Paper Citation Record · LEDGER

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species

As of 21 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2601.03729.

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

pith.paper-citation-record.v1
2601.03729 v4

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T12:16:24.069775Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-25T19:55:44.980016Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-04T20:40:07.896395Z

Reference resolution

39 of 39 outbound references displayed

verified exact0
verified fuzzy0
unresolved39
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation be77280d-6fe7-43a3-a40e-13f4fa5a1599 · outbound

This paper cites Correct species identifi- cation and its implications for conservation using haploniscidae (crustacea, isopoda) in icelandic waters as a proxy.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Correct species identifi- cation and its implications for conservation using haploniscidae (crustacea, isopoda) in icelandic waters as a proxy

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:20.043763Z

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Unavailable: canonical work link unavailable.

Observation 0857a3fd-dc50-4224-8203-7ea663381608 · outbound

This paper cites Accelerating species recognition and labelling of fish from underwater video with machine-assisted deep learning.Frontiers in Marine Science, V olume 9 - 2022, 2022.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Accelerating species recognition and labelling of fish from underwater video with machine-assisted deep learning.Frontiers in Marine Science, V olume 9 - 2022, 2022

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:20.103403Z

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Observation dda80022-2f4c-4224-94e5-872458584985 · outbound

This paper cites Identification crisis: a fauna-wide estimate of biodiversity expertise shows massive decline in a central european country.Biodiversity and Conservation, 33(13):3871–3903, 2024.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Identification crisis: a fauna-wide estimate of biodiversity expertise shows massive decline in a central european country.Biodiversity and Conservation, 33(13):3871–3903, 2024

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:20.163092Z

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Observation 04c0f076-f813-4f7d-be0a-6ea9ee27c912 · outbound

This paper cites Recent advances of machine vision technology in fish classification.ICES Journal of Marine Science, 79(2):263–284, 2022.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Recent advances of machine vision technology in fish classification.ICES Journal of Marine Science, 79(2):263–284, 2022

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:20.243185Z

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Observation 0ca99ea9-a070-4ddd-9917-95aac11785c3 · outbound

This paper cites Computer vision and deep learning for fish classification in underwater habitats: A survey.Fish and Fisheries, 23(4):977–999, 2022.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Computer vision and deep learning for fish classification in underwater habitats: A survey.Fish and Fisheries, 23(4):977–999, 2022

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:20.306693Z

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Observation d4ca45c8-9f0a-4117-96e9-2d63215133c5 · outbound

This paper cites Varalakshmi and J.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Varalakshmi and J

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:20.361459Z

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Observation 175079c2-588e-4656-ba89-7fc161bfc3ee · outbound

This paper cites Fish detection and species clas- sification in underwater environments using deep learning with temporal information.Ecological Informatics, 57:101088, 2020.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Fish detection and species clas- sification in underwater environments using deep learning with temporal information.Ecological Informatics, 57:101088, 2020

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:20.414267Z

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Observation 42ed4f94-4ba0-4af2-8983-9b6a9a873366 · outbound

This paper cites Deepfish: Accurate underwater live fish recognition with a deep architecture.Neurocomputing, 187:49–58, 2016.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Deepfish: Accurate underwater live fish recognition with a deep architecture.Neurocomputing, 187:49–58, 2016

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:20.470903Z

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Observation 29db0ff7-b151-4338-a151-7726a62af5d5 · outbound

This paper cites an unresolved cited work.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:20.530402Z

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Observation cf238cd0-d169-48aa-bb24-1f5cf53f1e66 · outbound

This paper cites an unresolved cited work.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Unresolved cited work

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:20.582756Z

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Observation 304a901b-9540-4dc3-807e-e3b58e1ace47 · outbound

This paper cites Fish-vista: A multi-purpose dataset for understanding & identification of traits from images.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Fish-vista: A multi-purpose dataset for understanding & identification of traits from images

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:20.627349Z

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Observation d4c6420b-b603-4f2f-b662-00906b46643b · outbound

This paper cites FathomNet: An underwater image training database for ocean exploration and discovery.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species FathomNet: An underwater image training database for ocean exploration and discovery

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:20.712298Z

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Observation 4c860d1d-95e8-4969-ab8b-31697a88ec9f · outbound

This paper cites Fine-grained representation learning and recognition by exploiting hierarchical semantic embedding.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Fine-grained representation learning and recognition by exploiting hierarchical semantic embedding

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:20.797500Z

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Observation e0183eb7-a728-4d97-8ad1-e71bfac0b51e · outbound

This paper cites Convfishnet: An efficient backbone for fish classification from composited underwater images.Information Sciences, 679:121078, 2024.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Convfishnet: An efficient backbone for fish classification from composited underwater images.Information Sciences, 679:121078, 2024

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:20.852141Z

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Unavailable: canonical work link unavailable.

Observation 7483b5a6-281e-4f3f-84a4-9d4a43bcd6a2 · outbound

This paper cites Duet of vit and cnn: multi-scale dual-branch network for fine-grained image classification of marine organisms.Intelligent Marine Technology and Systems, 2(1):1, 2024.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Duet of vit and cnn: multi-scale dual-branch network for fine-grained image classification of marine organisms.Intelligent Marine Technology and Systems, 2(1):1, 2024

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:20.929941Z

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Observation 4688eea5-5d0d-42bb-82e9-345b5bb8a432 · outbound

This paper cites an unresolved cited work.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Unresolved cited work

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:21.010201Z

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Unavailable: canonical work link unavailable.

Observation e0e7bb45-2e26-4ee7-99fc-33f374f83d93 · outbound

This paper cites Swinfishnet: A swin transformer-based approach for automatic fish species classification using transfer learning.PLOS ONE, 20(5):e0322711, 2025.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Swinfishnet: A swin transformer-based approach for automatic fish species classification using transfer learning.PLOS ONE, 20(5):e0322711, 2025

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:21.142071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2a498de-26c5-474f-b5d1-0c2d01d5d805 · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:21.254648Z

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Observation 4c319e2e-307a-49e3-93e8-3bb029cc128f · outbound

This paper cites Fathomnet 2025 @ cvpr-fgvc, 2025.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Fathomnet 2025 @ cvpr-fgvc, 2025

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:21.385627Z

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Unavailable: canonical work link unavailable.

Observation aacbc659-88b7-4dd1-8a8d-a59dd4c1bdb5 · outbound

This paper cites Fair1m: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery.ISPRS Journal of Photogrammetry and Remote Sensing, 184:116–130, 2022.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Fair1m: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery.ISPRS Journal of Photogrammetry and Remote Sensing, 184:116–130, 2022

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:21.531203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c53e646-efed-42b8-aa1e-677fb370c7b4 · outbound

This paper cites Lifeclef 2015: Multimedia life species identification challenges.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Lifeclef 2015: Multimedia life species identification challenges

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:21.706048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d7bbd74-e4ba-4a73-bdec-fb0ff86a01b7 · outbound

This paper cites Fish recognition based on robust features extraction from size and shape measurements using neural network.Journal of Computer Science, 6(10), 2010.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Fish recognition based on robust features extraction from size and shape measurements using neural network.Journal of Computer Science, 6(10), 2010

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:21.851556Z

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Unavailable: canonical work link unavailable.

Observation df5f53e4-2fb0-4d79-9bb9-fbf20edac86c · outbound

This paper cites Two-stream contextualized cnn for fine-grained image classification, 2016.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Two-stream contextualized cnn for fine-grained image classification, 2016

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:21.978500Z

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Observation 2276e30b-f155-4704-bbe3-fc1d059f85c1 · outbound

This paper cites an unresolved cited work.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Unresolved cited work

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:22.122788Z

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Observation 750af6a7-1e8c-4cc5-b0d3-048f52fc991c · outbound

This paper cites Deep-hipo: Multi-scale receptive field deep learning for histopathological image analysis.Methods, 179:3–13, 2020.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Deep-hipo: Multi-scale receptive field deep learning for histopathological image analysis.Methods, 179:3–13, 2020

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:22.240979Z

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Observation 7d2e36c5-b42d-4383-bc0a-2096c078d544 · outbound

This paper cites Silla and Alex A.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Silla and Alex A

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:22.355406Z

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Observation 9405b11b-2f39-4def-96ec-580bfc4ba998 · outbound

This paper cites Attribute hierarchy based multi-task learning for fine-grained image classification.Neurocomputing, 395:150–159, 2020.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Attribute hierarchy based multi-task learning for fine-grained image classification.Neurocomputing, 395:150–159, 2020

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:22.438249Z

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Unavailable: canonical work link unavailable.

Observation 62bde41a-88ee-4900-8238-cdca97895c03 · outbound

This paper cites an unresolved cited work.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Unresolved cited work

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:22.547611Z

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Observation 59895c4b-6ec5-461b-83b8-949ef1b5b852 · outbound

This paper cites Fathomnet: A global image database for enabling artificial intelligence in the ocean.Scientific reports, 12(1):15914, 2022.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Fathomnet: A global image database for enabling artificial intelligence in the ocean.Scientific reports, 12(1):15914, 2022

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:22.704100Z

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Observation 534e6d7a-5fb0-42c4-9967-431491b7ac82 · outbound

This paper cites A simple interpretable transformer for fine-grained image classification and analysis.Nature Communications, 15(1):3546, 2024.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species A simple interpretable transformer for fine-grained image classification and analysis.Nature Communications, 15(1):3546, 2024

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:22.872345Z

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Observation 55c34ded-d2d8-40d0-9771-d93436af37cf · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:23.038857Z

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Observation ceeea426-5f6f-4984-bc47-73711692d674 · outbound

This paper cites Deep residual learning for image recognition.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Deep residual learning for image recognition

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:23.172888Z

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Observation ee96f50a-bde2-4fa1-900b-fae8b90da788 · outbound

This paper cites Aggregated residual transformations for deep neural networks.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Aggregated residual transformations for deep neural networks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:23.255789Z

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Observation a268be67-d098-42cc-a599-dc9e53b1dc4f · outbound

This paper cites Maxvit: Multi-axis vision transformer, 2022.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Maxvit: Multi-axis vision transformer, 2022

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:23.392559Z

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Observation 9ef652b6-211a-45aa-b55b-d731b0fee0ed · outbound

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

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species DINOv2: Learning Robust Visual Features without Supervision

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:23.506347Z

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Observation 887b1830-de86-4d69-8aa4-4f5a3a54dce5 · outbound

This paper cites Transfg: A transformer architecture for fine-grained recognition.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Transfg: A transformer architecture for fine-grained recognition

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:23.692432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2723a563-8205-49a0-ac0d-2857b29b1e64 · outbound

This paper cites Use all the labels: A hierarchical multi-label contrastive learning framework.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Use all the labels: A hierarchical multi-label contrastive learning framework

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:23.844238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1da87c9-a76b-4446-8157-f99ebeb1b259 · outbound

This paper cites Bertinetto, R.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Bertinetto, R

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:24.001677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6b8ae52-920b-48df-b67a-79a761c19430 · outbound

This paper cites Visualizing data using t-sne.Journal of machine learning research, 9(Nov):2579–2605, 2008.

MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species Visualizing data using t-sne.Journal of machine learning research, 9(Nov):2579–2605, 2008

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-03T12:16:24.069775Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation 7021d34b-3351-4e1e-a0c1-793e3c23ba17 · inbound

Taxonomy-aware deep learning for hierarchical marine species classification in underwater imagery cites this paper.

Taxonomy-aware deep learning for hierarchical marine species classification in underwater imagery MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-07-04T20:40:07.897684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.