MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment | Paper Citation Record

Observation 24435525-b23a-4108-83a0-248939eed891 · outbound

This paper cites No-reference video quality assessment based on visual memory modeling.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment No-reference video quality assessment based on visual memory modeling

Reference 1

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Observation 9f1274e4-5f4b-4486-b798-abe61af2ec4e · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Learning generalized spatial-temporal deep feature representation for no-reference video quality as- sessment

Reference 2

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Observation 570e2ee3-d2d9-4695-96ef-13abfdfab5e7 · outbound

This paper cites Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding

Reference 3

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This paper cites No-reference video quality assessment using natural spa- tiotemporal scene statistics.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment No-reference video quality assessment using natural spa- tiotemporal scene statistics

Reference 4

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 5

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This paper cites Chipqa: No-reference video quality prediction via space-time chips.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Chipqa: No-reference video quality prediction via space-time chips

Reference 6

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This paper cites Hungry hungry hippos: Towards language modeling with state space mod- els.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Hungry hungry hippos: Towards language modeling with state space mod- els

Reference 7

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Observation 283a471c-b80d-4651-8d9d-91a6a2f5c3fa · outbound

This paper cites Aesmamba: Universal image aesthetic assessment with state space models.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Aesmamba: Universal image aesthetic assessment with state space models

Reference 8

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Observation 8fa09a53-d9a1-449e-ba55-ce36eece6cf1 · outbound

This paper cites Learning enriched features via selective state spaces model for efficient image deblurring.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Learning enriched features via selective state spaces model for efficient image deblurring

Reference 9

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Observation 71d3e190-4494-4d83-86de-c498e9ef512b · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Konvid-150k: A dataset for no-reference video qual- ity assessment of videos in-the-wild

Reference 10

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Observation 3303253f-173f-4dde-b007-cc8688cc759f · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 11

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Observation 4c70fbeb-592f-47e2-8a2d-aa07aef8cd4f · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Efficiently Modeling Long Sequences with Structured State Spaces

Reference 12

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Observation c9aa9481-1e7a-4455-aca9-9cb5be5d31c4 · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Combining recurrent, convolutional, and continuous-time models with linear state space layers

Reference 13

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Observation 2bd6d7a4-5d4b-471d-8980-9456a5293dee · outbound

This paper cites Mambair: A simple baseline for image restoration with state-space model.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Mambair: A simple baseline for image restoration with state-space model

Reference 14

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This paper cites Mambair: A simple baseline for image restoration with state-space model.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Mambair: A simple baseline for image restoration with state-space model

Reference 15

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Observation eb3ff5a1-c552-4771-9c77-2c22b88990a5 · outbound

This paper cites Learn- ing spatio-temporal features with 3d residual networks for action recognition.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Learn- ing spatio-temporal features with 3d residual networks for action recognition

Reference 16

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Observation 1d46476a-a21e-470b-9e8e-81c64037e15a · outbound

This paper cites Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet? In CVPR, pages 6546–6555, 2018.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet? In CVPR, pages 6546–6555, 2018

Reference 17

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Observation ada66869-3b76-4c7b-bfe8-ad8cac492b51 · outbound

This paper cites Image sharpness assessment based on local phase coherence.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Image sharpness assessment based on local phase coherence

Reference 18

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Observation f6047a06-d336-4534-802e-78a3aa73a1a8 · outbound

This paper cites Deep residual learning for image recognition.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Deep residual learning for image recognition

Reference 19

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Observation f37c6c04-491b-4ac1-88b0-fbb2ad27803b · outbound

This paper cites Identity mappings in deep residual networks.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Identity mappings in deep residual networks

Reference 20

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Observation ea3b17f0-41b0-4eeb-8b61-2270003c98b9 · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment The konstanz natural video database (konvid-1k)

Reference 21

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Observation 0939c798-e572-435e-b1c6-cb2be5d98e02 · outbound

This paper cites LocalMamba: Visual State Space Model with Windowed Selective Scan.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment LocalMamba: Visual State Space Model with Windowed Selective Scan

Reference 22

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Observation e54aa678-ad04-4b36-a4bf-ec844e1b821f · outbound

This paper cites Efficient movie scene detection using state-space transformers.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Efficient movie scene detection using state-space transformers

Reference 23

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Observation 1addbc3f-4396-4ed6-b7f0-8ace2121f35c · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment A new approach to linear filtering and prediction problems

Reference 24

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Observation 6188b793-6251-4471-a7e1-8dd2128df83f · outbound

This paper cites Convolu- tional neural networks for no-reference image quality assess- ment.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Convolu- tional neural networks for no-reference image quality assess- ment

Reference 25

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Observation ddb5ff2e-a58a-4ab1-ab11-5c4663a48b57 · outbound

This paper cites Simultane- ous estimation of image quality and distortion via multi-task convolutional neural networks.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Simultane- ous estimation of image quality and distortion via multi-task convolutional neural networks

Reference 26

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Observation 616cbd27-3df3-4eb5-973d-e6b2f9668101 · outbound

This paper cites The Kinetics Human Action Video Dataset.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment The Kinetics Human Action Video Dataset

Reference 27

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Observation 1a0c34c6-2a85-4eea-bbe0-ba1fada2ed9a · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Musiq: Multi-scale image quality transformer

Reference 28

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Observation 511815cd-41fd-4ac3-ba82-50a90eab2d3f · outbound

This paper cites Mret: Multi-resolution transformer for video quality assessment.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Mret: Multi-resolution transformer for video quality assessment

Reference 29

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Observation 2309ef50-880d-4af9-974f-685f6d27b9b0 · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Two-level approach for no-reference con- sumer video quality assessment

Reference 30

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Observation 6168efb3-16c0-473a-9759-71101af85693 · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Blind natural video quality prediction via statistical temporal features and deep spatial features

Reference 31

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Observation 9923c2ae-0670-4b2e-8df6-61a1bfcd7a4e · outbound

This paper cites No-reference quality assessment of tone- mapped hdr pictures.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment No-reference quality assessment of tone- mapped hdr pictures

Reference 32

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Observation ac0fa7b9-f2e8-4e6c-9bd8-bc0aac94a5b6 · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Blindly assess quality of in-the-wild videos via quality-aware pre-training and motion perception

Reference 33

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Quality as- sessment of in-the-wild videos

Reference 34

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Observation c82af063-375e-436c-ad59-55104da81f1e · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Videomamba: State space model for efficient video understanding

Reference 35

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Observation dc77a494-1f6f-4e5c-b30a-2035e5624e2e · outbound

This paper cites Mamba- nd: Selective state space modeling for multi-dimensional data.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Mamba- nd: Selective state space modeling for multi-dimensional data

Reference 36

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Observation 8ac76830-49e1-4977-b0ee-c90f08471f54 · outbound

This paper cites Ugc-video: perceptual quality assess- ment of user-generated videos.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Ugc-video: perceptual quality assess- ment of user-generated videos

Reference 37

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Observation feb1bc36-0fd5-4608-b257-50af5f72ab81 · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Exploring the ef- fectiveness of video perceptual representation in blind video quality assessment

Reference 38

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Observation 5128415a-f388-4dd2-a267-1cff47503ea4 · outbound

This paper cites Swin-umamba: Mamba-based unet with imagenet-based pretraining.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Swin-umamba: Mamba-based unet with imagenet-based pretraining

Reference 39

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Observation f8d8715c-5377-44fa-ba78-185c5180c08a · outbound

This paper cites End- to-end blind quality assessment of compressed videos using deep neural networks.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment End- to-end blind quality assessment of compressed videos using deep neural networks

Reference 40

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Observation a58f5a82-0162-42f9-9d4b-8e8efb4833c8 · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Scaling and masking: A new paradigm of data sampling for image and video quality assessment

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.349283Z

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Observation 99e596fc-e5c2-4855-a706-18267b2ffae8 · outbound

This paper cites VMamba: Visual State Space Model.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment VMamba: Visual State Space Model

Reference 42

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Observation 5bc85e06-c406-47f7-9c30-06de1a3b2ac0 · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Swin transformer: Hierarchical vision transformer using shifted windows

Reference 43

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Observation 5567cd7c-91d3-4611-acd5-5e1b1462cd88 · outbound

This paper cites Video swin transformer.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Video swin transformer

Reference 44

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Observation 5ab14404-13b8-4a39-9314-17ea103d9ea3 · outbound

This paper cites U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Reference 45

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Observation badbfc81-1c9d-4c1f-bd0c-132bc88edcb3 · outbound

This paper cites Long range language modeling via gated state spaces.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Long range language modeling via gated state spaces

Reference 46

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Observation ce5258cb-ec08-4f17-9974-b15055af8c34 · outbound

This paper cites ZE-FESG: A zero-shot feature extraction method based on semantic guid- ance for no-reference video quality assessment.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment ZE-FESG: A zero-shot feature extraction method based on semantic guid- ance for no-reference video quality assessment

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.306905Z

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Observation da5f5cf5-bca3-4cb7-bc8d-e10386807c50 · outbound

This paper cites CLiF-VQA: Enhancing video quality assess- ment by incorporating high-level semantic information re- lated to human feelings.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment CLiF-VQA: Enhancing video quality assess- ment by incorporating high-level semantic information re- lated to human feelings

Reference 48

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Observation a76fa9bc-5fb4-4d2e-b5da-248e1e940435 · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment No-reference image quality assessment in the spa- tial domain

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.283825Z

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Observation 6349a89b-a0e9-47d5-af87-b768d9ab2177 · outbound

This paper cites completely blind.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment completely blind

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.273669Z

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Observation 17327c09-fd9f-4434-91e2-5221557d6e13 · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment A com- pletely blind video integrity oracle

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.263129Z

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Observation c2f76d55-09ae-4c05-a608-72c1a5f49059 · outbound

This paper cites Hummuss: Human motion understanding using state space models.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Hummuss: Human motion understanding using state space models

Reference 52

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Observation a8b6b215-6322-416d-a56a-2e1010cd22b8 · outbound

This paper cites CVD2014—A database for evaluating no-reference video quality assess- ment algorithms.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment CVD2014—A database for evaluating no-reference video quality assess- ment algorithms

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.246955Z

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Observation d12fb4eb-a53d-4820-866b-2746ae0a1398 · outbound

This paper cites Videomamba: Spatio-temporal se- lective state space model.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Videomamba: Spatio-temporal se- lective state space model

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.235867Z

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 6d3d04ac-c700-4a57-b784-26418ab4c08d · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Learn- ing transferable visual models from natural language super- vision

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:58.597566Z

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Observation c7f321e8-d844-4ee3-a860-1dc7aef6554e · outbound

This paper cites Blind prediction of natural video quality.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Blind prediction of natural video quality

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.216395Z

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Observation e03767b9-29e3-436d-b782-682092e40ec3 · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:58.605659Z

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Observation 119596c6-6ea1-4b37-b598-4a5c3f8eed2e · outbound

This paper cites Large-scale study of perceptual video quality.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Large-scale study of perceptual video quality

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.204519Z

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

Observation 8c68d566-3df0-4011-b9d9-16508726f49b · outbound

This paper cites Simplified state space layers for sequence modeling.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Simplified state space layers for sequence modeling

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.192518Z

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Observation 91531444-a361-45aa-bd79-9e9240fe5873 · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment A deep learning based no-reference quality assessment model for ugc videos

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.181424Z

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Observation a5ae90b1-0b45-4229-af1c-12b564f6ed6c · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.171849Z

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Observation e2375d26-dd32-4771-83a6-c07568ae3b93 · outbound

This paper cites Efficientnetv2: Smaller models and faster training.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Efficientnetv2: Smaller models and faster training

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.161012Z

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Observation a7e582c5-3cc2-4957-aabd-c178a539e557 · outbound

This paper cites Learning spatiotemporal features with 3d convolutional networks.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Learning spatiotemporal features with 3d convolutional networks

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:58.629060Z

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Observation 1f49930b-b365-4527-8b2d-c3b38cc5c3b2 · outbound

This paper cites UGC-VQA: benchmarking blind video quality assessment for user generated content.IEEE TIP, 30: 4449–4464, 2021.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment UGC-VQA: benchmarking blind video quality assessment for user generated content.IEEE TIP, 30: 4449–4464, 2021

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.145192Z

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Observation 639aa9cd-1f0e-43d3-967b-4c4cac6437f1 · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Rapique: Rapid and accurate video quality prediction of user generated content

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.134460Z

Source-reported events for the cited work

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Observation 1d1601c1-fbb7-429b-a2ef-fb9e7c09a80d · outbound

This paper cites Selective structured state-spaces for long-form video understanding.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Selective structured state-spaces for long-form video understanding

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.123375Z

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 55d6704c-2a47-4295-95a5-49e2e177b582 · outbound

This paper cites Youtube ugc dataset for video compression research.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Youtube ugc dataset for video compression research

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.113272Z

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 35ff5cc1-89b9-4c70-8644-03eaaeef49e0 · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Rich features for perceptual quality assessment of ugc videos

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.102286Z

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Observation 085d9c31-246f-4210-a80d-acb2691c5616 · outbound

This paper cites Modular blind video quality assess- ment.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Modular blind video quality assess- ment

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.090247Z

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Observation 6addb7c0-9b24-438f-930c-c0962e5d8389 · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Fast- vqa: Efficient end-to-end video quality assessment with frag- ment sampling

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.079460Z

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 124d536a-3c0a-4110-8a60-16cc237c1d7d · outbound

This paper cites Neigh- bourhood representative sampling for efficient end-to-end video quality assessment.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Neigh- bourhood representative sampling for efficient end-to-end video quality assessment

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.068738Z

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Observation 1ab3edbf-ca82-470e-94af-a171143ee1fc · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Discovqa: Temporal distortion-content transformers for video quality assessment

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.055860Z

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Observation 4e56a716-97a6-4b16-95e1-cc27a4ccef46 · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Exploring opinion-unaware video quality assessment with semantic affinity criterion

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.044145Z

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Observation 44376807-1772-47cc-8e65-7253dd528304 · outbound

This paper cites Towards Robust Text-Prompted Semantic Criterion for In-the-Wild Video Quality Assessment.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Towards Robust Text-Prompted Semantic Criterion for In-the-Wild Video Quality Assessment

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:58.670083Z

Source-reported events for the cited work

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Observation 8e819ef5-481a-43f7-9c54-1456a14dff80 · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Towards explainable in-the-wild video quality assess- ment: A database and a language-prompted approach

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.033485Z

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Observation 548ca767-48f5-4ba1-8517-f8c78b73c60f · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Exploring video quality assessment on user generated contents from aesthetic and technical perspectives

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.022503Z

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

Observation 188801c5-5327-46ca-bfd0-9bd4c12060ca · outbound

This paper cites Rainmamba: Enhanced locality learning with state space models for video deraining.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Rainmamba: Enhanced locality learning with state space models for video deraining

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.012143Z

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Observation d23a1336-f921-47a1-98f7-32296898c7ad · outbound

This paper cites Q-align: teaching lmms for visual scoring via discrete text-defined levels.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Q-align: teaching lmms for visual scoring via discrete text-defined levels

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:59.001446Z

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Observation 52fffdf4-82aa-4679-9f5f-508344a31969 · outbound

This paper cites No- reference video quality assessment via feature learning.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment No- reference video quality assessment via feature learning

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:58.987749Z

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 27c1cea6-792e-4081-a5bd-19e4d1bb830f · outbound

This paper cites Perceptual quality assessment of internet videos.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Perceptual quality assessment of internet videos

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:58.976676Z

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 ad252a1c-de2c-44e9-af4c-fdabe8a4caab · outbound

This paper cites Blind image quality assessment using joint statistics of gradient magnitude and laplacian features.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Blind image quality assessment using joint statistics of gradient magnitude and laplacian features

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:58.966024Z

Source-reported events for the cited work

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Observation 61d75b6e-d2ba-4fd8-a5f7-f717c580ab1f · outbound

This paper cites Un- supervised feature learning framework for no-reference im- age quality assessment.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Un- supervised feature learning framework for no-reference im- age quality assessment

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:58.955205Z

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Observation 69688ec4-62b9-436b-8120-23829d8a6c20 · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Patch-vq:’patching up’the video quality problem

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:58.944653Z

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Observation ad407f5f-3739-4593-9286-4640f6576699 · outbound

This paper cites Deep neural networks for no-reference video quality assessment.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Deep neural networks for no-reference video quality assessment

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:58.933179Z

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Observation 9206a291-f761-4e23-bceb-976420df9a2f · outbound

This paper cites MambaOut: Do We Really Need Mamba for Vision?.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment MambaOut: Do We Really Need Mamba for Vision?

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:58.710278Z

Source-reported events for the cited work

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Observation 2241962d-a057-4106-ac16-35b27333419e · outbound

This paper cites Md-vqa: Multi-dimensional quality assessment for ugc live videos.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Md-vqa: Multi-dimensional quality assessment for ugc live videos

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:58.920861Z

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 fac9d8a0-5ddd-44d1-aec3-567bf540335d · outbound

This paper cites Motion mamba: Efficient and long sequence motion generation.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Motion mamba: Efficient and long sequence motion generation

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:58.908813Z

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 3b585bc4-5b59-448f-8b60-33d13407bfa9 · outbound

This paper cites Zoom-vqa: Patches, frames and clips integration for video quality as- sessment.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Zoom-vqa: Patches, frames and clips integration for video quality as- sessment

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:58.897278Z

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 ad2432df-aa5a-4bbe-a2bc-e106bd2fe1ec · outbound

This paper cites Learning spatiotemporal interactions for user- generated video quality assessment.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Learning spatiotemporal interactions for user- generated video quality assessment

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:58.886890Z

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 a5ac2fdb-7222-44a4-bc44-313e105d204a · outbound

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

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:58.727321Z

Source-reported events for the cited work

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Observation f5e3b618-ece7-4bb9-a971-4e66771d02ba · outbound

This paper cites State space models for event cameras.

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment State space models for event cameras

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:17:58.875695Z

Source-reported events for the cited work

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