Paper Citation Record · LEDGER

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment

As of 22 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2501.15485.

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

pith.paper-citation-record.v1
2501.15485 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:18:07.143016Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

39 of 39 outbound references displayed

verified exact3
verified fuzzy32
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a4516af3-0ce5-45f8-beac-9a4e2c74675d · outbound

This paper cites A morphing-based 3D point cloud reconstruction framework for medical image processing,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment A morphing-based 3D point cloud reconstruction framework for medical image processing,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.688842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5cc9c9da-a1a1-44fc-ab6f-8415595b26b7 · outbound

This paper cites Point cloud generation using deep local features for augmented and mixed reality contents,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Point cloud generation using deep local features for augmented and mixed reality contents,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.675597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9476f399-9a2d-4989-a6e7-8888f56d4d52 · outbound

This paper cites 3D point cloud processing and learning for autonomous driving: Impacting map creation, localization, and perception,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment 3D point cloud processing and learning for autonomous driving: Impacting map creation, localization, and perception,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.701705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4fe4ec4b-16ed-47c8-a734-d1b7fd984792 · outbound

This paper cites 3d is here: Point cloud library (PCL),.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment 3d is here: Point cloud library (PCL),

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.662406Z

Source-reported events for the cited work

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Observation c60e8841-df27-46a8-a88e-75627825ca96 · outbound

This paper cites Comparison of four subjective methods for image quality assessment,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Comparison of four subjective methods for image quality assessment,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.649147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a0808571-2cbd-4b63-abdb-57e34d1b940d · outbound

This paper cites A survey of DNN methods for blind image quality assessment,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment A survey of DNN methods for blind image quality assessment,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.635608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5d471f15-352f-4133-9a69-6e85572807c3 · outbound

This paper cites Point cloud quality assessment: Dataset construction and learning-based no-reference metric,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Point cloud quality assessment: Dataset construction and learning-based no-reference metric,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.621791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ef87a365-4efe-4f1d-ba20-bfdf732c971c · outbound

This paper cites Reduced reference quality assessment for point cloud compression,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Reduced reference quality assessment for point cloud compression,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.606872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ba2e5879-6898-48c9-8bde-b6350d8fa00f · outbound

This paper cites GMC-IQA: Exploiting Global-correlation and Mean-opinion Consistency for No-reference Image Quality Assessment.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment GMC-IQA: Exploiting Global-correlation and Mean-opinion Consistency for No-reference Image Quality Assessment

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:18:07.251654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a7643c67-705c-4664-9218-b1c02695c08b · outbound

This paper cites MM-PCQA: Multi-modal learning for no-reference point cloud quality assessment,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment MM-PCQA: Multi-modal learning for no-reference point cloud quality assessment,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.592528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation aa85fbea-268d-4596-a2a5-01cfe33d199f · outbound

This paper cites A deep learning based no- reference quality assessment model for ugc videos,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment A deep learning based no- reference quality assessment model for ugc videos,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.579191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 293368e8-c6e2-4d16-8f4a-b4e47a9ff452 · outbound

This paper cites Novel no-reference image quality assessment metric based on joint relative features,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Novel no-reference image quality assessment metric based on joint relative features,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.565239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2419e0e1-d6aa-4807-8959-72fac43e6513 · outbound

This paper cites RankIQA: Learning from rankings for no-reference image quality assessment,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment RankIQA: Learning from rankings for no-reference image quality assessment,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.551636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7114520a-c323-4060-8e15-ba5f7f3938c3 · outbound

This paper cites Once-Training-All-Fine: No-Reference Point Cloud Quality Assessment via Domain-relevance Degradation Description.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Once-Training-All-Fine: No-Reference Point Cloud Quality Assessment via Domain-relevance Degradation Description

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T14:18:07.037834Z

Source-reported events for the cited work

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Observation 83204749-88c9-457d-bea9-da9856705bf6 · outbound

This paper cites Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:18:07.219014Z

Source-reported events for the cited work

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Observation 6bd40d05-03cd-458a-9f53-1fdc5b85d6b6 · outbound

This paper cites No-reference image quality assessment in the spatial domain,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment No-reference image quality assessment in the spatial domain,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T14:18:07.047435Z

Source-reported events for the cited work

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Observation fcb81fdd-9d2b-41e5-9fbb-b1f66b7f866f · outbound

This paper cites A feature-enriched completely blind image quality evaluator,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment A feature-enriched completely blind image quality evaluator,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T14:18:07.051379Z

Source-reported events for the cited work

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Observation 805be010-2db8-4d82-859b-5b04021c8a86 · outbound

This paper cites Blind image quality assessment based on high order statistics aggregation,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Blind image quality assessment based on high order statistics aggregation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.520733Z

Source-reported events for the cited work

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Observation e2cbd90f-841b-4e8a-9c92-115d8eb1e0e8 · outbound

This paper cites Fully deep blind image quality predictor,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Fully deep blind image quality predictor,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.506694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2927940f-8184-4044-af72-3377515a6a9f · outbound

This paper cites Blind image quality assessment using a deep bilinear convolutional neural network,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Blind image quality assessment using a deep bilinear convolutional neural network,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.492882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e60ee2fd-131b-462f-8209-5d3682c84da1 · outbound

This paper cites Which has better visual quality: The clear blue sky or a blurry animal?.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Which has better visual quality: The clear blue sky or a blurry animal?

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.479434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e960c731-6769-4728-bdcf-f66ad3100e89 · outbound

This paper cites Deep cnn-based blind image quality predictor,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Deep cnn-based blind image quality predictor,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.466402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0e4e3a34-6a07-4c14-a402-1bef918f9b34 · outbound

This paper cites Hallucinated-iqa: No-reference image quality assessment via adversarial learning,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Hallucinated-iqa: No-reference image quality assessment via adversarial learning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.453552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4044bb99-7910-4e63-9a8d-7a307edc067a · outbound

This paper cites Blind predicting similar quality map for image quality assessment,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Blind predicting similar quality map for image quality assessment,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.440688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4c72b38a-e4f8-412b-ab71-a1d01ca48dc6 · outbound

This paper cites dipIQ: Blind image quality assessment by learning-to-rank discriminable image pairs,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment dipIQ: Blind image quality assessment by learning-to-rank discriminable image pairs,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.426668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 88d2a144-8954-4568-b153-728e250a606f · outbound

This paper cites Blindly assess image quality in the wild guided by a self-adaptive hyper network,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Blindly assess image quality in the wild guided by a self-adaptive hyper network,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.413595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c66f618c-88c2-4421-b711-f04c0a37200e · outbound

This paper cites Point cloud projection and multi-scale feature fusion network based blind quality assessment for colored point clouds,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Point cloud projection and multi-scale feature fusion network based blind quality assessment for colored point clouds,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.400822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 983fdf6c-50bc-466f-b63a-152352463cde · outbound

This paper cites PQA-Net: Deep no reference point cloud quality assessment via multi-view projection,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment PQA-Net: Deep no reference point cloud quality assessment via multi-view projection,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.387449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 08cf7ef0-05a9-4ff5-b680-b268e4df97df · outbound

This paper cites No-reference point cloud quality assessment via domain adaptation,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment No-reference point cloud quality assessment via domain adaptation,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.374649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8d364533-6d9f-4bb9-b64a-b61cc2660538 · outbound

This paper cites A no-reference quality assessment metric for point cloud based on captured video sequences,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment A no-reference quality assessment metric for point cloud based on captured video sequences,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.361448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 923df555-3063-4621-96eb-7aa60dfe2088 · outbound

This paper cites Evaluating Point Cloud from Moving Camera Videos: A No-Reference Metric.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Evaluating Point Cloud from Moving Camera Videos: A No-Reference Metric

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:18:07.199007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2744fb2d-bf9b-4d45-a15f-3e784400f8b1 · outbound

This paper cites GPA-Net: No-reference point cloud quality assessment with multi-task graph convolutional network,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment GPA-Net: No-reference point cloud quality assessment with multi-task graph convolutional network,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.348237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b768d4cf-7e65-435f-866d-9d961e677645 · outbound

This paper cites Most apparent distortion: full- reference image quality assessment and the role of strategy,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Most apparent distortion: full- reference image quality assessment and the role of strategy,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.334996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation baf03f4f-e438-46b1-8c26-4a440ddb57d0 · outbound

This paper cites A statistical evaluation of recent full reference image quality assessment algorithms,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment A statistical evaluation of recent full reference image quality assessment algorithms,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.320705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 12820994-ae93-4b66-9c00-37c515d21c76 · outbound

This paper cites Predicting the perceptual quality of point cloud: A 3d-to-2d projection-based exploration,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Predicting the perceptual quality of point cloud: A 3d-to-2d projection-based exploration,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.306416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7fcc5cec-191a-49a9-8a77-d3e2bfe15f03 · outbound

This paper cites Perceptual quality assessment of 3D point clouds,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Perceptual quality assessment of 3D point clouds,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.292853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0ca6af29-b70d-49f0-a3ce-6980850b732d · outbound

This paper cites Perceptual quality assessment of colored 3D point clouds,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Perceptual quality assessment of colored 3D point clouds,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.278646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8a847e56-d474-44d0-9774-9ebff6f2472a · outbound

This paper cites Deep neural networks for no-reference and full-reference image quality assessment,.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment Deep neural networks for no-reference and full-reference image quality assessment,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:07.265511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f6a6dcb9-0649-4980-833f-9933617d6089 · outbound

This paper cites A Probabilistic Quality Representation Approach to Deep Blind Image Quality Prediction.

Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment A Probabilistic Quality Representation Approach to Deep Blind Image Quality Prediction

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T14:18:07.143016Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.