Paper Citation Record · LEDGER

A model-agnostic active learning approach for animal detection from camera traps

As of 14 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 1 inbound Pith citation observation for arXiv:2507.06537.

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

pith.paper-citation-record.v1
2507.06537 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:05:02.048300Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:05:01.897569Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T19:05:02.135949Z

Reference resolution

24 of 24 outbound references displayed

verified exact0
verified fuzzy20
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 213587bb-2d62-4e05-a2ee-1b846309d780 · outbound

This paper cites A model-agnostic active learning approach for animal detection from camera traps.

A model-agnostic active learning approach for animal detection from camera traps A model-agnostic active learning approach for animal detection from camera traps

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T19:05:02.143959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 65ee5a84-290a-4add-a203-291d559c48b3 · outbound

This paper cites The uncertainty of a sample is often derived from the confidence score of the target model with re- spect to that sample.

A model-agnostic active learning approach for animal detection from camera traps The uncertainty of a sample is often derived from the confidence score of the target model with re- spect to that sample

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:02.657958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 62e77770-9dbe-476a-8d59-36d5730ad4d8 · outbound

This paper cites Overview Our problem of interest can be stated as follows.

A model-agnostic active learning approach for animal detection from camera traps Overview Our problem of interest can be stated as follows

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:02.640005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3156b9a6-114f-44c2-bca5-97a657d02ed8 · outbound

This paper cites Dataset We validated our method on SAWIT [18], a benchmark dataset of small-sized animals captured from camera traps in the wild.

A model-agnostic active learning approach for animal detection from camera traps Dataset We validated our method on SAWIT [18], a benchmark dataset of small-sized animals captured from camera traps in the wild

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:02.620368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1b011ef4-6e82-44b3-a20b-337698bf7a01 · outbound

This paper cites This is enabled by incorporating both un- certainty and diversity quantities in the sampling process.

A model-agnostic active learning approach for animal detection from camera traps This is enabled by incorporating both un- certainty and diversity quantities in the sampling process

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:02.597174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e1e1c64c-724d-4bed-8a26-80ced2fe0103 · outbound

This paper cites Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning,.

A model-agnostic active learning approach for animal detection from camera traps Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:02.572506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3729971f-9a60-4eb7-bfbf-1c600a2971cc · outbound

This paper cites The iWildCam 2021 Competition Dataset.

A model-agnostic active learning approach for animal detection from camera traps The iWildCam 2021 Competition Dataset

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T19:05:01.944099Z

Source-reported events for the cited work

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Observation 6f62fcc5-2144-4b7e-963a-890a9f1e55a1 · outbound

This paper cites Smart camera traps and computer vision improve detections of small fauna,.

A model-agnostic active learning approach for animal detection from camera traps Smart camera traps and computer vision improve detections of small fauna,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:02.551235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d5caf20c-dca7-47e4-902b-a947b0263428 · outbound

This paper cites A deep active learning system for species identification and counting in camera trap images,.

A model-agnostic active learning approach for animal detection from camera traps A deep active learning system for species identification and counting in camera trap images,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:02.522939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bb7060ff-3fa1-4c32-912d-0b5e48844cb4 · outbound

This paper cites A survey on deep active learning: Recent advances and new fron- tiers,.

A model-agnostic active learning approach for animal detection from camera traps A survey on deep active learning: Recent advances and new fron- tiers,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:02.499990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3a25a3e5-4726-458b-85a5-67f5caf22ade · outbound

This paper cites Plug and play active learning for object detection,.

A model-agnostic active learning approach for animal detection from camera traps Plug and play active learning for object detection,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:02.479844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 447801b8-eaf4-4188-abe0-51bf280309f1 · outbound

This paper cites Employing feature mixture for active learn- ing of object detection,.

A model-agnostic active learning approach for animal detection from camera traps Employing feature mixture for active learn- ing of object detection,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:02.443217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 687868f2-42cd-488b-9b93-e22df5f9cb18 · outbound

This paper cites SecretGen: Privacy recovery on pre-trained models via distribution discrimination,.

A model-agnostic active learning approach for animal detection from camera traps SecretGen: Privacy recovery on pre-trained models via distribution discrimination,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:02.414618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4518bb67-d479-4373-92c7-0858fbb8ab4d · outbound

This paper cites Simple copy-paste is a strong data augmenta- tion method for instance segmentation,.

A model-agnostic active learning approach for animal detection from camera traps Simple copy-paste is a strong data augmenta- tion method for instance segmentation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:02.389708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c7bae121-8854-4f76-844c-74b3efc35d6c · outbound

This paper cites Active learning for deep object de- tection,.

A model-agnostic active learning approach for animal detection from camera traps Active learning for deep object de- tection,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:02.368107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b6082b21-a81f-4806-9ddb-9d9c8bd142e1 · outbound

This paper cites Deep active learning for object detection.,.

A model-agnostic active learning approach for animal detection from camera traps Deep active learning for object detection.,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:02.346032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4d1b92bf-e591-4880-a109-0db9e00b1493 · outbound

This paper cites Instance-aware uncertainty for active learning in object detection,.

A model-agnostic active learning approach for animal detection from camera traps Instance-aware uncertainty for active learning in object detection,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:02.321516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 295e4fe0-854f-4b33-849c-3424b6f58d77 · outbound

This paper cites Entropy-based active learning for object detection with progressive di- versity constraint,.

A model-agnostic active learning approach for animal detection from camera traps Entropy-based active learning for object detection with progressive di- versity constraint,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:02.289730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4c016033-7446-4047-980e-7e367530fbd3 · outbound

This paper cites Agnostic ac- tive learning of single index models with linear sample complexity,.

A model-agnostic active learning approach for animal detection from camera traps Agnostic ac- tive learning of single index models with linear sample complexity,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:02.257104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4b3097d0-bb75-472a-ba18-ec08c414c492 · outbound

This paper cites Faster R-CNN: towards real-time object detection with region proposal networks,.

A model-agnostic active learning approach for animal detection from camera traps Faster R-CNN: towards real-time object detection with region proposal networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:02.237355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8236c912-5aa1-480c-882c-b06baf70e668 · outbound

This paper cites YOLO9000: better, faster, stronger,.

A model-agnostic active learning approach for animal detection from camera traps YOLO9000: better, faster, stronger,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:02.214925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f797891c-507e-4a86-9987-a75fcb2165f1 · outbound

This paper cites an unresolved cited work.

A model-agnostic active learning approach for animal detection from camera traps Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:05:02.195131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7a7e288b-3969-4d7b-a80b-44a11e534a5e · outbound

This paper cites SAWIT: A small-sized animal wild image dataset with annotations,.

A model-agnostic active learning approach for animal detection from camera traps SAWIT: A small-sized animal wild image dataset with annotations,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:02.177120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4ac2f2e2-a1d4-43dc-805b-c0bef7af059e · outbound

This paper cites Ultra- lytics yolov8,.

A model-agnostic active learning approach for animal detection from camera traps Ultra- lytics yolov8,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T19:05:02.048300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 213587bb-2d62-4e05-a2ee-1b846309d780 · inbound