Paper Citation Record · LEDGER

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning

As of 7 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:1907.07319.

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

pith.paper-citation-record.v1
1907.07319 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-24T20:52:43.001731Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

31 of 31 outbound references displayed

verified exact2
verified fuzzy29
unresolved0
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 73c62204-20c0-4f9d-bdc0-44cf3e282a97 · outbound

This paper cites Unmanned aerial vehicles (UA Vs) for surveying Marine Fauna: A dugong case study.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Unmanned aerial vehicles (UA Vs) for surveying Marine Fauna: A dugong case study

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.776688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 39e26a12-8bce-4e6a-9ad0-32731fb8ff77 · outbound

This paper cites Spotting East African mammals in open savannah from space.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Spotting East African mammals in open savannah from space

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.773905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aa5acf4f-c7f7-4657-be60-c9a72394691d · outbound

This paper cites Distribution and abundance of feral livestock in the ’top end’ of the northern territory (1985-86), and their relation to population control.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Distribution and abundance of feral livestock in the ’top end’ of the northern territory (1985-86), and their relation to population control

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.771135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation eccff705-195e-4bc9-96cb-1bd54584edce · outbound

This paper cites Norton-Grifﬁths, Counting animals.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Norton-Grifﬁths, Counting animals

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.767839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c08b1da9-124f-4fb4-a694-ce69c0ea9797 · outbound

This paper cites Are unmanned aircraft systems (UASs) the future of wildlife monitoring? A review of accomplishments and challenges.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Are unmanned aircraft systems (UASs) the future of wildlife monitoring? A review of accomplishments and challenges

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.764207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b472bdf0-f4ef-4d1a-a3ec-8248b7fa461e · outbound

This paper cites Drones count wildlife more accurately and precisely than humans.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Drones count wildlife more accurately and precisely than humans

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.761217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d4305e7d-3c61-49f1-bd7a-f8638a32ee27 · outbound

This paper cites Detecting animals in African Savanna with UA Vs and the crowds.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Detecting animals in African Savanna with UA Vs and the crowds

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.757965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1c4821a4-764f-4a72-b7df-280e660f60dd · outbound

This paper cites Detecting mammals in UA V images: Best practices to address a substantially imbalanced dataset with deep learning.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Detecting mammals in UA V images: Best practices to address a substantially imbalanced dataset with deep learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.754893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 728c1358-a1df-481e-9d63-156df9fb86fc · outbound

This paper cites Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.748892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 88c548b3-ad9a-4079-9013-2aace00a0df9 · outbound

This paper cites You Only Look Once: Uniﬁed, Real-Time Object Detection.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning You Only Look Once: Uniﬁed, Real-Time Object Detection

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.751968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1e1960d5-cdf9-4f9c-be26-4a07433e22ef · outbound

This paper cites Yolo9000: better, faster, stronger.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Yolo9000: better, faster, stronger

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.817113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4c2f39cf-7d85-48f2-ba99-b175d1220d54 · outbound

This paper cites ImageNet Classiﬁcation with Deep Convolutional Neural Networks.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning ImageNet Classiﬁcation with Deep Convolutional Neural Networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.819839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation daebaf97-560f-464f-a505-1e5e25fc9994 · outbound

This paper cites Deep learning.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Deep learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.822849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 602da518-4a53-457e-a973-dd36e4906dab · outbound

This paper cites Domain adaptation for the classiﬁcation of remote sensing data: An overview of recent advances.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Domain adaptation for the classiﬁcation of remote sensing data: An overview of recent advances

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.825564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d8806ef5-a1d7-428d-81d5-ec571ec3f3a3 · outbound

This paper cites A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classiﬁcation.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classiﬁcation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.828748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cc621088-7911-4c01-8fe8-b8b34fa2b27a · outbound

This paper cites Active learning.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Active learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.811474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0276eeec-67be-410f-9585-3e75d15f4b58 · outbound

This paper cites Active learning to recognize multiple types of plankton.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Active learning to recognize multiple types of plankton

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.814386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d637053d-3bd1-404a-9a23-ad2e925185b2 · outbound

This paper cites Less is more: Active learning with support vector machines.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Less is more: Active learning with support vector machines

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.805728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fca777ad-c861-4b74-83ee-2e26788901d2 · outbound

This paper cites Maximizing expected model change for active learning in regression.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Maximizing expected model change for active learning in regression

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.802458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7a2082f2-c066-459e-869d-950b8e671ec7 · outbound

This paper cites Deep bayesian active learning with image data.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Deep bayesian active learning with image data

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.808711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 01414574-b02a-4c9b-95c2-82f68e4ad419 · outbound

This paper cites One-shot Learning with Memory-Augmented Neural Networks.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning One-shot Learning with Memory-Augmented Neural Networks

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-05-24T20:54:54.636080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2cd14c4a-f61f-4f81-8f73-3f6533166a0f · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Sinkhorn distances: Lightspeed computation of optimal transport

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.793477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5ff3806a-4cce-4eef-bbdd-888b7a567d49 · outbound

This paper cites Visualizing high-dimensional data using t-sne.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Visualizing high-dimensional data using t-sne

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.796098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 723fe605-dce0-4976-b8b6-06569c337bff · outbound

This paper cites Support vector machine.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Support vector machine

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.798822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e1ad7e44-d820-4be4-ab97-e072f01fbe30 · outbound

This paper cites Optimal transport for domain adaptation.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Optimal transport for domain adaptation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.790618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8176707a-4338-46e9-93c5-1eb5110dbbd8 · outbound

This paper cites Combining human computing and machine learning to make sense of big (aerial) data for disaster response.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Combining human computing and machine learning to make sense of big (aerial) data for disaster response

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.782095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4edeaefc-f011-4bfc-8db4-204ec5f2b611 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Deep Residual Learning for Image Recognition

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.784690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 97e0b6e3-d338-4f38-99c7-2878d1d8d8a9 · outbound

This paper cites ImageNet Large Scale Visual Recognition Challenge.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning ImageNet Large Scale Visual Recognition Challenge

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.787900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5bb200dd-b824-468d-ab23-a4f2fd2c0550 · outbound

This paper cites Instance Normalization: The Missing Ingredient for Fast Stylization.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Instance Normalization: The Missing Ingredient for Fast Stylization

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-24T20:54:54.631326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 22cf82ae-4872-4343-bce4-f3d46977274b · outbound

This paper cites Localization-Aware Active Learning for Object Detection.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Localization-Aware Active Learning for Object Detection

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.831759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 600da576-d036-40a2-b4fb-1625969cae18 · outbound

This paper cites Learning user’s conﬁdence for active learning.

Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning Learning user’s conﬁdence for active learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T20:54:55.779270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.