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Paper Citation Record · LEDGER

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models

As of 21 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2412.07674.

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

pith.paper-citation-record.v1
2412.07674 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:40:28.695737Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

40 of 40 outbound references displayed

  • verified exact0
  • verified fuzzy12
  • unresolved28
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 123556f4-da29-4584-b1b4-f614e181b247 · outbound

This paper cites Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

Reference 1

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Observation 0ff369a0-008e-4ccb-9ceb-584132a5fc03 · outbound

This paper cites PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Reference 2

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source=pdf_text observed=2026-08-11T18:40:28.504786Z digest=sha256:aaef7892f2842c30fa70756f2c62214c4627378c94ac25f05ac556dd68d53116

Observation 3831a5ed-78d5-482f-ac7b-fe1ef7bb6c3b · outbound

This paper cites PixArt-\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models PixArt-\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation

Reference 3

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source=pdf_text observed=2026-08-11T18:40:28.510156Z digest=sha256:d3d562bfd7ad65e8343f1c2c8028f965be27427d03f74cb56e0771c5a31c84f5

Observation 8f187c7c-a289-4bb8-bd3c-f2b7dc9ce093 · outbound

This paper cites PIXART-{\delta}: Fast and Controllable Image Generation with Latent Consistency Models.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models PIXART-{\delta}: Fast and Controllable Image Generation with Latent Consistency Models

Reference 4

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source=pdf_text observed=2026-08-11T18:40:28.516313Z digest=sha256:83d4f67a979b12a67528a99a734c6b511af9257a796baa2279b7dfdeb2831f68

Observation 7b8bfe93-837a-418a-bc33-7275e61f4e0b · outbound

This paper cites AnyDoor: Zero-shot Object-level Image Customization.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models AnyDoor: Zero-shot Object-level Image Customization

Reference 5

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source=pdf_text observed=2026-08-11T18:40:28.521603Z digest=sha256:9bf70aa51e63ceb4a72d612f6278060e8a75f329bf0a0ae893aba79d3f06db07

Observation 863a72db-3cd7-46a1-b4b5-434cef1123f4 · outbound

This paper cites StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators

Reference 6

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source=pdf_text observed=2026-08-11T18:40:28.526087Z digest=sha256:68c6a8a45883e426ea7051478107e67b9d94569207232466d3f3a55701d35606

Observation 2ccdbf6d-f69d-4b6c-8f86-c6fbff59a759 · outbound

This paper cites Style Aligned Image Generation via Shared Attention.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Style Aligned Image Generation via Shared Attention

Reference 7

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source=pdf_text observed=2026-08-11T18:40:28.531192Z digest=sha256:c5013da96c5d441d922b1d00bb79e9862e465d806a2a32dddf78295171ebe097

Observation 00ffe697-6b61-4661-9d87-a2c0b969387c · outbound

This paper cites Denoising Diffusion Probabilistic Models.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Denoising Diffusion Probabilistic Models

Reference 8

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Observation cb74d589-ca20-4309-8e8c-7bdeda643d97 · outbound

This paper cites Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation

Reference 9

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source=pdf_text observed=2026-08-11T18:40:28.542539Z digest=sha256:8057369ba1a5d156c6b930951b9e6fa41867427b75a7cabc68152767b1cc28c4

Observation ee60112f-92dc-49b4-9d06-eee352350115 · outbound

This paper cites U-dit tts: U-diffusion vision transformer for text-to-speech.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models U-dit tts: U-diffusion vision transformer for text-to-speech

Reference 10

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T18:40:28.547520Z digest=sha256:f4cef451e4a53febcab0b273ea583a09d9c94bf13fbc1585fda57da2d331504e

Observation 1ae46ef0-fa04-483a-81af-f9acdb0f6f2e · outbound

This paper cites Shamma, Michael S.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Shamma, Michael S

Reference 11

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T18:40:28.552766Z digest=sha256:5471bf39e17c1254cfe0eb382fb833e27c47b4e738913c29f5d7247e47a1f387

Observation 4a3be902-907e-43e8-8b10-89e8cb51d104 · outbound

This paper cites Multi-concept customization of text-to-image diffusion.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Multi-concept customization of text-to-image diffusion

Reference 12

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T18:40:28.557545Z digest=sha256:8d7319ce4438bc7785244611d6519a27151ba52496e7442487f90d093e28f76f

Observation 52ed3e26-d7ca-40ec-8e86-bb4bbf858261 · outbound

This paper cites BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing

Reference 13

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source=pdf_text observed=2026-08-11T18:40:28.562905Z digest=sha256:0d3d7138ff8262d36d4a86c689a77a4fc2342fab6fda080c0abe1f0b88876ec8

Observation d85635d7-304a-4021-ba62-35a13fa7fdc7 · outbound

This paper cites Playground v2.5: Three insights towards enhancing aesthetic quality in text-to-image generation, 2024.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Playground v2.5: Three insights towards enhancing aesthetic quality in text-to-image generation, 2024

Reference 14

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source=pdf_text observed=2026-08-11T18:40:28.567813Z digest=sha256:98cec5e150ac8a7cb04152978ec0ade3c1baa69799ba7d5d8a049590ee842f4d

Observation f17c1609-96ed-4d8b-b483-8b2210fd039c · outbound

This paper cites an unresolved cited work.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Unresolved cited work

Reference 15

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source=pdf_text observed=2026-08-11T18:40:28.572408Z digest=sha256:7428bc3c6a5dd2730d7d5c55b26714a7e0993a842e9281a8bdb1e737ad818e19

Observation c93b783a-afec-4fa9-8d06-78d19c1b7d97 · outbound

This paper cites Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C

Reference 16

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source=pdf_text observed=2026-08-11T18:40:28.577187Z digest=sha256:c7744b94180dae4c7f4a52d1e9b275df6818eb0c2b7dab98fd9fceff3b180ec3

Observation 9693c1af-da83-4fe8-b793-fe24540a19d5 · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 17

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Observation 422a4f50-5edd-478c-8bac-5b206c302428 · outbound

This paper cites Scalable diffusion models with transformers.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Scalable diffusion models with transformers

Reference 18

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Observation 216f4f28-7531-4740-9a23-672717f2a722 · outbound

This paper cites Deadiff: An efficient stylization diffusion model with disentangled representations.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Deadiff: An efficient stylization diffusion model with disentangled representations

Reference 19

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation ccc476a0-c9ee-4095-a455-d4415c888ed7 · outbound

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

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Learning transferable visual models from natural language supervision

Reference 20

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source=pdf_text observed=2026-08-11T18:40:28.596189Z digest=sha256:a55a7a330d187eeb6a0e4f01cf6128cfdc40891b30672e2962df98d1225d9635

Observation e5e969db-e17b-436c-a2de-0852b9b6d7cb · outbound

This paper cites Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer

Reference 21

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source=pdf_text observed=2026-08-11T18:40:28.600751Z digest=sha256:49dc770c51a70c91d3d334ee4e9e2e4d0ebb3c40a8a825079132971e148b1f85

Observation 6ebed150-4ce5-419f-b01f-0c2aa596df0e · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models U-net: Convolutional networks for biomedical image segmentation

Reference 22

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source=pdf_text observed=2026-08-11T18:40:28.604965Z digest=sha256:f66214d5cc15c92f8b55b8b3ac6d1b04bbc50a11dc8af33a1575040afec8712a

Observation 61ca893c-e1a0-4c43-85ff-737fdbf7775d · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 23

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source=pdf_text observed=2026-08-11T18:40:28.609185Z digest=sha256:12488537da9079802ca46ee391c6bbdb9ab16171592c673eea7a13b757958a0f

Observation 3f9604d0-f297-4230-a940-f6b8aac25916 · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models, 2022.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Laion-5b: An open large-scale dataset for training next generation image-text models, 2022

Reference 24

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source=pdf_text observed=2026-08-11T18:40:28.613283Z digest=sha256:a00e195bf2fbb840ac4f37d9f9d3b980bffdc8ada4e2d651dc62cdaa8e59c327

Observation 651bbf4a-c436-4f06-8e1b-fa117c04f349 · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 25

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation a05873d1-6f36-49be-b0b8-f554d1766e88 · outbound

This paper cites Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning

Reference 26

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 8f304f1c-82e2-459a-b33e-a519b0734e1b · outbound

This paper cites StyleDrop: Text-to-Image Generation in Any Style.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models StyleDrop: Text-to-Image Generation in Any Style

Reference 28

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Observation 2da18837-135c-4929-9a28-78edfa457b65 · outbound

This paper cites ObjectStitch: Generative Object Compositing.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models ObjectStitch: Generative Object Compositing

Reference 29

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Observation fe3ea982-b90d-422d-8fe9-1316ba354f9b · outbound

This paper cites Shamma, Gerald Friedland, Benjamin Elizalde, Karl S.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Shamma, Gerald Friedland, Benjamin Elizalde, Karl S

Reference 30

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation d536c86b-0386-46ca-afe7-22e8f05b567b · outbound

This paper cites StyleAdapter: A Unified Stylized Image Generation Model.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models StyleAdapter: A Unified Stylized Image Generation Model

Reference 31

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source=pdf_text observed=2026-08-11T18:40:28.646066Z digest=sha256:408d80f114a4ad6c9d10816c3cfd1906566dd43b39ead637239827664d11af01

Observation a4da1109-0fb9-447d-b887-b96fd195529d · outbound

This paper cites DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models

Reference 32

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source=pdf_text observed=2026-08-11T18:40:28.651519Z digest=sha256:06fa885350a27071bcf5775760212bd2a8874eddb4a5e4c30c59e7b11ac992ec

Observation 041610ed-f5e5-4402-bbb4-8719d5116c90 · outbound

This paper cites Paint by example: Exemplar-based image editing with diffusion models.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Paint by example: Exemplar-based image editing with diffusion models

Reference 33

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raw_fallback, observed 2026-08-11T18:40:29.094528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T18:40:28.656683Z digest=sha256:4c9ef5043c1baae5cbca5594caf48199e67e9a0bd27723a555ff239d73e5a29a

Observation 5ed338e7-4fed-45c1-8132-cb2f399d53d4 · outbound

This paper cites Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models

Reference 34

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Observation 70c85eb5-c59a-49d0-bf37-6a2b39ba0f7a · outbound

This paper cites [sks]” denoting the placeholder for subjects that might fit into the sentence. Prompts are created by replacing “[sks].

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models [sks]” denoting the placeholder for subjects that might fit into the sentence. Prompts are created by replacing “[sks]

Reference 35

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raw_fallback, observed 2026-08-11T18:40:29.067960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T18:40:28.666224Z digest=sha256:cbda6ccac815c174f47d89ab2b8c9b4cf813cbcc86b7c24c23425920ca7cfb58

Observation 56324b7f-e934-4a48-8e70-a7b506e1b063 · outbound

This paper cites stage pyrotechnics.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models stage pyrotechnics

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-11T18:40:29.051486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T18:40:28.671798Z digest=sha256:f600d4d4f5c3f6b2402555a69138f7e2bfd635508287ce99bb7ec4e68df6d6cb

Observation 8294fc73-8938-49d1-9adb-debd7f9146bb · outbound

This paper cites an unresolved cited work.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Unresolved cited work

Reference 37

Resolution
unresolved
raw_fallback, observed 2026-08-11T18:40:29.036486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T18:40:28.677687Z digest=sha256:4d17806d04ba4591a44b89ef452457854df022b24b958f0d3c6ee9607498cc6d

Observation 9d513c26-c9e2-45d8-8d86-742407f48343 · outbound

This paper cites (a) Did you state the full set of assumptions of all theoretical results? [N/A] (b) Did you include complete proofs of all theoretical results? [N/A].

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models (a) Did you state the full set of assumptions of all theoretical results? [N/A] (b) Did you include complete proofs of all theoretical results? [N/A]

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-11T18:40:28.682116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:40:28.682116Z digest=sha256:ec83813695ee6bbd320b94348ec22a972dcc0bac03b47ad64de71289f7a258e1

Observation e54029d3-36a3-42db-9116-a33dccc2d946 · outbound

This paper cites for benchmarks).

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models for benchmarks)

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:40:29.008646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T18:40:28.686288Z digest=sha256:de81c12b723a505243c80e06ec5b0e7b82eff669dbeb52bb7375255811366157

Observation fe5b9adb-78f5-4ca2-8113-15d386a35bcb · outbound

This paper cites (a) If your work uses existing assets, did you cite the creators? [Yes] See reference.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models (a) If your work uses existing assets, did you cite the creators? [Yes] See reference

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:40:28.991422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T18:40:28.690731Z digest=sha256:3ae6d97727163427fcb11bcc4b735e781ef0635b7ef79ef216fb0ee5544fe63f

Observation 39f542e4-a151-4afb-9ea6-13d56ebcf22d · outbound

This paper cites an unresolved cited work.

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Unresolved cited work

Reference 41

Resolution
unresolved
raw_fallback, observed 2026-08-11T18:40:28.973554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T18:40:28.695737Z digest=sha256:49dbc43a1ac3e00c8751b1c6053f04ed1ad85c1fb2ba0d70ab8ab9acca2ec644

Pith citing papers

No inbound Pith citation observations are available.