Paper Citation Record · LEDGER

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings

As of 13 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2507.02403.

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

pith.paper-citation-record.v1
2507.02403 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:33:38.269666Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a3297303-2fef-4a3f-997f-bef719387b90 · outbound

This paper cites Beyond observation: Deep learning for animal behavior and ecological con- servation,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Beyond observation: Deep learning for animal behavior and ecological con- servation,

Reference 1

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Observation aca353f3-e4bb-4ebc-87f2-c5c7f108979b · outbound

This paper cites Hotspotter—patterned species instance recognition,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Hotspotter—patterned species instance recognition,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:33:45.315256Z

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Observation a071e36f-276c-478c-bc1f-a1d5dd952240 · outbound

This paper cites Automated identification of individuals in wildlife population using siamese neural networks,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Automated identification of individuals in wildlife population using siamese neural networks,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:33:45.151530Z

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Observation 279d83bd-0fcd-4e45-a335-08925d2338d1 · outbound

This paper cites WildFusion: Individual Animal Identification with Calibrated Similarity Fusion.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings WildFusion: Individual Animal Identification with Calibrated Similarity Fusion

Reference 4

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Observation 1e736a61-06cc-4c58-8b9c-dcb5907a4b1e · outbound

This paper cites Toward re-identifying any animal,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Toward re-identifying any animal,

Reference 5

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Observation 50f6393f-d742-4002-bfae-573855955b44 · outbound

This paper cites Wildlifedatasets: An open-source toolkit for animal re- identification,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Wildlifedatasets: An open-source toolkit for animal re- identification,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:33:44.810929Z

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Observation 1c644e46-8229-4d41-9552-e9cbde257482 · outbound

This paper cites WildlifeReID-10k: Wildlife re-identification dataset with 10k individual animals.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings WildlifeReID-10k: Wildlife re-identification dataset with 10k individual animals

Reference 7

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Observation f85b7869-1af2-4dfe-9416-b99394439c01 · outbound

This paper cites Multispecies Animal Re-ID Using a Large Community-Curated Dataset.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Multispecies Animal Re-ID Using a Large Community-Curated Dataset

Reference 8

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Observation 4b0d7681-10fa-43e1-adff-3e453a117cb8 · outbound

This paper cites Unsupervised feature learning via non-parametric instance discrimina- tion,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Unsupervised feature learning via non-parametric instance discrimina- tion,

Reference 9

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Observation 32563562-5648-4a3e-a59e-4a47a5b644e0 · outbound

This paper cites Distinctive image features from scale- invariant keypoints,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Distinctive image features from scale- invariant keypoints,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:33:44.404166Z

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Observation 3b8356a6-a794-4c01-8423-9e98ba119e14 · outbound

This paper cites Su- perpoint: Self-supervised interest point detection and description,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Su- perpoint: Self-supervised interest point detection and description,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:33:44.208432Z

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Observation 1ef7bd0d-4537-48c2-82b4-7771aa9b2682 · outbound

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

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Bioclip: A vision foundation model for the tree of life,

Reference 12

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Observation 85025bbe-3320-4874-ad88-868ba5ddac2b · outbound

This paper cites Transreid: Transformer-based object re-identification,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Transreid: Transformer-based object re-identification,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:33:43.623741Z

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Observation cb113649-d475-422d-9b19-72747325a9b0 · outbound

This paper cites Understanding the Impact of Training Set Size on Animal Re-identification.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Understanding the Impact of Training Set Size on Animal Re-identification

Reference 14

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Observation 8f8a5f6b-d41d-4e91-b061-8c122e4f716d · outbound

This paper cites replicant: a pipeline for generating anno- tated images of animals in complex environments using unreal engine,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings replicant: a pipeline for generating anno- tated images of animals in complex environments using unreal engine,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:33:43.441567Z

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Observation 7d6ef19c-3408-41a9-ace7-4ae9fd4dd63a · outbound

This paper cites A critical analysis of self-supervision, or what we can learn from a single image.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings A critical analysis of self-supervision, or what we can learn from a single image

Reference 16

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Observation 5f85a4a3-3ee6-4a1b-8f2a-a394d0f8a836 · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings A simple framework for contrastive learning of visual representations,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:33:43.275155Z

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Observation 4e399626-60aa-4df7-bf46-34b1c2d56569 · outbound

This paper cites Mo- mentum contrast for unsupervised visual representation learning,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Mo- mentum contrast for unsupervised visual representation learning,

Reference 18

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Observation 35717df3-306d-4a3b-98cc-38edfb3f42b6 · outbound

This paper cites Exploring simple siamese rep- resentation learning,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Exploring simple siamese rep- resentation learning,

Reference 19

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Observation 90fb0f56-8fdd-424a-96ae-9cce46d92989 · outbound

This paper cites Bootstrap your own latent- a new approach to self-supervised learning,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Bootstrap your own latent- a new approach to self-supervised learning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:33:42.603491Z

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Observation 0a848f28-8dcd-4f57-a253-622aa5b1d4dc · outbound

This paper cites Barlow twins: Self-supervised learning via redundancy reduction,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Barlow twins: Self-supervised learning via redundancy reduction,

Reference 21

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Observation 9ebb4cdb-c7e7-4cdd-ac66-a0dd500483a1 · outbound

This paper cites Emerging properties in self-supervised vision transformers,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Emerging properties in self-supervised vision transformers,

Reference 22

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This paper cites Unsupervised learning of visual features by contrasting cluster assignments,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Unsupervised learning of visual features by contrasting cluster assignments,

Reference 23

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This paper cites Sim- ilarity of neural network representations revisited,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Sim- ilarity of neural network representations revisited,

Reference 24

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Observation 71f2a55f-bd25-40f8-b936-169c92eeb4bd · outbound

This paper cites Towards Out-Of-Distribution Generalization: A Survey.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Towards Out-Of-Distribution Generalization: A Survey

Reference 25

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Observation 541a0a24-b00a-46bc-b083-2b041b2fa2cd · outbound

This paper cites Self-Supervised Features Improve Open-World Learning.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Self-Supervised Features Improve Open-World Learning

Reference 26

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Observation 5beea4ac-4cf4-4c52-b44c-fb95b43bf241 · outbound

This paper cites How well do self-supervised models transfer?.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings How well do self-supervised models transfer?

Reference 27

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Observation d102cb7e-4bc8-4c81-b3c8-9157fdb9b5c8 · outbound

This paper cites Efficient pipeline for camera trap image review,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Efficient pipeline for camera trap image review,

Reference 28

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Observation 5fc5ac4b-42e4-4a77-a38e-5ac73914811d · outbound

This paper cites Decoupled contrastive learning,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Decoupled contrastive learning,

Reference 29

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This paper cites Improved deep metric learning with multi- class n-pair loss objective,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Improved deep metric learning with multi- class n-pair loss objective,

Reference 30

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This paper cites Fast- siam: Resource-efficient self-supervised learning on a single gpu,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Fast- siam: Resource-efficient self-supervised learning on a single gpu,

Reference 31

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Observation 6fee9afe-6ea1-42c3-a948-f75ee2f54935 · outbound

This paper cites Arcface: Additive angular margin loss for deep face recognition,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Arcface: Additive angular margin loss for deep face recognition,

Reference 32

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Observation 731d68f5-fd4e-4639-ace1-6fac901be9be · outbound

This paper cites In Defense of the Triplet Loss for Person Re-Identification.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings In Defense of the Triplet Loss for Person Re-Identification

Reference 33

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Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Su- pervised contrastive learning,

Reference 34

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Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Lightly

Reference 35

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This paper cites Benchmarking representation learn- ing for natural world image collections,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Benchmarking representation learn- ing for natural world image collections,

Reference 36

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Observation 966d145a-623d-4cda-ab60-b9ab154d0ae8 · outbound

This paper cites Learning multiple layers of features from tiny images.(2009),.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Learning multiple layers of features from tiny images.(2009),

Reference 37

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This paper cites Animal kingdom: A large and diverse dataset for animal behavior understanding,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Animal kingdom: A large and diverse dataset for animal behavior understanding,

Reference 38

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This paper cites Animals detection images dataset,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Animals detection images dataset,

Reference 39

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This paper cites Microsoft coco: Common objects in context,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Microsoft coco: Common objects in context,

Reference 40

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Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Cats and dogs,

Reference 41

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This paper cites Zero- shot learning—a comprehensive evaluation of the good, the bad and the ugly,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Zero- shot learning—a comprehensive evaluation of the good, the bad and the ugly,

Reference 42

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This paper cites Automated flower classification over a large number of classes,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Automated flower classification over a large number of classes,

Reference 43

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This paper cites Incremental class learning using variational autoencoders with similarity learning,.

Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Incremental class learning using variational autoencoders with similarity learning,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:33:39.067464Z

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Pith citing papers

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