation fb810036-9314-45c5-9beb-a32e00cb8be8 · outbound

This paper cites An evaluation of platforms for processing camera-trap data using artificial intelligence.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time An evaluation of platforms for processing camera-trap data using artificial intelligence

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-02T17:52:55.580988Z

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Observation 71ebe265-3ebd-4e32-a2ea-c7e526c6e97e · outbound

This paper cites Robust fine-tuning of zero-shot models.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Robust fine-tuning of zero-shot models

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-02T17:52:55.583592Z

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Observation 2b97e9fa-8565-488a-b942-2f0bbb8021f8 · outbound

This paper cites Generalized out-of-distribution detection: A survey.Inter- national Journal of Computer Vision, 132(12):5635–5662,.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Generalized out-of-distribution detection: A survey.Inter- national Journal of Computer Vision, 132(12):5635–5662,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-02T17:52:55.586243Z

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Observation 2cbffa5a-dac2-4dc6-a26c-92048e4c0922 · outbound

This paper cites Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-02T17:52:55.588814Z

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Observation 5354937f-7619-4986-96b1-3335784750c8 · outbound

This paper cites Pro- crustean training for imbalanced deep learning.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Pro- crustean training for imbalanced deep learning

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-02T17:52:55.591637Z

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Observation 9f8904a0-5d4c-4408-a4e0-75112ef85bf8 · outbound

This paper cites Identifying and compensating for feature deviation in imbalanced deep learning, 2022.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Identifying and compensating for feature deviation in imbalanced deep learning, 2022

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-02T17:52:55.594156Z

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Observation 399e8e4b-40a6-4206-962d-e92859f2e799 · outbound

This paper cites Automated identification of animal species in camera trap images.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Automated identification of animal species in camera trap images

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-02T17:52:55.596868Z

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Observation f69494d9-8356-4e18-9b3f-1c13d483ffd3 · outbound

This paper cites Deep long-tailed learning: A survey.IEEE transactions on pattern analysis and machine intelligence, 45(9):10795–10816, 2023.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Deep long-tailed learning: A survey.IEEE transactions on pattern analysis and machine intelligence, 45(9):10795–10816, 2023

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-02T17:52:55.599488Z

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Observation 492d2152-efb3-4de4-89c2-b8679ba7f31c · outbound

This paper cites Domain generalization: A survey.IEEE transactions on pattern analysis and machine intelligence, 45(4):4396–4415, 2022.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Domain generalization: A survey.IEEE transactions on pattern analysis and machine intelligence, 45(4):4396–4415, 2022

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-02T17:52:55.602030Z

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Observation 2fa037e5-1dcd-4e87-878c-fdf9370fe4a5 · outbound

This paper cites Class incremental learning for wildlife biodiversity monitoring in camera trap images.Ecological Informatics, 71:101760, 2022.

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time Class incremental learning for wildlife biodiversity monitoring in camera trap images.Ecological Informatics, 71:101760, 2022

Reference 62

Resolution: malformed identifier; no resolver link, observed 2026-08-02T17:52:55.604661Z

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Pith citing papers

Observation 5514fd08-b3de-4765-abe0-723ae21c3dfa · inbound

Democratising Camera Trap AI: An Open-Source Model for Detecting UK Mammals cites this paper.

Democratising Camera Trap AI: An Open-Source Model for Detecting UK Mammals Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-07-24T02:23:00.905528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.