Paper Citation Record · LEDGER

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment

As of 7 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2507.17182.

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

pith.paper-citation-record.v1
2507.17182 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:57:34.604751Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

verified exact2
verified fuzzy29
unresolved1
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5f00ccbb-9466-4514-80c1-d49af5df7cdc · outbound

This paper cites High-resolution image synthesis with latent diffusion models,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment High-resolution image synthesis with latent diffusion models,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.515955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 97e36989-6be2-4bb5-8e32-50bea05d806d · outbound

This paper cites Zero-shot text-to-image generation,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Zero-shot text-to-image generation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.496233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8d4a4622-91ce-4275-a773-13d39370923c · outbound

This paper cites Hierarchical text-con ditional image generation with clip latents,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Hierarchical text-con ditional image generation with clip latents,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.470043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4eeb3e58-324a-4739-a406-6b431c7dd749 · outbound

This paper cites Making a ‘completely bli nd’ image quality analyzer,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Making a ‘completely bli nd’ image quality analyzer,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.438527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1297cecb-d197-4ddc-80ab-5f30cb350ef8 · outbound

This paper cites NIMA: Neural image assessment,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment NIMA: Neural image assessment,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.405948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5cf3eb90-0e78-44c3-a775-6f74e6216eba · outbound

This paper cites Hybrid no-reference quality m etric for singly and multiply distorted images,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Hybrid no-reference quality m etric for singly and multiply distorted images,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.384208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 957ae0ba-445b-4bb6-b0ff-eec4ce1be613 · outbound

This paper cites Blind image quality assessm ent: A natural scene statistics approach in the DCT domain,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Blind image quality assessm ent: A natural scene statistics approach in the DCT domain,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.366622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 178e92ea-58c7-4fa2-8db7-f1f9af88e49b · outbound

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

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Blind image quality assessment: From natural scene statistics to perceptual quality,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.346018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 525e8f5e-ffd7-4509-bab4-ba62c12726da · outbound

This paper cites Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-06T14:57:34.882264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3388f55f-9156-4e56-aa9b-1786c9ef386c · outbound

This paper cites Aigc image quality assessmen t via image-prompt correspondence,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Aigc image quality assessmen t via image-prompt correspondence,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.327524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 54b011b9-e1c4-4c2d-b571-0fd27717cef8 · outbound

This paper cites CLIP-AGIQA: boosting the p erformance of ai-generated image quality assessment with clip,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment CLIP-AGIQA: boosting the p erformance of ai-generated image quality assessment with clip,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.307346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2edd68b6-c605-4848-9b06-24bac09182a3 · outbound

This paper cites Adaptive mixed-scale feature fusion network for blind AI-generated image quality assessment,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Adaptive mixed-scale feature fusion network for blind AI-generated image quality assessment,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.286970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 79f7bf1a-5f83-4487-af37-95c5dea54b49 · outbound

This paper cites Tier: Text-image encoder-base d regression for aigc image quality assessment,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Tier: Text-image encoder-base d regression for aigc image quality assessment,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.260446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cfa61e4b-7f19-42ff-b8a5-f69fd2d201f5 · outbound

This paper cites Do vision tran sformers see like convolutional neural networks?,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Do vision tran sformers see like convolutional neural networks?,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.235690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 79c70cbc-02f7-49e7-af83-d3441f6b37ca · outbound

This paper cites Boosting image quality assessm ent through efficient transformer adaptation with local feature enhancem ent,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Boosting image quality assessm ent through efficient transformer adaptation with local feature enhancem ent,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.215653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 08572979-96c9-411e-8f2b-91841ec3104c · outbound

This paper cites Visualizing and understanding convolution al networks,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Visualizing and understanding convolution al networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.195576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e3015de5-6a5f-4d9e-91c3-e3b890846c63 · outbound

This paper cites Understanding the effective r eceptive field in deep convolutional neural networks,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Understanding the effective r eceptive field in deep convolutional neural networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.167673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e2240223-22d4-43a5-b15b-87e192a742b1 · outbound

This paper cites Understanding neural networks through deep visualization,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Understanding neural networks through deep visualization,

Reference 18

Resolution: verified exact; raw_fallback, observed 2026-08-06T14:57:34.848610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d910281e-80fd-46f3-a070-f166438b5adc · outbound

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

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Learning transfer able visual models from natural language supervision,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.149405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d087a422-822c-4bf2-a7a5-1bc3bbc688f6 · outbound

This paper cites Deep residual learning for image r ecognition,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Deep residual learning for image r ecognition,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.128770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bcd337db-bb39-4dfa-bbe2-eb279cd6921d · outbound

This paper cites A perceptual quality assessment exploration for aigc images,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment A perceptual quality assessment exploration for aigc images,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.106290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6babe495-a3db-4ee4-b8bb-515754c2d274 · outbound

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

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Agiqa-3k: An open database for ai-generated image quality assessment,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.083340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 29046c97-3871-46a8-b6ba-dbce90b5126a · outbound

This paper cites Aigciqa2023: A large-scale i mage quality assessment database for ai generated images: from the pers pectives of quality, authenticity and correspondence,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Aigciqa2023: A large-scale i mage quality assessment database for ai generated images: from the pers pectives of quality, authenticity and correspondence,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.061978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3b9c4972-280a-4ecb-a985-273f8c6e0f30 · outbound

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

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Blind image quality assessment using a deep bilinear convolutional neural network,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.040861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2062c110-090d-4abd-aa4c-4695bc2b1983 · outbound

This paper cites A multi-dimensional aesthetic quality assessment model for mobile game images,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment A multi-dimensional aesthetic quality assessment model for mobile game images,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.019745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e84a8d38-709f-4bac-b654-ee82d91fe96a · outbound

This paper cites Blindly assess image quality in the wild guided by a self-adaptive hyper network,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Blindly assess image quality in the wild guided by a self-adaptive hyper network,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:35.000685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 899ef7d8-5df5-458d-bf57-532df8605af1 · outbound

This paper cites Blind quality assessment for in-th e-wild images via hierarchical feature fusion and iterative mixed databas e training,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Blind quality assessment for in-th e-wild images via hierarchical feature fusion and iterative mixed databas e training,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:34.982998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9670cf83-a0f9-40ec-afb5-9ec007fc2cda · outbound

This paper cites Image quality assessment using contrastive learning,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Image quality assessment using contrastive learning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:34.962123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a78e3a82-7b69-4fea-ad93-cffc5fdbbde3 · outbound

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

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Bringing textual prompt to ai-generated ima ge quality assessment,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:34.940791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9135cf0e-5da9-42bb-91a7-97590fe737ad · outbound

This paper cites Moe-agiqa: Mixture-of-expert s boosted visual perception-driven and semantic-aware quality assessme nt for ai-generated images,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Moe-agiqa: Mixture-of-expert s boosted visual perception-driven and semantic-aware quality assessme nt for ai-generated images,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:34.923565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8f6da203-fe2c-4f3d-ad2a-978a4f13d9f7 · outbound

This paper cites Sf-iqa: Quality and similarity integr ation for ai generated image quality assessment,.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment Sf-iqa: Quality and similarity integr ation for ai generated image quality assessment,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:57:34.904297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bf82b06c-ab4d-4162-9e9b-2af2c2292d5e · outbound

This paper cites PSCR: Patches Sampling-based Contrastive Regression for AIGC Image Quality Assessment.

Hierarchical Fusion and Joint Aggregation: A Multi-Level Feature Representation Method for AIGC Image Quality Assessment PSCR: Patches Sampling-based Contrastive Regression for AIGC Image Quality Assessment

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T14:57:34.604751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.