OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions | Paper Citation Record

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This paper cites Obc306:Alarge-scaleoraclebonecharacterrecognition dataset.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Obc306:Alarge-scaleoraclebonecharacterrecognition dataset

Reference 1

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Observation 2751afaa-bf50-46df-9905-b4ad6aac01e6 · outbound

This paper cites Comparison of different image denoising algorithms for chinese calligraphy images.Neurocomputing, 188:102–112, 2016.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Comparison of different image denoising algorithms for chinese calligraphy images.Neurocomputing, 188:102–112, 2016

Reference 2

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This paper cites Restora- tion method of characters on jiagu rubbings based on poisson distri- bution and fractal geometry.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Restora- tion method of characters on jiagu rubbings based on poisson distri- bution and fractal geometry

Reference 3

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This paper cites Restoration of degraded historical document image: Anadaptive multilayer-informationbinarization technique.J.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Restoration of degraded historical document image: Anadaptive multilayer-informationbinarization technique.J

Reference 4

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This paper cites Robust kronecker-decomposablecomponentanalysisforlow-rankmodeling.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Robust kronecker-decomposablecomponentanalysisforlow-rankmodeling

Reference 5

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This paper cites Robust kroneckercomponentanalysis.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Robust kroneckercomponentanalysis

Reference 6

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This paper cites Kroneckercomponentwithrobustlow-rank dictionary for image denoising.Displays, 74:102194, 2022.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Kroneckercomponentwithrobustlow-rank dictionary for image denoising.Displays, 74:102194, 2022

Reference 7

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This paper cites Robust low-rank analysis with adaptive weighted tensor for image denoising.Displays, 73:102200, 2022.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Robust low-rank analysis with adaptive weighted tensor for image denoising.Displays, 73:102200, 2022

Reference 8

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This paper cites Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising

Reference 9

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This paper cites Restormer: Efficient transformer for high-resolution image restoration.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Restormer: Efficient transformer for high-resolution image restoration

Reference 10

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Observation e7de51be-b656-4c03-a12d-e3e92a1d241d · outbound

This paper cites Rcrn: Real-world character image restoration network via skeleton extraction.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Rcrn: Real-world character image restoration network via skeleton extraction

Reference 11

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Observation 653ad3c1-e91a-48b5-bbc7-777b69f78f9b · outbound

This paper cites Charformer: A glyph fusion based attentive framework for high-precision character image denoising.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Charformer: A glyph fusion based attentive framework for high-precision character image denoising

Reference 12

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Observation f0ce02fa-18a3-47d7-968b-7a22d741da84 · outbound

This paper cites Self-supervised learning of orc-bert augmentator for recog- nizing few-shot oracle characters.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Self-supervised learning of orc-bert augmentator for recog- nizing few-shot oracle characters

Reference 13

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This paper cites Unsupervised structure-texture separation network for oracle character recognition.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Unsupervised structure-texture separation network for oracle character recognition

Reference 14

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Observation 42fca2ab-b1e7-4fea-93a8-3f43243205ff · outbound

This paper cites Obi- bench:Canlmmsaidinstudyofancientscriptonoraclebones?,2025.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Obi- bench:Canlmmsaidinstudyofancientscriptonoraclebones?,2025

Reference 15

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Observation f0a5df26-8147-464d-a67d-4caeb3a1b133 · outbound

This paper cites Hwobc-ahandwritingoraclebonecharacterrecognition database.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Hwobc-ahandwritingoraclebonecharacterrecognition database

Reference 16

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Observation 1c13ca44-08c1-47da-b750-4a8e5c3d9776 · outbound

This paper cites Study on the evolution of chinese characters based on few-shot learning: From oracle bone inscriptions to regular script.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Study on the evolution of chinese characters based on few-shot learning: From oracle bone inscriptions to regular script

Reference 17

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This paper cites Dy- namic dataset augmentation for deep learning-based oracle bone inscriptions recognition.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Dy- namic dataset augmentation for deep learning-based oracle bone inscriptions recognition

Reference 18

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Observation 683f73f8-1511-4fef-916b-84f91120adb4 · outbound

This paper cites An open dataset for the evolution of oracle bone characters: EVOBC.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions An open dataset for the evolution of oracle bone characters: EVOBC

Reference 19

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This paper cites Building hierarchical representations for oracle character and sketch recognition.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Building hierarchical representations for oracle character and sketch recognition

Reference 20

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This paper cites Accurateoracleclassificationbasedondeepconvolu- tionalneuralnetwork.In 2018IEEE18thInternationalConferenceon Communication Technology (ICCT), pages 1188–1191.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Accurateoracleclassificationbasedondeepconvolu- tionalneuralnetwork.In 2018IEEE18thInternationalConferenceon Communication Technology (ICCT), pages 1188–1191

Reference 21

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Observation b2b06f1d-7aa2-4be0-ad26-9c379fac6631 · outbound

This paper cites Deep self-supervised learning for oracle bone inscriptions features representation.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Deep self-supervised learning for oracle bone inscriptions features representation

Reference 22

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Observation 6c14b8ab-fca2-4ef7-8069-89a9970e7216 · outbound

This paper cites Large-scale oracle bone inscriptions dataset construc- tionandalgorithmresearch.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Large-scale oracle bone inscriptions dataset construc- tionandalgorithmresearch

Reference 23

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Observation 78935b64-f810-48fc-9db9-8e64afe530c4 · outbound

This paper cites Oracle bone inscriptions recognition based on deep convolutional neural network.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Oracle bone inscriptions recognition based on deep convolutional neural network

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:10:34.208274Z

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This paper cites Ai-powered oracle bone inscriptions recognition and fragments rejoining.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Ai-powered oracle bone inscriptions recognition and fragments rejoining

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:10:34.194170Z

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Observation 0bd35278-10b1-457f-ade7-df2b81ca5c4f · outbound

This paper cites Recognition of oracle bone inscriptions by using two deep learning models.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Recognition of oracle bone inscriptions by using two deep learning models

Reference 26

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Observation 1a74f79c-669c-4753-8f56-d860fff4cabf · outbound

This paper cites Data-driven oracle bone rejoining: A dataset and practical self-supervised learning scheme.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Data-driven oracle bone rejoining: A dataset and practical self-supervised learning scheme

Reference 27

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Observation 2bf9da9c-1424-4508-95e8-f31a2ce8ef99 · outbound

This paper cites A dataset of oracle characters for benchmarking machine learning algorithms.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions A dataset of oracle characters for benchmarking machine learning algorithms

Reference 28

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This paper cites An open dataset for oracle bone script recognition and decipherment.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions An open dataset for oracle bone script recognition and decipherment

Reference 29

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This paper cites Mitigating long-tail distribution in oracle bone inscriptions: Dataset, model, and benchmark, 2025.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Mitigating long-tail distribution in oracle bone inscriptions: Dataset, model, and benchmark, 2025

Reference 30

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Observation 6f7fefe6-7747-4626-ba29-117bf458d506 · outbound

This paper cites Oracle bone inscriptions in the collection of shanghai museum (volume i), 2009.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Oracle bone inscriptions in the collection of shanghai museum (volume i), 2009

Reference 31

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Observation b715ccbc-faa9-4566-940a-d484cd134256 · outbound

This paper cites Gradient-based learning applied to document recognition.Proceed- ings of the IEEE, 86(11):2278–2324, 1998.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Gradient-based learning applied to document recognition.Proceed- ings of the IEEE, 86(11):2278–2324, 1998

Reference 32

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Observation d18b9195-6ed5-463a-b099-2fa578073676 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.Advances in neural information processing systems, 25, 2012.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Imagenet classification with deep convolutional neural networks.Advances in neural information processing systems, 25, 2012

Reference 33

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This paper cites Inception-v4, inception-resnet and the impact of residual connections on learning.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Inception-v4, inception-resnet and the impact of residual connections on learning

Reference 34

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

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 35

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This paper cites Deep residual learning for image recognition.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Deep residual learning for image recognition

Reference 36

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This paper cites Oracle character recognition by nearest neighbor classifica- tion with deep metric learning.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Oracle character recognition by nearest neighbor classifica- tion with deep metric learning

Reference 37

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This paper cites Oraclecharacterrecognition using unsupervised discriminative consistency network.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Oraclecharacterrecognition using unsupervised discriminative consistency network

Reference 38

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This paper cites Statistical techniques for digital pre-processing of computed tomography medi- cal images: A current review.Displays, 85:102835, 2024.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Statistical techniques for digital pre-processing of computed tomography medi- cal images: A current review.Displays, 85:102835, 2024

Reference 39

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This paper cites Lesion-inspired denoising network: Connecting medical image denoising and lesion detection.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Lesion-inspired denoising network: Connecting medical image denoising and lesion detection

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:10:34.049072Z

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This paper cites Indeandcoe:Aframeworkbasedonmulti-scalefeaturefusion and residual learning for interferometric sar remote sensing image denoising and coherence estimation.Displays, 79:102496, 2023.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Indeandcoe:Aframeworkbasedonmulti-scalefeaturefusion and residual learning for interferometric sar remote sensing image denoising and coherence estimation.Displays, 79:102496, 2023

Reference 41

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Observation a4011a56-82ff-445b-9629-fd1b25fb6e5e · outbound

This paper cites Selectiveresidualm-netforrealimagedenoising.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Selectiveresidualm-netforrealimagedenoising

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:10:34.021971Z

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Observation 4c0c0267-c53f-4608-b6dd-8a230971d126 · outbound

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

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions U-net: Con- volutional networks for biomedical image segmentation

Reference 43

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This paper cites KBNet: Kernel Basis Network for Image Restoration.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions KBNet: Kernel Basis Network for Image Restoration

Reference 44

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This paper cites Invertible denoising network: A light solution for real noise removal.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Invertible denoising network: A light solution for real noise removal

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:10:34.001455Z

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Observation f5a7225a-2fa8-47e1-9815-f91e3c1bd528 · outbound

This paper cites Dual adversarial network: Toward real-world noise removal and noise generation.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Dual adversarial network: Toward real-world noise removal and noise generation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:10:33.988328Z

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Observation 06e8d47c-875d-45a2-a91f-df6fe4ec8632 · outbound

This paper cites Animageisworth16x16words:Transformersfor image recognition at scale.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Animageisworth16x16words:Transformersfor image recognition at scale

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:10:33.975172Z

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Observation 918891aa-e1cb-443f-9f7d-1a3cabf3ebee · outbound

This paper cites Uformer: A general u-shaped transformer for image restoration.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Uformer: A general u-shaped transformer for image restoration

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:10:33.961818Z

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Observation e8489153-8246-4e5e-857c-049a513973b8 · outbound

This paper cites Cascadedgaze: Efficiency in global context extraction for image restoration.Trans- actions on Machine Learning Research, 2024.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Cascadedgaze: Efficiency in global context extraction for image restoration.Trans- actions on Machine Learning Research, 2024

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:10:33.948611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ebf42736-e12a-43e6-b88e-921ca0757ea5 · outbound

This paper cites Selectivekernel networks.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Selectivekernel networks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:10:33.931437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 489ea0d8-fc10-40ec-8d2e-53e184505e53 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Imagenet: A large-scale hierarchical image database

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-16T12:10:33.713772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64bddf79-24ff-45b7-a324-7cb762ef22e8 · outbound

This paper cites Chinesecharacters strokethinningandextractionbasedonmathematicalmorphology[j].

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Chinesecharacters strokethinningandextractionbasedonmathematicalmorphology[j]

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:10:33.909660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 283a3dc8-7fa1-4bbb-b55a-46621a356221 · outbound

This paper cites Jiaguwen zixing biao (a list of oracle characters).

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Jiaguwen zixing biao (a list of oracle characters)

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:10:33.896311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5c9a3f49-1240-4c69-88e9-9eb14436de03 · outbound

This paper cites Decoupled Weight Decay Regularization.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Decoupled Weight Decay Regularization

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-16T12:10:33.726103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 943dc2be-849c-4930-a4e4-41817da30134 · outbound

This paper cites Quality Assessment in the Era of Large Models: A Survey.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Quality Assessment in the Era of Large Models: A Survey

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-16T12:10:33.730197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d86d2b7b-ab6c-40d5-a672-1d5d8d1c8dcd · outbound

This paper cites A-Bench: Are LMMs Masters at Evaluating AI-generated Images?.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions A-Bench: Are LMMs Masters at Evaluating AI-generated Images?

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-16T12:10:33.734620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23df172d-7c4a-4f0d-96a8-597794770167 · outbound

This paper cites Q-Bench-Video: Benchmarking the Video Quality Understanding of LMMs.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Q-Bench-Video: Benchmarking the Video Quality Understanding of LMMs

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-16T12:10:33.738860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c787c68a-42c1-4eef-a6ea-04fc55324794 · outbound

This paper cites Study of subjective and objective naturalness assessment of ai-generated images.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Study of subjective and objective naturalness assessment of ai-generated images

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:10:33.883688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 34a11c54-842b-4b84-ae76-0a5ad4e2b0d7 · outbound

This paper cites Image quality metrics: Psnr vs.

OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions Image quality metrics: Psnr vs

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:10:33.871399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers