Paper Citation Record · LEDGER

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery

As of 14 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2509.06660.

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pith.paper-citation-record.v1
2509.06660 v1

Coverage vector

measured 30 of 30 reference resolution

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measured 30 of 30 standing notices

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

30 of 30 outbound references displayed

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External citation measurements

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Outbound references

Observation 24788c68-ad5a-4444-8e82-10f490c959d0 · outbound

This paper cites Monitoring of benthic reference sites: Using an autonomous underwater vehicle,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery Monitoring of benthic reference sites: Using an autonomous underwater vehicle,

Reference 1

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Observation 15dc3e6c-e54f-41cb-a13e-97b1d7916aa7 · outbound

This paper cites A survey on contrastive self-supervised learning,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery A survey on contrastive self-supervised learning,

Reference 2

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Observation e60bbb10-b4d3-4a3b-ae25-675682981446 · outbound

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

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery A simple framework for contrastive learning of visual representations,

Reference 3

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Observation f5ab8c1b-ddbc-4fb5-8ed3-fe59834cb6f7 · outbound

This paper cites ugel-Bennett, and B. Thornton, “Learning features from georeferenced seafloor imagery with location guided autoen- coders,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery ugel-Bennett, and B. Thornton, “Learning features from georeferenced seafloor imagery with location guided autoen- coders,

Reference 4

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This paper cites ugel-Bennett, S. B. Williams, O. Pizarro, and B. Thornton, “Geoclr: Georeference contrastive learning for efficient seafloor image interpretation,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery ugel-Bennett, S. B. Williams, O. Pizarro, and B. Thornton, “Geoclr: Georeference contrastive learning for efficient seafloor image interpretation,

Reference 5

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Observation 5e247048-7c4d-47ed-8052-c27b77147a01 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery Momentum contrast for unsupervised visual representation learning,

Reference 6

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Observation 8bc377fa-151b-487e-a23a-6409a6b25e7b · outbound

This paper cites Self-supervised learning with data augmentations provably isolates content from style,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery Self-supervised learning with data augmentations provably isolates content from style,

Reference 7

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Observation 485aead5-9d41-4f3d-b830-3c09d886eb29 · outbound

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

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery Emerging properties in self-supervised vision transformers,

Reference 8

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Observation 790962a4-7850-4819-aaa3-c81448fc1047 · outbound

This paper cites Guiding labelling effort for efficient learn- ing with georeferenced images,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery Guiding labelling effort for efficient learn- ing with georeferenced images,

Reference 9

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Observation 9f38eff5-2e80-46e9-9904-46700baf2dbb · outbound

This paper cites Assessing the repeatability of automated seafloor classification algorithms, with application in marine protected area monitoring,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery Assessing the repeatability of automated seafloor classification algorithms, with application in marine protected area monitoring,

Reference 10

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Observation 3a795e86-333c-4403-a3bb-0f3062f93ad7 · outbound

This paper cites Exploring simple siamese representation learning,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery Exploring simple siamese representation learning,

Reference 11

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Observation b82116a1-06e1-4df5-9991-791b11097b41 · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assign- ments,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery Unsupervised learning of visual features by contrasting cluster assign- ments,

Reference 12

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Observation 9cef5bd2-7734-4985-b681-e7982d358155 · outbound

This paper cites Deep clustering for unsupervised learning of visual features,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery Deep clustering for unsupervised learning of visual features,

Reference 13

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Observation 81a574a1-73e9-41c6-932e-ae4f3526513b · outbound

This paper cites A survey on self-supervised learning: Algorithms, applications, and future trends,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery A survey on self-supervised learning: Algorithms, applications, and future trends,

Reference 14

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Observation 55d18dad-4817-4225-a259-1478b8846ae7 · outbound

This paper cites BERT: Pre- training of deep bidirectional transformers for language understanding,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery BERT: Pre- training of deep bidirectional transformers for language understanding,

Reference 15

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Observation f2edfa42-0257-4766-a109-11c94e30a569 · outbound

This paper cites GPT-4 Technical Report.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery GPT-4 Technical Report

Reference 16

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Observation 7fe02e53-4996-4951-888e-ab02ae4f91e6 · outbound

This paper cites Self-supervised learning: Generative or contrastive,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery Self-supervised learning: Generative or contrastive,

Reference 17

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This paper cites Advancing surface defect detection: A review of self- supervised learning approaches,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery Advancing surface defect detection: A review of self- supervised learning approaches,

Reference 18

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Observation 42b9271c-b0b0-4ec6-a4e4-464f3c73874c · outbound

This paper cites Concerning nonnegative matrices and doubly stochastic matrices,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery Concerning nonnegative matrices and doubly stochastic matrices,

Reference 19

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Observation 5709b809-1e17-4271-b264-af5ea756a584 · outbound

This paper cites DenseDINO: Boosting Dense Self-Supervised Learning with Token-Based Point-Level Consistency.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery DenseDINO: Boosting Dense Self-Supervised Learning with Token-Based Point-Level Consistency

Reference 20

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Observation 18fa7d9c-29f6-4fb5-9a89-53a248e5c5c5 · outbound

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

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery Bootstrap your own latent: A new approach to self-supervised learning,

Reference 21

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Observation 0b753110-82a9-4d4d-a7ed-132fa4c1764e · outbound

This paper cites Transformers in vision: A survey,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery Transformers in vision: A survey,

Reference 22

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This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery DINOv2: Learning Robust Visual Features without Supervision

Reference 23

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Observation e51e84bd-3d7d-4431-861e-d79b5c90009d · outbound

This paper cites Unsupervised learning of dense visual representations,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery Unsupervised learning of dense visual representations,

Reference 24

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This paper cites Generation and visualization of large-scale three-dimensional reconstructions from underwater robotic surveys,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery Generation and visualization of large-scale three-dimensional reconstructions from underwater robotic surveys,

Reference 25

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Observation 92296d1e-6c86-4fae-923e-4a22a405f9e3 · outbound

This paper cites Self-supervised learning with multimodal remote sensed maps for seafloor visual class inference,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery Self-supervised learning with multimodal remote sensed maps for seafloor visual class inference,

Reference 26

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This paper cites Leveraging spatial metadata in machine learning for improved objective quantification of geological drill core,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery Leveraging spatial metadata in machine learning for improved objective quantification of geological drill core,

Reference 27

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Observation a83e598e-122b-4a65-9e9c-a29129562585 · outbound

This paper cites 3D CNN-PCA: A deep-learning-based parameterization for complex geomodels,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery 3D CNN-PCA: A deep-learning-based parameterization for complex geomodels,

Reference 28

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This paper cites Compact DINO- ViT: Feature reduction for visual transformer,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery Compact DINO- ViT: Feature reduction for visual transformer,

Reference 29

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This paper cites Pytorch: An imperative style, high-performance deep learning library,.

Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery Pytorch: An imperative style, high-performance deep learning library,

Reference 30

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Pith citing papers

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