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Paper Citation Record · LEDGER

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution

As of 8 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 1 inbound Pith citation observation for arXiv:2506.01037.

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

pith.paper-citation-record.v1
2506.01037 v1

Coverage vector

measured 75 of 75 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:56:19.162495Z

measured 76 of 76 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T19:49:46.222837Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

75 of 75 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9f00f10b-e390-4222-a235-877903fb1e7f · outbound

This paper cites Masked siamese networks for label-efficient learning.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Masked siamese networks for label-efficient learning

Reference 1

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Observation bbf9ac7e-42af-4848-945d-545a386a0e00 · outbound

This paper cites Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models

Reference 2

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Observation 034fa7c8-621a-4c27-8b40-94772b905310 · outbound

This paper cites Video Super-Resolution Transformer.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Video Super-Resolution Transformer

Reference 3

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Observation fd6c973d-7f9c-4c9f-81c4-8b36571680bd · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assignments.Ad- vances in neural information processing systems, 33:9912– 9924, 2020.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Unsupervised learning of visual features by contrasting cluster assignments.Ad- vances in neural information processing systems, 33:9912– 9924, 2020

Reference 4

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Observation bf930bbf-24db-4c9e-88b1-a4d0f559474e · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Emerg- ing properties in self-supervised vision transformers

Reference 5

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Observation 4d40e281-f844-4a06-ba60-d3efc7100567 · outbound

This paper cites Diffusart: Enhancing line art coloriza- tion with conditional diffusion models.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Diffusart: Enhancing line art coloriza- tion with conditional diffusion models

Reference 6

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Observation 744a2e8a-27ea-4e74-9eac-b9826880cea3 · outbound

This paper cites Basicvsr: The search for essential compo- nents in video super-resolution and beyond.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Basicvsr: The search for essential compo- nents in video super-resolution and beyond

Reference 7

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Observation c8dc98ce-f4a5-46df-ae20-e00fba9e025f · outbound

This paper cites Basicvsr++: Improving video super- resolution with enhanced propagation and alignment.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Basicvsr++: Improving video super- resolution with enhanced propagation and alignment

Reference 8

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Observation 96ade946-9782-48a1-91b5-3e6317809702 · outbound

This paper cites Investigating tradeoffs in real-world video super-resolution.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Investigating tradeoffs in real-world video super-resolution

Reference 9

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Observation c678cae0-9a61-4aaf-93d2-dd633e4deb32 · outbound

This paper cites Investigating tradeoffs in real-world video super-resolution.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Investigating tradeoffs in real-world video super-resolution

Reference 10

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Observation 08e55f1c-df77-44fe-9982-d787181fba1a · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution A simple framework for contrastive learning of visual representations

Reference 11

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Observation 2cbfe812-8f91-49a7-a50d-46746cf7bbc7 · outbound

This paper cites Panda-70m: Captioning 70m videos with multiple cross-modality teachers.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Panda-70m: Captioning 70m videos with multiple cross-modality teachers

Reference 12

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Observation 2c7004ce-1bf9-4d04-b75d-c5ee06bdd6da · outbound

This paper cites Image quality assessment: Unifying structure and texture similarity.IEEE transactions on pattern analysis and ma- chine intelligence, 44(5):2567–2581, 2020.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Image quality assessment: Unifying structure and texture similarity.IEEE transactions on pattern analysis and ma- chine intelligence, 44(5):2567–2581, 2020

Reference 13

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Observation 08e4bf4f-dd56-4a17-ac8f-f687dd46bd6a · outbound

This paper cites Adaptive soft contrastive learning.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Adaptive soft contrastive learning

Reference 14

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Observation 7013b0cd-e4eb-4149-a0c7-3bd7e1ebe931 · outbound

This paper cites Maskcon: Masked con- trastive learning for coarse-labelled dataset.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Maskcon: Masked con- trastive learning for coarse-labelled dataset

Reference 15

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Observation b6bbb30d-ee4d-40ae-b4b0-abe6715fe4a3 · outbound

This paper cites SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise

Reference 16

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Observation 6e27ce29-2ea3-48dd-9113-b7db7af17246 · outbound

This paper cites Self-supervised representation learning with cross-context learning between global and hypercolumn features.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Self-supervised representation learning with cross-context learning between global and hypercolumn features

Reference 17

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Observation 148f795e-4f59-4b70-ba5c-6daab419e784 · outbound

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

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 18

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Observation 5b82160f-94bc-472c-9405-7679d9d54809 · outbound

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

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Efficiently Modeling Long Sequences with Structured State Spaces

Reference 19

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Observation fe46dd98-d737-4676-98b7-60013c3bb5aa · outbound

This paper cites AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

Reference 20

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Observation 4e3bf6c2-c51f-44f6-92fa-6adfdd164a2e · outbound

This paper cites Diagonal state spaces are as effective as structured state spaces.Advances in Neural Information Processing Systems, 35:22982–22994,.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Diagonal state spaces are as effective as structured state spaces.Advances in Neural Information Processing Systems, 35:22982–22994,

Reference 21

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Observation a97c5b24-41e1-4cb2-939c-ae5a130a1c56 · outbound

This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Momentum contrast for unsupervised visual rep- resentation learning

Reference 22

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Observation 9910dc5b-7ef2-4f60-9bc7-d5728fb04e7f · outbound

This paper cites Masked autoencoders are scalable vision learners.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Masked autoencoders are scalable vision learners

Reference 23

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Observation 65b7ebd9-6e9b-4ff2-9ed3-6637e4870e4d · outbound

This paper cites Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 24

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Observation 04ab455e-7d80-4303-b027-f15d7e9c902d · outbound

This paper cites Imagen Video: High Definition Video Generation with Diffusion Models.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Imagen Video: High Definition Video Generation with Diffusion Models

Reference 25

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Observation d95a4dd6-e385-432e-b431-a6a7c3d0a2d9 · outbound

This paper cites Long movie clip classification with state-space video models.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Long movie clip classification with state-space video models

Reference 26

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Observation 05b132ca-3350-47d3-bd66-e4a89549bcb6 · outbound

This paper cites Video super-resolution with recurrent structure-detail network.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Video super-resolution with recurrent structure-detail network

Reference 27

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Observation 782e1d3d-8e88-4fd9-955d-b03dc6ed0248 · outbound

This paper cites Video super-resolution with temporal group attention.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Video super-resolution with temporal group attention

Reference 28

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Observation 43734d44-ce6c-43d8-b085-185eca1a3953 · outbound

This paper cites Revisiting Temporal Modeling for Video Super-resolution.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Revisiting Temporal Modeling for Video Super-resolution

Reference 29

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Observation d40aa5c5-edd8-4fd6-888e-50b24542005f · outbound

This paper cites Dynamic filter networks.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Dynamic filter networks

Reference 30

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Observation 223621cb-6c2c-45aa-a991-396fd551fb62 · outbound

This paper cites Deep video super-resolution network using dynamic upsampling filters without explicit motion compen- sation.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Deep video super-resolution network using dynamic upsampling filters without explicit motion compen- sation

Reference 31

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Observation 44e25c19-ab90-41a4-9c47-2f2469fe3a27 · outbound

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

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution A new approach to linear filtering and prediction problems

Reference 32

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Observation 8aa48af0-b0f2-422f-8305-d8e8dc439693 · outbound

This paper cites Denoising diffusion restoration models.Advances in Neural Information Processing Systems, 35:23593–23606,.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Denoising diffusion restoration models.Advances in Neural Information Processing Systems, 35:23593–23606,

Reference 33

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Observation 18a81fa8-9fdc-4f2c-bf74-3e406f793a86 · outbound

This paper cites Imagic: Text-based real image editing with diffusion models.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Imagic: Text-based real image editing with diffusion models

Reference 34

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source=pdf_text observed=2026-08-07T11:56:19.018299Z digest=sha256:695cac4ded627062294f9aff2c2961de20c2753f8c2778c137be383c91c0629f

Observation 38674a40-0290-45d6-9d8f-69d13abb8e98 · outbound

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

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Musiq: Multi-scale image quality transformer

Reference 35

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source=pdf_text observed=2026-08-07T11:56:19.021791Z digest=sha256:5b333c093ca404f186c84fd4d0fe5873ca6ebb783feef67dd78e47b2274cda0f

Observation 03f230e9-0ed8-46c5-905f-ba60299019ac · outbound

This paper cites A method for stochastic optimization.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution A method for stochastic optimization

Reference 36

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raw_fallback, observed 2026-08-07T11:56:19.589464Z

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=2026-08-07T11:56:19.025486Z digest=sha256:1e173aaf16b0a9483614cacabd949dabc468c4cd1b50f7c2fd1a434d7b972842

Observation b7309f44-be06-43a5-80ac-39482b801f2d · outbound

This paper cites Open-sora-plan, 2024.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Open-sora-plan, 2024

Reference 37

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raw_fallback, observed 2026-08-07T11:56:19.580191Z

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=2026-08-07T11:56:19.029779Z digest=sha256:96a44629ad0b7a1d6b50c1a57d40e9c444c27d8da1a4146b1f2626103aa6c84d

Observation ddd0b48b-9a92-4697-bb98-d15d1041a9ce · outbound

This paper cites Learning blind video temporal consistency.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Learning blind video temporal consistency

Reference 38

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raw_fallback, observed 2026-08-07T11:56:19.571646Z

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=2026-08-07T11:56:19.033972Z digest=sha256:9ffcadb62bf865b1a4f927c638728b0a2ef5189a151ecc3b3ef4b6b4e42daf0b

Observation a9424935-0413-47ce-867a-e7566c36f8fc · outbound

This paper cites Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data

Reference 39

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source=pdf_text observed=2026-08-07T11:56:19.037863Z digest=sha256:c99426cff52d15e016b1a9182bff85e7912be0b2ab2941cbb17dd97986e6f6be

Observation d403d388-a4a2-497d-8669-825f62bdf79a · outbound

This paper cites Mucan: Multi-correspondence aggregation net- work for video super-resolution.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Mucan: Multi-correspondence aggregation net- work for video super-resolution

Reference 40

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raw_fallback, observed 2026-08-07T11:56:19.561706Z

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=2026-08-07T11:56:19.042239Z digest=sha256:b0eb6e28b8b92431c886768f701f7c8ca8c6b2814d23ec183ce480cd718246ed

Observation ca1c07ec-e538-4452-b756-1cb097e3f3d9 · outbound

This paper cites PointMamba: A Simple State Space Model for Point Cloud Analysis.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution PointMamba: A Simple State Space Model for Point Cloud Analysis

Reference 41

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source=pdf_text observed=2026-08-07T11:56:19.045549Z digest=sha256:8c238184c48cf35cb9d0a09215797244145814c81336ba2e7655a1142b32dd0d

Observation 6bb4e000-6d37-4389-9cb9-c212f5a72f7c · outbound

This paper cites Recurrent video restoration trans- former with guided deformable attention.Advances in Neu- ral Information Processing Systems, 35:378–393, 2022.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Recurrent video restoration trans- former with guided deformable attention.Advances in Neu- ral Information Processing Systems, 35:378–393, 2022

Reference 42

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:56:19.049545Z digest=sha256:52029f0e0f138780ea8a61ebbf915c155ede0916afcbb941bd9843eb6e5e2cf7

Observation ce7cba02-52de-42ff-bef1-2fc68aba6189 · outbound

This paper cites Vrt: A video restoration transformer.IEEE Transactions on Image Processing, 2024.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Vrt: A video restoration transformer.IEEE Transactions on Image Processing, 2024

Reference 43

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:56:19.053105Z digest=sha256:a75f23897da805aa5ee8cb66048ed0e6697b118c258cf7af221974681d8ea1be

Observation 1d98475d-f21d-4495-9dfc-db6221668d82 · outbound

This paper cites On bayesian adaptive video super resolution.IEEE transactions on pattern analysis and ma- chine intelligence, 36(2):346–360, 2013.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution On bayesian adaptive video super resolution.IEEE transactions on pattern analysis and ma- chine intelligence, 36(2):346–360, 2013

Reference 44

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raw_fallback, observed 2026-08-07T11:56:19.542651Z

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=2026-08-07T11:56:19.056556Z digest=sha256:e05bb1a8ce3e1156e70dfb07e15c3f58b276cc31634ee523a79e7e7b1dfbdb82

Observation 3a07e878-1866-4710-9f0f-cfe9e9fa317f · outbound

This paper cites Repaint: Inpainting using denoising diffusion probabilistic models.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Repaint: Inpainting using denoising diffusion probabilistic models

Reference 45

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:56:19.060556Z digest=sha256:5d3a1a5d2814d248f93988b77a5a89abc9d07b3d737ee90635b29b3e7ee9dfe9

Observation ef17ecad-34f4-4a34-b76b-6bdd1af0a92b · outbound

This paper cites completely blind.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution completely blind

Reference 46

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:56:19.064156Z digest=sha256:aad730fbc2a0075a91cbce44a95f595932fba3607b7f05a4280be30ca0ec4861

Observation e6091e67-fc28-4119-8761-aee7266762c1 · outbound

This paper cites Ntire 2019 challenge on video deblurring and super- resolution: Dataset and study.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Ntire 2019 challenge on video deblurring and super- resolution: Dataset and study

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:56:19.524146Z

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=2026-08-07T11:56:19.067602Z digest=sha256:427a787deea825731b6ea6b6f48c9754de465ccef6226873770d0a7db2d75c4f

Observation 8fea2f14-4ab8-4cdc-918d-5ebf529144c9 · outbound

This paper cites S4nd: Modeling images and videos as multidimensional signals with state spaces.Advances in neural information processing systems, 35:2846–2861, 2022.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution S4nd: Modeling images and videos as multidimensional signals with state spaces.Advances in neural information processing systems, 35:2846–2861, 2022

Reference 48

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raw_fallback, observed 2026-08-07T11:56:19.514873Z

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=2026-08-07T11:56:19.070486Z digest=sha256:fb74acb666f9cb0377dcca33500f4ff44ce3ea476e8aa0136b83eca733b2256b

Observation 4a15a2d3-e629-4d84-b1e1-c5bf9d510209 · outbound

This paper cites Deep blind video super-resolution.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Deep blind video super-resolution

Reference 49

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raw_fallback, observed 2026-08-07T11:56:19.505303Z

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=2026-08-07T11:56:19.074315Z digest=sha256:c8878c89751ad6a1011155375e0c949fc6964d335f98d2bc71c1f0b2816e8749

Observation 57d1a527-e4bd-44ff-82ca-b4125f600380 · outbound

This paper cites Movie Gen: A Cast of Media Foundation Models.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Movie Gen: A Cast of Media Foundation Models

Reference 50

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no resolver link, observed 2026-08-07T11:56:19.077238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:56:19.077238Z digest=sha256:e388bbd87ff93ac00de1c9e5388513fb6dcaba85c099161bfb9195b2f72579e1

Observation f8112753-8235-4392-a3e4-1381d51fdaa4 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 51

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:56:19.080961Z digest=sha256:3a434835efb15c0d2ae564b70180f50518e7bd0ea7488a1634757befc1f23195

Observation 59e2db3e-f0e8-4533-869d-905c587388b5 · outbound

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

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution High-resolution image synthesis with latent diffusion models

Reference 52

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source=pdf_text observed=2026-08-07T11:56:19.084715Z digest=sha256:cc841a422b23d009fc31085ceb8cc1e985523ffe985eecf126c43c3e41a998f5

Observation 2798be9a-5a35-45c8-9da8-fee00f15bd42 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep 10 language understanding.Advances in neural information processing systems, 35:36479–36494, 2022.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Photorealistic text-to-image diffusion models with deep 10 language understanding.Advances in neural information processing systems, 35:36479–36494, 2022

Reference 53

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raw_fallback, observed 2026-08-07T11:56:19.491482Z

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=2026-08-07T11:56:19.087986Z digest=sha256:46eac24001479e5adc1d5dbb4f6944d3db9e0e4ad0c6cf2b8ed76c498c71c4f8

Observation 97c8fec1-98f3-491e-8971-6eab84adfdd6 · outbound

This paper cites Simplified State Space Layers for Sequence Modeling.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Simplified State Space Layers for Sequence Modeling

Reference 54

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:56:19.091847Z digest=sha256:7e802f148c1f7e9d09f2c9fa59c7d8d36d60dae3abaf5a4a7b917eb8c671381e

Observation 8dfc46bb-dc6a-4a71-90d9-e02d9d6d6959 · outbound

This paper cites Detail-revealing deep video super-resolution.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Detail-revealing deep video super-resolution

Reference 55

Resolution
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raw_fallback, observed 2026-08-07T11:56:19.481668Z

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=2026-08-07T11:56:19.095322Z digest=sha256:cb93b5d134035f289df227a1a94eefcdb1181c56a4eec8ab9a73a25dd951bed6

Observation d700ec39-fa82-4f74-807f-f48898f9028f · outbound

This paper cites Ex- ploring clip for assessing the look and feel of images.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Ex- ploring clip for assessing the look and feel of images

Reference 56

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:56:19.098349Z digest=sha256:59600bfe853a3724a590f70ceb1a850057212ac4285d81da24f72471058c677a

Observation 69de7a97-542b-4022-b964-9bb34b243741 · outbound

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

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Selective structured state-spaces for long-form video understanding

Reference 57

Resolution
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raw_fallback, observed 2026-08-07T11:56:19.468079Z

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=2026-08-07T11:56:19.101551Z digest=sha256:0c4e4435fdce3a528fc7d8ddbdf21d33c3f75a543b8fb264ae59f01a65629f6e

Observation 75a53956-49ee-4c89-99dc-31ab2df6d8ee · outbound

This paper cites Exploiting diffusion prior for real-world image super-resolution.International Journal of Computer Vision, pages 1–21, 2024.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Exploiting diffusion prior for real-world image super-resolution.International Journal of Computer Vision, pages 1–21, 2024

Reference 58

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raw_fallback, observed 2026-08-07T11:56:19.458846Z

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=2026-08-07T11:56:19.104900Z digest=sha256:d7ac3ddb9a74e81557311d5b4b11067fe2182f13128ac30abdf41e0fa0c1b20f

Observation e7d98d0b-54b0-431e-9b94-83ee8403368d · outbound

This paper cites Exploiting diffusion prior for real-world image super-resolution.International Journal of Computer Vision, pages 1–21, 2024.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Exploiting diffusion prior for real-world image super-resolution.International Journal of Computer Vision, pages 1–21, 2024

Reference 59

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raw_fallback, observed 2026-08-07T11:56:19.449176Z

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=2026-08-07T11:56:19.108188Z digest=sha256:00aeeee8616a656a553c0c0769113edf0f04c199c3f1fd125783452f83f3d784

Observation d4fe3596-daa3-43c2-be3f-8debe269f603 · outbound

This paper cites Edvr: Video restoration with enhanced deformable convolutional networks.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Edvr: Video restoration with enhanced deformable convolutional networks

Reference 60

Resolution
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raw_fallback, observed 2026-08-07T11:56:19.440295Z

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=2026-08-07T11:56:19.111280Z digest=sha256:f646c1f5db48735f36ba06e84d867049ee065bc910db883963fbef8e3dc25304

Observation ea11cdb6-f5e0-41a2-a489-71bf545445bd · outbound

This paper cites Real-esrgan: Training real-world blind super-resolution with pure synthetic data.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Real-esrgan: Training real-world blind super-resolution with pure synthetic data

Reference 61

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:56:19.114233Z digest=sha256:77d7d721cd6eb9e268aa9a70072637428958a90f960bab0737f73440cc907dd6

Observation 944ced1e-847e-44df-93c3-334dc73bc601 · outbound

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

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Exploring video quality assessment on user gener- ated contents from aesthetic and technical perspectives

Reference 62

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source=pdf_text observed=2026-08-07T11:56:19.117580Z digest=sha256:03a00246ba95c4c27b1e11cf949f8c4858e8c52adafde33e1c78aa8018bd56cb

Observation 3431231e-5a09-44bd-aba1-01d4ece6f8a0 · outbound

This paper cites Mitigating artifacts in real-world video super-resolution models.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Mitigating artifacts in real-world video super-resolution models

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:56:19.421763Z

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=2026-08-07T11:56:19.120737Z digest=sha256:dc21faa26e5cb13ad358923670291c718f575954f81dbfe664654f77afa4c555

Observation fe7b2e1b-71ac-45c7-8d93-faf923e973be · outbound

This paper cites Simmim: A simple framework for masked image modeling.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Simmim: A simple framework for masked image modeling

Reference 64

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no resolver link, observed 2026-08-07T11:56:19.123676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:56:19.123676Z digest=sha256:2a6e00a4dddc24d9f9f2fd1b6b40042e03ed52d96f712b1dbcb63ab7a8aff126

Observation f0e18d28-a00f-43b0-9f9b-c32a0a177aed · outbound

This paper cites Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation

Reference 65

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raw_fallback, observed 2026-08-07T11:56:19.407420Z

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=2026-08-07T11:56:19.126732Z digest=sha256:8f425d06f71129ff122c9325349c91f0d4512bb37da787bae6e8f4ff29da5b39

Observation 010f26d6-032f-4b29-bab3-e1d34d55fe8f · outbound

This paper cites Video enhancement with task-oriented flow.International Journal of Computer Vision, 127:1106– 1125, 2019.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Video enhancement with task-oriented flow.International Journal of Computer Vision, 127:1106– 1125, 2019

Reference 66

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verified fuzzy
raw_fallback, observed 2026-08-07T11:56:19.397162Z

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=2026-08-07T11:56:19.130273Z digest=sha256:8e3d92d3ba59e056df6e2dd2f6c7671d1cfb3f7fe18b543376a05984a9ed5540

Observation a474a792-3a00-4a0d-a88b-2a6f07e2294b · outbound

This paper cites Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylization.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylization

Reference 67

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:56:19.133724Z digest=sha256:9e476ced48ef55c07b19e64e9c500ea89d971d68812b67403aeb74f7d2b0f7bb

Observation 75a3a9f5-0702-4158-8476-2ce9929352d9 · outbound

This paper cites Real- world video super-resolution: A benchmark dataset and a de- composition based learning scheme.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Real- world video super-resolution: A benchmark dataset and a de- composition based learning scheme

Reference 68

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verified fuzzy
raw_fallback, observed 2026-08-07T11:56:19.387991Z

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=2026-08-07T11:56:19.137121Z digest=sha256:cf382658c35d56f79adea6615ee05a26bbd689aa854dd204cb53ac1bcd910a5e

Observation 54a8545f-f868-4e66-a3f0-3bf0d5276820 · outbound

This paper cites Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolution.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolution

Reference 69

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local_arxiv, observed 2026-08-07T11:56:19.222507Z

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.

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Observation 35040288-4fdf-4d89-b6ae-35dc15780646 · outbound

This paper cites Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations

Reference 70

Resolution
verified fuzzy
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Observation 93e61fcd-4dd4-4d7d-ba6b-acccdb0185d9 · outbound

This paper cites Adding conditional control to text-to-image diffusion models, 2023.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Adding conditional control to text-to-image diffusion models, 2023

Reference 71

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 9f539182-1f0d-4ecd-abbc-9eaee0c08d32 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution The unreasonable effectiveness of deep features as a perceptual metric

Reference 72

Resolution
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Unavailable: canonical work link unavailable.

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Observation a8168367-a5fe-45b4-bace-999f90d27620 · outbound

This paper cites RealViformer: Investigating Attention for Real-World Video Super-Resolution.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution RealViformer: Investigating Attention for Real-World Video Super-Resolution

Reference 73

Resolution
verified exact
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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.

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Observation 990d8255-c654-4444-b7e3-21c4e87d460d · outbound

This paper cites Upscale-a-video: Temporal- consistent diffusion model for real-world video super- resolution.

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Upscale-a-video: Temporal- consistent diffusion model for real-world video super- resolution

Reference 74

Resolution
verified fuzzy
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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.

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Observation 0fefa1b4-b97e-4d33-a412-3401a32da63a · outbound

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

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 75

Resolution
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Unavailable: canonical work link unavailable.

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Pith citing papers

Observation 8f21914b-3fef-446a-a88f-f5a3a8ad87b2 · inbound

VEMamba: Efficient Isotropic Reconstruction of Volume Electron Microscopy with Axial-Lateral Consistent Mamba cites this paper.

VEMamba: Efficient Isotropic Reconstruction of Volume Electron Microscopy with Axial-Lateral Consistent Mamba Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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