Paper Citation Record · LEDGER

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results

As of 18 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2508.18445.

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

pith.paper-citation-record.v1
2508.18445 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:30:19.110374Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:30:18.921542Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T16:30:19.257074Z

Reference resolution

34 of 34 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3bc63959-bfa6-4601-a96a-f14a1ac61707 · outbound

This paper cites VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T16:30:19.263705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3b27105d-d3bd-4ba3-b853-65cdc5df8fbf · outbound

This paper cites Blind image quality assessment using natural scene statistics.IEEE Transactions on Image Processing, 23(1):310–325, 2014.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Blind image quality assessment using natural scene statistics.IEEE Transactions on Image Processing, 23(1):310–325, 2014

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:30:19.881707Z

Source-reported events for the cited work

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Observation 9c68e975-adc3-42a2-aad1-b66a8678ff62 · outbound

This paper cites Progressive Growing of GANs for Improved Quality, Stability, and Variation.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Progressive Growing of GANs for Improved Quality, Stability, and Variation

Reference 15

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Observation 603732e6-528e-452d-bc5e-556688a7f72e · outbound

This paper cites Adam: A method for stochastic optimization.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Adam: A method for stochastic optimization

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:30:19.853083Z

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Observation a0c3a2ad-ddbc-47fe-8969-b0ca966ff4a7 · outbound

This paper cites Imagenet classification with deep convolutional neural net- works.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Imagenet classification with deep convolutional neural net- works

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:30:19.831871Z

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Observation 6e720300-5f62-4eae-976a-c89ce3f4433d · outbound

This paper cites Vquala 2025 challenge on engagement prediction for short videos: Methods and results.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Vquala 2025 challenge on engagement prediction for short videos: Methods and results

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:30:19.797428Z

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Observation baf8f2c4-361b-45e2-a39b-a0e2c3e94357 · outbound

This paper cites Vquala 2025 challenge on image super-resolution generated content qual- ity assessment: Methods and results.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Vquala 2025 challenge on image super-resolution generated content qual- ity assessment: Methods and results

Reference 19

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Observation d9dcd501-ea0e-4bb0-8142-adbff50f9d07 · outbound

This paper cites Ntire 2023 quality assess- ment of video enhancement challenge.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Ntire 2023 quality assess- ment of video enhancement challenge

Reference 20

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Observation 9c112d29-5c9d-43fb-8fb8-d4e6a3c2802e · outbound

This paper cites Vmamba: Visual state space model.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Vmamba: Visual state space model

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:30:19.723242Z

Source-reported events for the cited work

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Observation bbbc128f-0602-4b43-bba9-dd11263165bc · outbound

This paper cites Deep learning face attributes in the wild.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Deep learning face attributes in the wild

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:30:19.695287Z

Source-reported events for the cited work

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Observation f93dd18f-aa34-4b5f-8e74-a2e5dea0704d · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Swin transformer: Hierarchical vision transformer using shifted windows

Reference 23

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Observation 79ff33f4-f1ef-4635-a556-9e6b07cae3ac · outbound

This paper cites Sgdr: Stochastic gradient descent with restarts.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Sgdr: Stochastic gradient descent with restarts

Reference 24

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Observation 806806d3-5b4e-4aa2-beb1-21e7fc4c809e · outbound

This paper cites Decoupled weight de- cay regularization.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Decoupled weight de- cay regularization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:30:19.624247Z

Source-reported events for the cited work

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Observation 8bf4e381-b237-45d8-a72d-b667cb9ce4da · outbound

This paper cites Shufflenet v2: Practical guidelines for efficient cnn architec- ture design.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Shufflenet v2: Practical guidelines for efficient cnn architec- ture design

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:30:19.601997Z

Source-reported events for the cited work

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Observation 45218cdf-19a1-42b9-b4e5-27c8d316dd0c · outbound

This paper cites Edgenext: efficiently amalgamated cnn-transformer architecture for mobile vision applications.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Edgenext: efficiently amalgamated cnn-transformer architecture for mobile vision applications

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:30:19.580302Z

Source-reported events for the cited work

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Observation 9945baf0-c619-4a58-96b9-f963c905ff4e · outbound

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

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results No-reference image quality assessment in spatial domain

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:30:19.557016Z

Source-reported events for the cited work

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Observation 7f020e5b-b6a5-42e5-9dfa-8081fbaa69d1 · outbound

This paper cites Improving road defect detection precision and efficiency with structural pruning techniques.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Improving road defect detection precision and efficiency with structural pruning techniques

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:30:19.536105Z

Source-reported events for the cited work

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Observation 72b69339-dde2-4877-bb1e-98a9e2cb4db6 · outbound

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

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Pytorch: An imperative style, high-performance deep learning library

Reference 30

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Observation 8883faea-107e-4eb1-9ace-3f2b75265f4e · outbound

This paper cites Mobilenetv4: universal models for the mobile ecosystem.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Mobilenetv4: universal models for the mobile ecosystem

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:30:19.500095Z

Source-reported events for the cited work

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Observation e61660c5-7d6c-4382-a1a9-2f13d0997e0e · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Learning transferable visual models from natural language supervi- sion

Reference 32

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Observation 6dae8107-3772-4c11-a212-023701aff783 · outbound

This paper cites Designing network design spaces.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Designing network design spaces

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:30:19.469408Z

Source-reported events for the cited work

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Observation 1cf8afa0-dffb-44d8-b74c-8cb1a5cb2922 · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 34

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Observation cbdb1ba2-bef5-4217-aee3-a8948aa538da · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 35

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Observation d0f22094-b790-462d-a45d-027497b5249f · outbound

This paper cites Going the extra mile in face image quality assess- ment: A novel database and model.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Going the extra mile in face image quality assess- ment: A novel database and model

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:30:19.402679Z

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Observation 9ab83908-ef7b-4e27-ab8f-6c9079a991ad · outbound

This paper cites Ef- ficient face image quality assessment via self-training and knowledge distillation.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Ef- ficient face image quality assessment via self-training and knowledge distillation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:30:19.386122Z

Source-reported events for the cited work

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Observation b8189694-1d1c-4f9f-be6e-2614cb7fdaae · outbound

This paper cites A strong baseline for image and video quality assessment.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results A strong baseline for image and video quality assessment

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:30:19.212612Z

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This paper cites Pytorch image models (timm).

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Pytorch image models (timm)

Reference 39

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Observation 855f4005-226c-4a3b-9c75-c617e6ed462b · outbound

This paper cites MSPT: A Lightweight Face Image Quality Assessment Method with Multi-stage Progressive Training.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results MSPT: A Lightweight Face Image Quality Assessment Method with Multi-stage Progressive Training

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:30:19.187949Z

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Observation 0616b919-c5b5-4da4-9e73-7386d463c223 · outbound

This paper cites Self-training with noisy student improves imagenet clas- sification.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Self-training with noisy student improves imagenet clas- sification

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:30:19.350352Z

Source-reported events for the cited work

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Observation b6f569ee-85f3-4bca-98f9-c6b4198cf0e5 · outbound

This paper cites Rethinking mobile block for ef- ficient attention-based models.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Rethinking mobile block for ef- ficient attention-based models

Reference 42

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Observation fd592c2d-bfac-4ee6-98bc-6e25b4ba6797 · outbound

This paper cites EMOv2: Pushing 5M Vision Model Frontier.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results EMOv2: Pushing 5M Vision Model Frontier

Reference 43

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Observation 504345b7-a6e9-4792-873d-a3ab6ff26ae3 · outbound

This paper cites Vquala 2025 challenge on visual quality comparison for large multimodal models: Methods and results.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Vquala 2025 challenge on visual quality comparison for large multimodal models: Methods and results

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:30:19.297761Z

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Observation 8868de10-d8d6-4ca8-b009-05c46704761c · outbound

This paper cites Dynamic convolution-based image dehazing network.

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Dynamic convolution-based image dehazing network

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:30:19.281856Z

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Observation c89f3c1b-0daf-4222-a649-860ae0b7446e · outbound

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VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results Unresolved cited work

Reference 1400

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Source-reported events for the cited work

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Pith citing papers

Observation 3bc63959-bfa6-4601-a96a-f14a1ac61707 · inbound