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Paper Citation Record · LEDGER

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA

As of 8 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2509.03494.

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

pith.paper-citation-record.v1
2509.03494 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:55:57.401337Z

measured 35 of 35 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

35 of 35 outbound references displayed

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  • verified fuzzy3
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External citation measurements

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Outbound references

Observation 11adf929-04cd-492f-9edf-39031eb81514 · outbound

This paper cites Completely Blind.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Completely Blind

Reference 1

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Observation bf867e90-610d-4b37-a201-bab05c1e399d · outbound

This paper cites Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network

Reference 2

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Observation 2dbaba8b-b8c6-43e5-a8c5-a328bf92cb29 · outbound

This paper cites MUSIQ: Multi-scale Image Quality Transformer.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA MUSIQ: Multi-scale Image Quality Transformer

Reference 3

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Observation cd0eb886-c7c4-4d11-9518-3a0d4a945e7e · outbound

This paper cites Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models

Reference 4

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Observation 25b9ee8b-9a75-4134-a86b-f5f7ead5bb4c · outbound

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

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels

Reference 5

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Observation be8da81c-7580-462f-a660-1fbb792414a0 · outbound

This paper cites Exploring CLIP for Assessing the Look and Feel of Images.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Exploring CLIP for Assessing the Look and Feel of Images

Reference 6

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Observation fd9350a5-f19b-4416-a29b-f95cef1e66e5 · outbound

This paper cites Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision

Reference 7

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Observation d5ebee90-74ef-475e-bb10-56018248cbb8 · outbound

This paper cites A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessment.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessment

Reference 8

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Observation c9adff78-b987-4e73-b669-cef5a1080ce7 · outbound

This paper cites Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective

Reference 9

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 98d6f1f5-70c9-4e02-80d5-f8a463baf381 · outbound

This paper cites mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration

Reference 10

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Observation 241f9d26-79d5-4ac8-af64-68c4e92693a7 · outbound

This paper cites Exploring Visual Prompts for Adapting Large-Scale Models.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Exploring Visual Prompts for Adapting Large-Scale Models

Reference 11

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Observation de9714dc-682c-4c6c-8283-93dcb726333f · outbound

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

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment

Reference 12

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Observation bab68c99-2e4a-4bec-a125-31f116770661 · outbound

This paper cites KADID-10k: A Large-scale Artifi- cially Distorted IQA Database.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA KADID-10k: A Large-scale Artifi- cially Distorted IQA Database

Reference 13

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Observation 4e449ccf-8cd5-4309-a198-abcfd0285a44 · outbound

This paper cites AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment

Reference 14

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Observation 2caf8e2d-aabd-41db-b704-64635f427224 · outbound

This paper cites Unleashing the Power of Visual Prompting At the Pixel Level.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Unleashing the Power of Visual Prompting At the Pixel Level

Reference 15

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Observation 2a29d498-df21-4100-9590-31c73398beb3 · outbound

This paper cites AutoVP: An Automated Visual Prompting Framework and Benchmark.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA AutoVP: An Automated Visual Prompting Framework and Benchmark

Reference 16

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Observation ae5c3327-dfa0-45bf-9ea6-74fcf3596313 · outbound

This paper cites Understanding and Improving Visual Prompting: A Label-Mapping Perspective.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Understanding and Improving Visual Prompting: A Label-Mapping Perspective

Reference 17

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Observation 338f4cbf-63d3-4d33-9241-23135d09b9ab · outbound

This paper cites End-to-End Blind Image Quality Assessment Using Deep Neural Networks.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA End-to-End Blind Image Quality Assessment Using Deep Neural Networks

Reference 18

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Observation e24a9897-aa11-416e-9cca-943b7e43c507 · outbound

This paper cites Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild

Reference 19

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Observation 3e0333b8-21f6-4741-9366-a8f084cb5d5a · outbound

This paper cites MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment

Reference 20

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Observation fd7c852e-c98b-4f4f-9194-c0039e7d95a8 · outbound

This paper cites Madhusudana, Neil Birkbeck, Yilin Wang, Balu Adsumilli, and Alan C.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Madhusudana, Neil Birkbeck, Yilin Wang, Balu Adsumilli, and Alan C

Reference 21

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Observation 2916d72e-2a0a-44e0-962b-a523ed74966c · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Learning Transferable Visual Models From Natural Language Supervision

Reference 22

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Observation 48d30153-be2b-4046-abd6-d03ed49b3b94 · outbound

This paper cites Learning to Prompt for Vision-Language Models.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Learning to Prompt for Vision-Language Models

Reference 23

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Observation 03de08e0-830a-468b-96ae-6c177aa7d57e · outbound

This paper cites A Survey of Automatic Prompt Engineering: An Optimization Perspective.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA A Survey of Automatic Prompt Engineering: An Optimization Perspective

Reference 24

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Observation 9e168cb3-3a98-4597-bcc2-b76e5b07cce0 · outbound

This paper cites Robust Adaptation of Foundation Models with Black-Box Visual Prompting.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Robust Adaptation of Foundation Models with Black-Box Visual Prompting

Reference 25

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Observation fa6af6b4-4379-45cb-ace9-9ea18d67a053 · outbound

This paper cites Multi-Layer Cross-Modal Prompt Fusion for No-Reference Image Quality Assess- ment.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Multi-Layer Cross-Modal Prompt Fusion for No-Reference Image Quality Assess- ment

Reference 26

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Observation 9198f5a8-6e6c-4df9-8007-65fe93d246c5 · outbound

This paper cites Multi-Modal Prompt Learning on Blind Image Quality Assessment.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Multi-Modal Prompt Learning on Blind Image Quality Assessment

Reference 27

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local_arxiv, observed 2026-08-05T10:55:57.739821Z

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Observation 74ced107-eef2-42be-bf99-e39ccb347e32 · outbound

This paper cites Q-Adapt: Adapting LMM for Visual Quality Assessment with Progressive Instruction Tuning.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Q-Adapt: Adapting LMM for Visual Quality Assessment with Progressive Instruction Tuning

Reference 28

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Observation b41370ed-0d29-4847-8126-7da1470c97eb · outbound

This paper cites A Survey on Multimodal Large Language Models.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA A Survey on Multimodal Large Language Models

Reference 29

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Observation 1d066402-1aab-4c99-9a97-fdb1e726d985 · outbound

This paper cites Alireza Golestaneh, Saba Dadsetan, and Kris M.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Alireza Golestaneh, Saba Dadsetan, and Kris M

Reference 31

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Observation ac956a8a-66d7-41ab-9e59-52ada205a3e8 · outbound

This paper cites Perceptual Quality As- sessment of Smartphone Photography.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Perceptual Quality As- sessment of Smartphone Photography

Reference 32

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Observation d1095579-ba2e-4b4d-b63f-a27a2ecc0cdf · outbound

This paper cites From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality

Reference 33

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Observation 3cbbb798-c313-4953-858d-8740bab2dfc8 · outbound

This paper cites https://ieeexplore.ieee.org/document/7327186.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA https://ieeexplore.ieee.org/document/7327186

Reference 34

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation b93078ea-c99d-47d3-8e3e-8eb406b30a74 · outbound

This paper cites 2017.2774045.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA 2017.2774045

Reference 1213

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malformed identifier
no resolver link, observed 2026-08-05T10:55:56.083618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:55:56.083618Z digest=sha256:fae160a727064bf77c06b5c7cc15de1e04d48d944b5bc4f9a51246a637b1b66c

Observation f3a39b0d-4f2c-4663-a7e6-0c42e8636cd5 · outbound

This paper cites an unresolved cited work.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Unresolved cited work

Reference 2020

Resolution
verified exact
arxiv_id_nonexistent, observed 2026-08-05T10:55:59.101917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T10:55:57.266765Z digest=sha256:75bca6828dd0f9039dd4a8c921774f9765e3ced5b092d7d6192c4d8e4459932d

Pith citing papers

No inbound Pith citation observations are available.