Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality | Paper Citation Record

Observation 465a6665-28ac-4ad7-95dd-e833d8f0d96a · outbound

This paper cites Audio-visual multimedia quality assessment: A comprehensive survey,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Audio-visual multimedia quality assessment: A comprehensive survey,

Reference 1

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Observation d4dcc6f5-85c0-4974-9a8c-af9ef48f3bb1 · outbound

This paper cites Perceptual image quality assessment: a survey,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Perceptual image quality assessment: a survey,

Reference 2

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Observation 084403e4-efba-4e17-abfe-00474edf8ec2 · outbound

This paper cites Perceptual video quality assessment: A survey,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Perceptual video quality assessment: A survey,

Reference 3

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Observation 5126048e-3554-4350-93b5-8ffd1b79d070 · outbound

This paper cites Image aesthetic assessment: An experimental survey,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Image aesthetic assessment: An experimental survey,

Reference 4

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Observation 3e2e0d30-4cfc-4727-b600-81b3a377cbd9 · outbound

This paper cites A brief survey on adaptive video streaming quality assessment,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality A brief survey on adaptive video streaming quality assessment,

Reference 5

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Observation b87b9056-97b1-45ef-ac10-ecbf1940cf86 · outbound

This paper cites Real-time quality-and energy-aware bitrate ladder construction for live video streaming,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Real-time quality-and energy-aware bitrate ladder construction for live video streaming,

Reference 6

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Observation e7949539-7003-4d0b-bbbc-767b7b16d3b0 · outbound

This paper cites Telepresence video quality assessment,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Telepresence video quality assessment,

Reference 7

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Observation 8152f3f8-2241-46f1-9c9a-783d3215690a · outbound

This paper cites A systematic literature review: Real-time 3d reconstruction method for telepresence system,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality A systematic literature review: Real-time 3d reconstruction method for telepresence system,

Reference 8

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Observation 13cf0c20-6734-4223-90aa-95215cd7a8ec · outbound

This paper cites Quality assessment of videos on social media platforms related to gestational diabetes mellitus in china: A cross-section study,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Quality assessment of videos on social media platforms related to gestational diabetes mellitus in china: A cross-section study,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:18.950782Z

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Observation 7b205f58-2d8d-459a-a68b-85d881629cd6 · outbound

This paper cites Robust dual-modal image quality assessment aware deep learning network for traffic targets detection of autonomous vehicles,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Robust dual-modal image quality assessment aware deep learning network for traffic targets detection of autonomous vehicles,

Reference 10

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Observation 72f80c80-35d2-4328-9659-2874247408ce · outbound

This paper cites Deep-based quality assessment of medical images through domain adaptation,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Deep-based quality assessment of medical images through domain adaptation,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:19.096561Z

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Observation 738233fb-8aad-43d4-9a4f-a2b37715cce7 · outbound

This paper cites Representation learning optimization for 3d point cloud quality assessment without reference,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Representation learning optimization for 3d point cloud quality assessment without reference,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:19.165470Z

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Observation 550eb904-4576-4bdf-a8f7-5a304f1f4a25 · outbound

This paper cites Recommendation p.910: Subjective video quality assessment methods for multimedia applications,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Recommendation p.910: Subjective video quality assessment methods for multimedia applications,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:19.239783Z

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Observation e7b7032d-1d11-428d-a15a-7971d7de26ff · outbound

This paper cites Mean opinion score (mos) revisited: methods and applications, limitations and alternatives,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Mean opinion score (mos) revisited: methods and applications, limitations and alternatives,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:19.319701Z

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Observation 4a2feeb1-3df6-466b-816e-b839073eef18 · outbound

This paper cites William james, gustav fechner, and early psychophysics,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality William james, gustav fechner, and early psychophysics,

Reference 15

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Observation ef688437-7442-4d1a-b78b-ae2029fe21aa · outbound

This paper cites A brief review of the history and application of psychometrics and scaling to image quality assessment,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality A brief review of the history and application of psychometrics and scaling to image quality assessment,

Reference 16

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Observation c2509a9c-bf4a-4760-b79b-66bae451f420 · outbound

This paper cites Just noticeable difference,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Just noticeable difference,

Reference 17

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Observation 361dcadc-fa0f-47df-9de8-54991f753ec6 · outbound

This paper cites From pairwise comparisons and rating to a unified quality scale,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality From pairwise comparisons and rating to a unified quality scale,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:19.661852Z

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Observation 991b3d0d-6c27-4e06-8d46-596c295f5633 · outbound

This paper cites A law of comparative judgment,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality A law of comparative judgment,

Reference 19

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Observation 9d13aaf4-6a08-494b-abdb-26ca632c144e · outbound

This paper cites Recommendation bt.500: Methodology for the subjective assessment of the quality of television pictures,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Recommendation bt.500: Methodology for the subjective assessment of the quality of television pictures,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:19.851034Z

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Observation f3740948-1f2f-4df2-8a38-235962c4d25e · outbound

This paper cites Subjective and Objective Quality Assessment of Image: A Survey.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Subjective and Objective Quality Assessment of Image: A Survey

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:19.923541Z

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Observation e956d9ed-10bf-4ccf-af53-3cbfcf3f172c · outbound

This paper cites Experimental comparison of psnr and ssim metrics for video quality estimation,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Experimental comparison of psnr and ssim metrics for video quality estimation,

Reference 22

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Observation cb312883-3a13-4d9b-8917-202a9b3df661 · outbound

This paper cites Image qualityassessment: From errorvisibilitytostructural similarity,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Image qualityassessment: From errorvisibilitytostructural similarity,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:20.100232Z

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Observation 88e2cb54-6463-4b62-8774-61cffc11c6a0 · outbound

This paper cites Toward a practical perceptual video quality metric, 2016,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Toward a practical perceptual video quality metric, 2016,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:20.164457Z

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Observation 116090ab-e4be-495c-bbb2-6447496d17e3 · outbound

This paper cites Reduced-reference image quality assessment based on perceptual image hashing,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Reduced-reference image quality assessment based on perceptual image hashing,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:20.246572Z

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Observation 1caa9e64-ecc7-41e1-811a-96da0914ba94 · outbound

This paper cites Reduced reference image quality assessment via sub-image similarity based redundancy measurement,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Reduced reference image quality assessment via sub-image similarity based redundancy measurement,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:20.321106Z

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Observation 2c9c4a0a-b5e6-4c7e-bb11-0671510357c2 · outbound

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

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality No-reference image quality assessment in the spatial domain,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:20.408960Z

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Observation f7de7197-7ee9-4cb9-a45a-b95ddadc653a · outbound

This paper cites Making a “completely blind.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Making a “completely blind

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:20.515958Z

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Observation 70c6b312-e5c4-4f8e-a695-5bdc230e8b39 · outbound

This paper cites Arniqa: Learning distortion manifold for image quality assessment,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Arniqa: Learning distortion manifold for image quality assessment,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:20.592045Z

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Observation 323005bb-2dec-4c48-a19e-979f103f1245 · outbound

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

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Blind image quality assessment using a deep bilinear convolutional neural network,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:41.586265Z

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Observation eb4b09d2-9f6a-4dfa-bd25-2384243a52f3 · outbound

This paper cites Koniq-10k: An ecologically valid database for deep learning of blind image quality assessment,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Koniq-10k: An ecologically valid database for deep learning of blind image quality assessment,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:41.462547Z

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Observation 09e9b0bf-2e45-41d3-8db6-d5e06278fa61 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality The unreasonable effectiveness of deep features as a perceptual metric,

Reference 32

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Observation db06a423-1c9f-4f20-b9e5-551dad7926c9 · outbound

This paper cites Topiq: A top-down approach from semantics to distortions for image quality assessment,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Topiq: A top-down approach from semantics to distortions for image quality assessment,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:41.321723Z

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Observation f38eb180-fb81-4356-af58-5b2d590ecc38 · outbound

This paper cites Musiq: Multi-scale image quality transformer,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Musiq: Multi-scale image quality transformer,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:41.207747Z

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Observation 83e1abe1-b703-440d-bb30-7e5100574d09 · outbound

This paper cites Perceptual image quality assessment with transform- ers,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Perceptual image quality assessment with transform- ers,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:41.109506Z

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Observation fd8bf3f9-377c-4518-8008-6d45cf155725 · outbound

This paper cites Re-iqa: Unsupervised learning for image quality assessment in the wild,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Re-iqa: Unsupervised learning for image quality assessment in the wild,

Reference 36

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Observation 40356528-4003-46db-a2cb-8e3380a2ad56 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Learning transferable visual models from natural language supervision,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:40.738438Z

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Observation d6fe8ab5-d9db-4c26-990b-16b171a48363 · outbound

This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:40.573735Z

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Observation e470dc0f-1a17-408d-83e3-e8ad4869cab8 · outbound

This paper cites A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges

Reference 39

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Observation 63e61f3e-0b76-4fa8-9c4f-2b42249e4a51 · outbound

This paper cites Exploring clip for assessing the look and feel of images,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Exploring clip for assessing the look and feel of images,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:40.377887Z

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Observation 94dbc69b-b486-481e-ad24-13b94c95c2fd · outbound

This paper cites Quality-Aware Image-Text Alignment for Opinion-Unaware Image Quality Assessment.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Quality-Aware Image-Text Alignment for Opinion-Unaware Image Quality Assessment

Reference 41

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Observation 3a636623-c59d-4718-8014-7b81e974ab5f · outbound

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

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels

Reference 42

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Observation f3a3826c-7869-477a-a704-51b347ebc393 · outbound

This paper cites Flamingo: a visual language model for few-shot learning,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Flamingo: a visual language model for few-shot learning,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:40.264294Z

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Observation 241bd1aa-9347-49ea-9e00-028aec13d9e8 · outbound

This paper cites Visual instruction tuning,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Visual instruction tuning,

Reference 44

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Observation 5d5dbc4e-f8cd-4bd6-b3ed-72de4e2a9126 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 45

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Observation 5a0ef625-6a7b-4120-8cea-cabca96e0208 · outbound

This paper cites A comprehensive study of multimodal large language models for image quality assessment,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality A comprehensive study of multimodal large language models for image quality assessment,

Reference 46

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Observation a19db6fe-50e1-49b6-bc11-aeaaf0b8d6c4 · outbound

This paper cites Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning,

Reference 47

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Unavailable: canonical work link unavailable.

Observation 18e04d24-b01e-46f8-974a-8d0e193b9348 · outbound

This paper cites Qwq-32b: Embracing the power of reinforcement learning,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Qwq-32b: Embracing the power of reinforcement learning,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:39.628450Z

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Observation 72491819-6742-4f64-9c8c-a0bf73754a6d · outbound

This paper cites VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model

Reference 49

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Observation 64dfd561-3c7e-43e8-9a06-b2e8105d981f · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 50

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Observation 601fab26-5b95-4c28-95d4-3d04fdc7c7f1 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Proximal Policy Optimization Algorithms

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:22.401812Z

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Observation 7d4d2aab-883f-469b-af5d-dceff3449ab0 · outbound

This paper cites Blind image quality assessment: From natural scene statistics to perceptual quality,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Blind image quality assessment: From natural scene statistics to perceptual quality,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:39.366085Z

Source-reported events for the cited work

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Observation d6f0e627-1c9d-45db-b607-951c42b9ee79 · outbound

This paper cites Cnn-based cross-dataset no-reference image quality assessment,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Cnn-based cross-dataset no-reference image quality assessment,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:39.114548Z

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Observation 41e25044-d799-4502-b5b4-141b97cf90ea · outbound

This paper cites Semantically-Aware Game Image Quality Assessment.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Semantically-Aware Game Image Quality Assessment

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:11:32.169474Z

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Observation 9cf00f34-60e8-4cc9-8ba6-9226c7e91cfb · outbound

This paper cites PyTorch Image Quality: Metrics for Image Quality Assessment.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality PyTorch Image Quality: Metrics for Image Quality Assessment

Reference 55

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Observation 7fa7188e-e9b9-4078-bb1e-6b9b47baa405 · outbound

This paper cites Do image and video quality metrics model low-level human vision?,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Do image and video quality metrics model low-level human vision?,

Reference 56

Resolution: verified exact; raw_fallback, observed 2026-08-07T14:11:31.934664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5c81be46-0439-4abd-a6fb-49b6c46a2594 · outbound

This paper cites A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:26.293988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23b62a56-dffc-4f95-a6ff-5f6be89f0a5c · outbound

This paper cites Koniq-10k: An ecologically valid database for deep learning of blind image quality assessment,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Koniq-10k: An ecologically valid database for deep learning of blind image quality assessment,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:26.358277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b27148d5-07a8-4ebe-bc48-2d734a170021 · outbound

This paper cites Quality assessment of in-the-wild videos,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Quality assessment of in-the-wild videos,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:38.785513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d1dddcf2-71d1-4ea1-8935-4772e73f026f · outbound

This paper cites Perceptual quality prediction on authentically distorted images using a bag of features approach,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Perceptual quality prediction on authentically distorted images using a bag of features approach,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:38.545489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a1ffb09c-a7a4-4960-941a-d1be6664f21b · outbound

This paper cites Exploring Semantic Feature Discrimination for Perceptual Image Super-Resolution and Opinion-Unaware No-Reference Image Quality Assessment.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Exploring Semantic Feature Discrimination for Perceptual Image Super-Resolution and Opinion-Unaware No-Reference Image Quality Assessment

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:11:31.530663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1db60652-698b-43a1-a505-c76af9cb01ee · outbound

This paper cites No-reference image quality assessment combining swin-transformer and natural scene statistics,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality No-reference image quality assessment combining swin-transformer and natural scene statistics,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:38.250232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9ba67c30-c056-451b-b7c7-9075ee2a93ab · outbound

This paper cites Ie-iqa: Intelligibility enriched generalizable no-reference image quality assessment,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Ie-iqa: Intelligibility enriched generalizable no-reference image quality assessment,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:38.138533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4ff0d22e-b217-4d3f-8e6d-cea2c57bd27c · outbound

This paper cites Massive online crowdsourced study of subjective and objective picture quality,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Massive online crowdsourced study of subjective and objective picture quality,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:37.938083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 72910514-86dd-4a87-bdca-07db9b8433d1 · outbound

This paper cites Biq2021: a large-scale blind image quality assessment database,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Biq2021: a large-scale blind image quality assessment database,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:37.741009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a5fbc6e3-0fd3-4924-ab4a-65f7fee058b9 · outbound

This paper cites No reference opinion unaware quality assessment of authentically distorted images,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality No reference opinion unaware quality assessment of authentically distorted images,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:37.637003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 49370b59-3090-415f-993c-208c2e67a420 · outbound

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

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Patch-vq:’patching up’the video quality problem,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:37.544544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 59e8eaad-e3b4-4e06-9b3a-c74c785509bd · outbound

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

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality The konstanz natural video database (konvid-1k),

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:37.364005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3143d324-c5ba-4467-902d-03dea1b3ab3d · outbound

This paper cites Agiqa-3k: An open database for ai-generated image quality assessment,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Agiqa-3k: An open database for ai-generated image quality assessment,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:37.219855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b5e51a70-4134-4a68-a426-d575aba874f4 · outbound

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

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Kvq: Kwai video quality assessment for short-form videos,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:37.125137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e1878a1c-43f6-421c-8c93-55225b56a86b · outbound

This paper cites A Multi-annotated and Multi-modal Dataset for Wide-angle Video Quality Assessment.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality A Multi-annotated and Multi-modal Dataset for Wide-angle Video Quality Assessment

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:11:31.415311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 06359a2c-b8b2-49d1-81bd-a13a5814b854 · outbound

This paper cites Nits-iqa database: a new image quality assessment database,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Nits-iqa database: a new image quality assessment database,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:37.001733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4123375e-4bd5-41e7-9a52-5c5c8fea700b · outbound

This paper cites Exiqa: Explainable image quality assessment using distortion attributes,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Exiqa: Explainable image quality assessment using distortion attributes,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:36.890148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 84f4b7d0-0f07-42aa-a1d5-3bbf41d29393 · outbound

This paper cites Explainable and generalizable blind image quality assessment via semantic attribute reasoning,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Explainable and generalizable blind image quality assessment via semantic attribute reasoning,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:36.806269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8c527b92-12a9-41ec-964a-76d67303892e · outbound

This paper cites Explainability for deep learning in mammography image quality assessment,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Explainability for deep learning in mammography image quality assessment,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:36.704008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation efadd2b5-63dd-4588-bb41-ca273bb9866d · outbound

This paper cites Pipal: a large-scale image quality assessment dataset for perceptual image restoration,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Pipal: a large-scale image quality assessment dataset for perceptual image restoration,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:36.579088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2a5a5b8b-2d77-44ba-9b69-3e89423164ce · outbound

This paper cites Maniqa: Multi-dimension attention network for no-reference image quality assessment,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Maniqa: Multi-dimension attention network for no-reference image quality assessment,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:36.436260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1f1d1246-0e2a-4df8-82ce-72ea2835e2d4 · outbound

This paper cites Large multi-modality model assisted ai-generated image quality assessment,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Large multi-modality model assisted ai-generated image quality assessment,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:36.315295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 83c5ee49-d20b-4a49-8221-eb2fb3ea8df4 · outbound

This paper cites Pea265: Perceptual assessment of video compression artifacts,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Pea265: Perceptual assessment of video compression artifacts,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:36.185222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f00b7a53-b51f-4b50-9271-35469a0a7e8a · outbound

This paper cites Saliency-aware spatio-temporal artifact detection for compressed video quality assessment,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Saliency-aware spatio-temporal artifact detection for compressed video quality assessment,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:36.024919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 634550f0-0f58-4fce-b876-2ca9039d6035 · outbound

This paper cites Clip-agiqa: Boosting the performance of ai-generated image quality assessment with clip,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Clip-agiqa: Boosting the performance of ai-generated image quality assessment with clip,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:35.820353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b7f4a9d1-981c-4508-95e7-7a8fa1605d61 · outbound

This paper cites Bringing textual prompt to ai-generated image quality assessment,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Bringing textual prompt to ai-generated image quality assessment,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:35.670474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation caba7da3-bfbe-4e26-ab6c-fbd29a6173ef · outbound

This paper cites Metaiqa: Deep meta-learning for no- reference image quality assessment,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Metaiqa: Deep meta-learning for no- reference image quality assessment,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:35.516443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e6c824d1-44e9-4dab-9ad1-cd005aac2296 · outbound

This paper cites Sgiqa: semantic-guided no-reference image quality assessment,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Sgiqa: semantic-guided no-reference image quality assessment,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:35.387857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6637a1d2-f657-44fb-8d7a-8d89d1d49b12 · outbound

This paper cites A domain adaptive deep learning solution for scanpath prediction of paintings,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality A domain adaptive deep learning solution for scanpath prediction of paintings,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:35.269258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 41e4a5be-81fd-4099-ad63-8a3e77e62bde · outbound

This paper cites Self supervised scanpath prediction framework for painting images,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Self supervised scanpath prediction framework for painting images,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:35.158861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cde7d50c-ce66-47e6-b547-7cd76410917c · outbound

This paper cites On the use of a scanpath predictor and convolutional neural network for blind image quality assessment,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality On the use of a scanpath predictor and convolutional neural network for blind image quality assessment,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:34.999781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 59f7f287-225b-4ea2-a9f3-9eeb0759d3e0 · outbound

This paper cites Emotion detection through facial expressions: A survey of ai-based methods,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Emotion detection through facial expressions: A survey of ai-based methods,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:34.876984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9abf77b0-9be0-41cd-877a-c7ac0e3d2983 · outbound

This paper cites Human emotion detection and face recognition system,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Human emotion detection and face recognition system,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:34.764496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 330aecc2-00dc-40fa-9caa-26fb99a86acb · outbound

This paper cites A subjective study of image quality assessment metrics using crowdsourcing,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality A subjective study of image quality assessment metrics using crowdsourcing,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:34.612876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 105fd76b-922c-46f1-a4e3-cdf6d77c2d53 · outbound

This paper cites A survey on deep active learning: Recent advances and new frontiers,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality A survey on deep active learning: Recent advances and new frontiers,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:34.520326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 417f5fd7-89ca-49bd-971e-462084609d0f · outbound

This paper cites Crowdsourcing image descriptions using gamification: a comparison between game-generated labels and professional descriptors,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Crowdsourcing image descriptions using gamification: a comparison between game-generated labels and professional descriptors,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:34.400373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 539afd5d-2069-4712-9907-597742fe668e · outbound

This paper cites The leading guideline: Reporting standards for expert panel, best-estimate diagnosis, and longitudinal expert all data (lead) methods,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality The leading guideline: Reporting standards for expert panel, best-estimate diagnosis, and longitudinal expert all data (lead) methods,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:34.289293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 20451a15-c19d-45f9-9201-b4c901eeda57 · outbound

This paper cites Scaling synthetic data creation with 1,000,000,000 personas,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Scaling synthetic data creation with 1,000,000,000 personas,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:34.164805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 45d5ef45-6dc5-461f-89b9-f13cebd9ab4f · outbound

This paper cites QAFactEval: Improved QA-based factual consistency evaluation for summarization,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality QAFactEval: Improved QA-based factual consistency evaluation for summarization,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:34.017692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 17c11550-9bb0-4c9c-8b28-46a99bbe7323 · outbound

This paper cites Evaluating step-by-step reasoning traces: A survey,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Evaluating step-by-step reasoning traces: A survey,

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-07T14:11:30.460570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 474e5ba1-3365-4dfa-8a3f-a7a61d99fed5 · outbound

This paper cites Color image database tid2013: Peculiarities and preliminary results,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Color image database tid2013: Peculiarities and preliminary results,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:33.870654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7720e4ad-77c1-4dcb-ba7d-1837320bcb43 · outbound

This paper cites Image-guided outdoor lidar perception quality assessment for autonomous driving,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Image-guided outdoor lidar perception quality assessment for autonomous driving,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:33.733372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1cfd3c49-d06f-4aa0-a3fa-3ffd82ca520c · outbound

This paper cites Explainable artificial intelligence: Importance, use domains, stages, output shapes, and challenges,.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Explainable artificial intelligence: Importance, use domains, stages, output shapes, and challenges,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:33.604438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2c55d3b7-6862-43cb-bfc9-51d011271492 · outbound

This paper cites Can surgeons trust ai? perspectives on machine learning in surgery and the importance of explainable artificial intelligence (xai),.

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality Can surgeons trust ai? perspectives on machine learning in surgery and the importance of explainable artificial intelligence (xai),

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:11:33.453002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers