Paper Citation Record · LEDGER

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor

As of 22 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 1 inbound Pith citation observation for arXiv:2505.03261.

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

pith.paper-citation-record.v1
2505.03261 v1

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:00:38.614780Z

measured 71 of 71 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 2026-08-05T11:17:54.452860Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T11:17:54.868705Z

Reference resolution

70 of 70 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9cf9c392-92c7-4cbc-93ff-7a153b2479a9 · outbound

This paper cites Vivit: A video vi- sion transformer.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Vivit: A video vi- sion transformer

Reference 1

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Observation 408af9a4-5537-403e-b6cf-980bcedded37 · outbound

This paper cites Blended diffusion for text-driven editing of natural images.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Blended diffusion for text-driven editing of natural images

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.626583Z

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Observation f002d58b-dcc3-45a3-80b9-d5cf7a0b8c8e · outbound

This paper cites Learning generalized spatial-temporal deep feature representation for no-reference video quality as- sessment.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Learning generalized spatial-temporal deep feature representation for no-reference video quality as- sessment

Reference 3

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Observation d3eb5eb7-614c-4160-a4a5-8350bef4925f · outbound

This paper cites UniRestore: Unified Perceptual and Task-Oriented Image Restoration Model Using Diffusion Prior.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor UniRestore: Unified Perceptual and Task-Oriented Image Restoration Model Using Diffusion Prior

Reference 4

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Observation dc479f3b-a255-4818-bca9-3c13f312e98c · outbound

This paper cites Con- trolstyle: Text-driven stylized image generation using diffu- sion priors.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Con- trolstyle: Text-driven stylized image generation using diffu- sion priors

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.596834Z

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Observation ad2e2b10-de08-4022-99ad-5f61064d7f51 · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T00:00:38.327925Z

Source-reported events for the cited work

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Observation d8a81e36-988d-4666-9eaa-25c119d3c068 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T00:00:38.333210Z

Source-reported events for the cited work

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Observation 40c68812-079c-464b-9bb9-b6adfa16d519 · outbound

This paper cites Konvid-150k: A dataset for no-reference video qual- ity assessment of videos in-the-wild.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Konvid-150k: A dataset for no-reference video qual- ity assessment of videos in-the-wild

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.582091Z

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Observation 747c9606-31ec-41f3-9569-18525f340da0 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 9

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Source-reported events for the cited work

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Observation 84979348-d816-4555-b378-cc93effd75cf · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Efficiently Modeling Long Sequences with Structured State Spaces

Reference 10

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Source-reported events for the cited work

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Observation bf64fa4e-4f4b-492a-9618-8c42681cfbf3 · outbound

This paper cites Combining recurrent, convolutional, and continuous-time models with linear state space layers.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Combining recurrent, convolutional, and continuous-time models with linear state space layers

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.565879Z

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Observation c75c85a4-84d9-4e27-9860-c393ba6472bd · outbound

This paper cites Deep residual learning for image recognition.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Deep residual learning for image recognition

Reference 12

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Source-reported events for the cited work

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Observation 1378c596-8db9-4603-9c80-d3a74281a529 · outbound

This paper cites Multi-Scale Representation Learning for Image Restoration with State-Space Model.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Multi-Scale Representation Learning for Image Restoration with State-Space Model

Reference 13

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Source-reported events for the cited work

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Observation 5cb5daee-54e1-49c9-9cf9-167dd80f4d36 · outbound

This paper cites Pvqm–a perceptual video quality measure.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Pvqm–a perceptual video quality measure

Reference 14

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Observation b7083395-3086-4070-9212-c737b35107b5 · outbound

This paper cites Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Reference 15

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Observation b8e9176b-626b-41a1-958e-0f270f7f5bd0 · outbound

This paper cites Cascaded diffusion models for high fidelity image generation.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Cascaded diffusion models for high fidelity image generation

Reference 16

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Source-reported events for the cited work

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Observation 4cb82dbb-877e-4ce5-9fb9-ca1d8146508b · outbound

This paper cites The konstanz natural video database (konvid-1k).

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor The konstanz natural video database (konvid-1k)

Reference 17

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Observation 016b9234-ceb6-43d6-8aaf-22dfb3e291c8 · outbound

This paper cites Squeeze-and-excitation net- works.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Squeeze-and-excitation net- works

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.497184Z

Source-reported events for the cited work

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Observation 2cb69318-cb03-44c6-92f3-7dc4835bb4fd · outbound

This paper cites A new approach to linear filtering and prediction problems.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor A new approach to linear filtering and prediction problems

Reference 19

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Observation 7e82366e-c170-4489-994b-a45a1e49dea3 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Adam: A Method for Stochastic Optimization

Reference 20

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Observation 509d2a83-32ab-483b-ad85-3bcca951cc92 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Semi-Supervised Classification with Graph Convolutional Networks

Reference 21

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Observation b0c2b259-cab3-491d-92f6-515994ec92db · outbound

This paper cites Two-level approach for no-reference con- sumer video quality assessment.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Two-level approach for no-reference con- sumer video quality assessment

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.473555Z

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Observation 670faf29-96d8-4e95-90af-e8801ae7cc68 · outbound

This paper cites Blind natural video quality prediction via statistical temporal features and deep spatial features.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Blind natural video quality prediction via statistical temporal features and deep spatial features

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.459689Z

Source-reported events for the cited work

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Observation 99899262-2c03-44d3-895c-8861faf81f9d · outbound

This paper cites Blindly assess quality of in-the-wild videos via quality-aware pre-training and motion perception.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Blindly assess quality of in-the-wild videos via quality-aware pre-training and motion perception

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.445918Z

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Observation ab3acd20-3ee4-47e0-8c8b-d86f504abffe · outbound

This paper cites Quality as- sessment of in-the-wild videos.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Quality as- sessment of in-the-wild videos

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.431039Z

Source-reported events for the cited work

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Observation 9fcd444e-9029-4fc7-9983-dec06dda27b7 · outbound

This paper cites Unified qual- ity assessment of in-the-wild videos with mixed datasets training.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Unified qual- ity assessment of in-the-wild videos with mixed datasets training

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.416374Z

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.

Observation 57cf1249-719d-4b42-8793-3414d43970fa · outbound

This paper cites Which has better visual quality: The clear blue sky or a blurry animal? TMM, 2018.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Which has better visual quality: The clear blue sky or a blurry animal? TMM, 2018

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.401532Z

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Observation 5197f6ca-a4a4-4d43-8b1d-798606e64e5c · outbound

This paper cites Videomamba: State space model for efficient video understanding.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Videomamba: State space model for efficient video understanding

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.385396Z

Source-reported events for the cited work

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Observation 353660cc-5110-4abd-a33e-f6b42065b29d · outbound

This paper cites Exploring the ef- fectiveness of video perceptual representation in blind video quality assessment.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Exploring the ef- fectiveness of video perceptual representation in blind video quality assessment

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.371154Z

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

Observation 374cfd9c-688f-4261-ba69-6c09feee11ee · outbound

This paper cites Diff- bir: Toward blind image restoration with generative diffusion prior.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Diff- bir: Toward blind image restoration with generative diffusion prior

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.356560Z

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Observation bddcfce5-d465-4924-b64e-ea82f8cb0064 · outbound

This paper cites Scaling and masking: A new paradigm of data sampling for image and video quality assessment.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Scaling and masking: A new paradigm of data sampling for image and video quality assessment

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.341522Z

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Observation b3ba7889-809c-4f0c-9419-fdf4e7a6b534 · outbound

This paper cites VMamba: Visual State Space Model.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor VMamba: Visual State Space Model

Reference 32

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Observation 5327d881-3b29-4f9b-b7b8-4a5fe8d0602c · outbound

This paper cites Snakes and Ladders: Two Steps Up for VideoMamba.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Snakes and Ladders: Two Steps Up for VideoMamba

Reference 33

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Observation db06c707-e024-4870-92b3-8edd837a1b35 · outbound

This paper cites Kvq: Kwai video quality assessment for short-form videos.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Kvq: Kwai video quality assessment for short-form videos

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.325906Z

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

Observation 2abc2a47-f735-4728-a101-09171e174501 · outbound

This paper cites Clif-vqa: Enhancing video quality assessment by incorporating high-level semantic information related to human feelings.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Clif-vqa: Enhancing video quality assessment by incorporating high-level semantic information related to human feelings

Reference 35

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Observation bf05e8af-66de-4327-9d20-d685d4a0b5d8 · outbound

This paper cites No-reference image quality assessment in the spa- tial domain.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor No-reference image quality assessment in the spa- tial domain

Reference 36

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Observation cd2b8e34-5cad-4679-bb3a-d0aeebe4fa6a · outbound

This paper cites A com- pletely blind video integrity oracle.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor A com- pletely blind video integrity oracle

Reference 37

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Observation 450c1529-41bc-424f-90f8-e8203effbc6e · outbound

This paper cites completely blind.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor completely blind

Reference 38

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Observation 221590fc-8ec8-4135-a05d-3df21c18f823 · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 39

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DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Improved denoising diffusion probabilistic models

Reference 40

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Observation 797990a9-a224-4447-a2ab-ab90beaf8218 · outbound

This paper cites Discrete-time control systems.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Discrete-time control systems

Reference 41

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Observation aa3b1d4d-38bd-44f8-a685-391cfcadda36 · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Learn- ing transferable visual models from natural language super- vision

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.221067Z

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Observation 143237db-db06-412c-a0f6-d8f47d7acc7f · outbound

This paper cites High-resolution image syn- thesis with latent diffusion models.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor High-resolution image syn- thesis with latent diffusion models

Reference 43

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DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor High-resolution image syn- thesis with latent diffusion models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.197483Z

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Observation 5a30372b-a221-4f1f-bd2b-1ea305a6da71 · outbound

This paper cites Palette: Image-to-image diffusion models.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Palette: Image-to-image diffusion models

Reference 45

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DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Bidirectional recurrent neural networks

Reference 46

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Observation 1ee4a2d1-e700-431d-9129-174c60b5675f · outbound

This paper cites ControlUDA: Controllable Diffusion-assisted Unsupervised Domain Adaptation for Cross-Weather Semantic Segmentation.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor ControlUDA: Controllable Diffusion-assisted Unsupervised Domain Adaptation for Cross-Weather Semantic Segmentation

Reference 47

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DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Simplified State Space Layers for Sequence Modeling

Reference 48

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Observation 756666ec-8a94-4232-8c08-1757bee7cccc · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Deep unsupervised learning using nonequilibrium thermodynamics

Reference 49

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Observation f1da58ba-2e37-4465-afd3-a9a2bf6ed640 · outbound

This paper cites Adapool: Expo- nential adaptive pooling for information-retaining downsam- pling.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Adapool: Expo- nential adaptive pooling for information-retaining downsam- pling

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.148880Z

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Observation 06b13543-b476-4e21-9490-b137d8669d35 · outbound

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

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor A deep learning based no-reference quality assessment model for ugc videos

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.134326Z

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Observation 1f58703c-5774-4174-a92c-af5061c5a527 · outbound

This paper cites Ugc-vqa: Benchmarking blind video quality assessment for user generated content.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Ugc-vqa: Benchmarking blind video quality assessment for user generated content

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.118870Z

Source-reported events for the cited work

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Observation acba265f-c5ce-40d5-a899-2240554709b0 · outbound

This paper cites Rapique: Rapid and accurate video quality prediction of user generated content.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Rapique: Rapid and accurate video quality prediction of user generated content

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.103326Z

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Observation 13967863-4f08-459c-989f-c4ccbc7505ff · outbound

This paper cites Youtube ugc dataset for video compression research.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Youtube ugc dataset for video compression research

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.088135Z

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Observation 20258de5-1051-45f9-b13d-a6f0975772ff · outbound

This paper cites Rich features for perceptual quality assessment of ugc videos.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Rich features for perceptual quality assessment of ugc videos

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.073496Z

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Observation 7abea99f-308c-48d5-96a2-5db71f21fe99 · outbound

This paper cites Cbam: Convolutional block attention module.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Cbam: Convolutional block attention module

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.058062Z

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Observation 29f8a30d-e396-4801-8460-8dfafb57f0eb · outbound

This paper cites Fast- vqa: Efficient end-to-end video quality assessment with frag- ment sampling.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Fast- vqa: Efficient end-to-end video quality assessment with frag- ment sampling

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.041999Z

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Observation a1a565c7-df1a-4ced-bb78-576f98fef62d · outbound

This paper cites Discovqa: Temporal distortion-content transformers for video quality assessment.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Discovqa: Temporal distortion-content transformers for video quality assessment

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.027023Z

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Observation 5d09a27d-f75a-4cf7-886f-97d9d3d87ff0 · outbound

This paper cites Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives

Reference 59

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Observation d3d41e22-e23c-443c-a295-cd3181dab1ad · outbound

This paper cites Exploring opinion-unaware video quality assessment with semantic affinity criterion.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Exploring opinion-unaware video quality assessment with semantic affinity criterion

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:39.012063Z

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Observation 3bdb0b11-4b09-46b2-879c-cc03a0b75dd7 · outbound

This paper cites Exploring video quality assessment on user generated contents from aesthetic and technical perspectives.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Exploring video quality assessment on user generated contents from aesthetic and technical perspectives

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:38.997092Z

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Observation 4ddce787-a77f-4283-a17b-ef11997da18d · outbound

This paper cites Towards explainable in-the-wild video quality assess- ment: a database and a language-prompted approach.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Towards explainable in-the-wild video quality assess- ment: a database and a language-prompted approach

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:38.982189Z

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Observation f7a3c975-1d85-4326-b26b-bf89c28d868a · outbound

This paper cites Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-16T00:00:38.584086Z

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Observation f78d4499-588f-4b31-9bdd-64ce27ffc9a8 · outbound

This paper cites Harnessing the spatial- temporal attention of diffusion models for high-fidelity text- to-image synthesis.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Harnessing the spatial- temporal attention of diffusion models for high-fidelity text- to-image synthesis

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-16T00:00:38.588808Z

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Observation c9a80532-11f6-49bd-8b57-64a595344566 · outbound

This paper cites Diffir: Efficient diffusion model for image restoration.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Diffir: Efficient diffusion model for image restoration

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:38.957726Z

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Observation a406d1e7-9c14-463f-a830-35aaf78e042d · outbound

This paper cites Patch-vq:’patching up’the video quality problem.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Patch-vq:’patching up’the video quality problem

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:38.943728Z

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Observation c651ed7d-17ab-4498-b46c-1cc571debfe0 · outbound

This paper cites Long short-term convolutional transformer for no-reference video quality assessment.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Long short-term convolutional transformer for no-reference video quality assessment

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:38.928916Z

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Observation f47a0e7e-1d18-49ab-a3f9-e245b886fe86 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Adding conditional control to text-to-image diffusion models

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-16T00:00:38.606373Z

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Observation 2b02efa0-e0aa-4b3c-89cc-b9a08d9b19e6 · outbound

This paper cites A completely blind video quality evaluator.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor A completely blind video quality evaluator

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:38.904415Z

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Observation fdd4ac62-84a7-4288-87c0-45cc23d4797e · outbound

This paper cites Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model.

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 70

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Pith citing papers

Observation ee3f365f-b592-4237-a8e0-5a7805617b04 · inbound

VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results cites this paper.

VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-05T11:17:54.873690Z

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