Pith. sign in

REVIEW 4 major objections 6 minor 41 references

MambaVSR: Content-Aware Scanning State Space Model for Video Super-Resolution

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read MambaVSR, a state-space video super-resolution model using content-aware scanning, beats transformer-based VRT by 0.58 dB PSNR on REDS4 with 55% fewer parameters.

desk verdict The content-aware scanning mechanism is genuinely novel, but the paper is too under-specified to reproduce and the theoretical analysis promised in the contributions is not delivered. read the letter →

arxiv 2506.11768 v1 pith:WFCW6NT4 submitted 2025-06-13 cs.CV

Linfeng He , Meiqin Liu , Qi Tang , Chao Yao , Yao Zhao This is my paper
classification cs.CV
keywords videosuper-resolutionstatespacemodelcontent-awarescanningspectralclusteringFiedlervectornon-localdependencieslinearcomplexitytemporalalignment
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that video super-resolution can be done better and cheaper by replacing the rigid 1D scanning of state-space models with a content-aware scan order that adapts to each video's structure. It introduces MambaVSR, the first state-space model framework for VSR, which builds a per-frame semantic affinity graph, derives a scan order from it via spectral clustering, and interleaves features from coarsely aligned frames along that order so that similar content from different frames becomes adjacent before the selective SSM processes them. The paper reports that this design outperforms the transformer-based VRT by 0.58 dB PSNR on REDS4 while using 55% fewer parameters, and beats the Swin-transformer PSRT-recurrent by 0.22 dB on Vid4. A sympathetic reader should care because it suggests that the bottleneck in state-space vision models is not the SSM itself but the sequentialization order, and that a content-derived order can give linear-complexity models transformer-level non-local aggregation.

What carries the argument

The load-bearing object is the content-aware scanner built from three cooperating modules. Shared Compass Construction (SCC) downsamples features, computes a sparse dual-branch attention similarity matrix, forms a Laplacian, and extracts its Fiedler vector — the eigenvector of the graph Laplacian associated with the second-smallest eigenvalue, whose sorted components give a 1D ordering that approximately groups similar nodes — to produce a shared spatial scan order used across refinement stages. Content-Aware Sequentialization (CAS) then uses patch alignment to reuse this order across frames and interleaves spatial features along it, so temporally matched similar content becomes adjacent in the SSM input sequence. The Global-Local State Space Block (GLSSB) wraps the SSM propagation inside fixed windows and fuses it with window self-attention through a learnable residual parameter, so global dependency guidance and local high-frequency recovery happen in the same block. The Fiedler-vector ordering is the mechanism that converts a spatial similarity graph into a 1D scan the selective SSM can exploit with linear complexity.

What would settle it

Train MambaVSR on REDS with the SCC-generated scan order replaced by a random but fixed per-frame permutation, keeping everything else identical; if PSNR then matches the raster baseline instead of the reported 0.14 dB (REDS4) and 0.24 dB (Vid4) content-aware gains, the Fiedler ordering itself, not the interleaving or extra parameters, is the cause. As a second check, compute the Spearman rank correlation between Fiedler vectors of adjacent frames in a high-motion clip: a low correlation would show the shared compass is not stable enough to align content across frames.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes that a selective state-space model can outperform both CNN- and transformer-based video super-resolution methods when the scanning order is made content-aware rather than fixed raster or multi-directional scanning. The central mechanism is that spatially similar patches, identified through sparse attention and spectral clustering, are arranged into a sequence such that the SSM's cumulative state sees globally similar content within a frame, and after coarse patch alignment, the same order interleaves temporally corresponding features across frames. This yields non-local spatiotemporal aggregation with linear complexity, which the paper argues is what enables the reported gains: 32.75 dB PSNR on REDS4 versus 32.17 dB for VRT with 55% fewer parameters, and a 0.24 dB improvement over raster scanning on Vid4 from the content-aware sequentialization alone. The paper also presents LAM-based diffusion index comparisons and temporal-downsampling tests as evidence that the model uses a larger effective receptive field than linear-complexity CNN and Swin-transformer baselines.

Load-bearing premise

The load-bearing premise is that the Fiedler-vector scan order computed on downsampled features from sparse attention graphs is semantically meaningful and stable enough to reuse across frames after coarse alignment, so that similar patches become adjacent in the scan; if that fails, interleaving aligned features along the order would not aggregate similar content and the claimed 0.24 dB gain over raster scanning would vanish.

Editorial extensions

If this is right

  • If the reported gains hold, state-space models become a viable backbone for video restoration tasks that need long-range spatiotemporal modeling, not just efficiency.
  • The 0.58 dB lead over VRT at 55% fewer parameters implies that content-aware sequentialization can substitute for global self-attention's quadratic complexity without sacrificing non-local aggregation.
  • The 0.24 dB gain of content-aware over raster scanning on Vid4 isolates the ordering as the source of improvement, not the SSM or the window attention alone.
  • Recomputing the scan order only once per stage and reusing it across aligned frames (the shared compass) keeps the overhead of graph construction low enough that the whole model runs at 2.46 T FLOPs per 180×320 frame.
  • Longer training sequences (16 frames) and larger motion produce bigger gains, suggesting the method's advantage grows with temporal extent.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An untested corollary: the same Fiedler-vector sequentialization could be dropped into other state-space restoration tasks (deblurring, deraining, interpolation) where non-local self-similarity matters, as long as the similarity graph is computed on downsampled features.
  • One risk the paper leaves open is that the shared compass computed once per stage may drift on clips with rapid scene changes; if the Fiedler order becomes unstable across frames, CAS interleaving could mix unrelated patches. A cheap stability check would be to correlate the scan orders of adjacent frames on high-motion clips.
  • The ablation numbers suggest a testable prediction: a model using a random but fixed per-frame permutation should perform close to raster scanning, not to content-aware scanning, which would confirm that the ordering, not just the extra parameters, carries the benefit.
  • If the content-aware ordering acts mainly by placing similar textures adjacent, the method could also benefit single-image super-resolution with internal patch recurrence, where a graph over image patches already exists.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The manuscript proposes MambaVSR, a video super-resolution (VSR) network built on selective state-space models (SSMs). It introduces Shared Compass Construction (SCC), which builds intra-frame similarity graphs via efficient sparse attention and derives a spatial scan order by spectral clustering (Fiedler vector); Content-Aware Sequentialization (CAS), which reuses the current frame's scan order after coarse PatchAlignment and interleaves aligned temporal features; and the Global-Local State Space Block (GLSSB), which combines window self-attention with SSM-based feature propagation. Experiments on REDS4, Vimeo-90K-T, and Vid4 report 32.75 dB PSNR on REDS4 under 16-frame training with 14.1M parameters, outperforming VRT by 0.58 dB with 55% fewer parameters, along with ablations of the scanning mechanism and GLSSB components and additional LAM/DI and large-motion analyses.

Significance. If the reported numbers and the mechanism are correct, this is a potentially notable contribution: it would be the first state-space-model framework for VSR, and content-aware scanning would provide a way to aggregate non-local similar content across misaligned frames in linear time, a meaningful advance over optical-flow-based and quadratic-attention methods. The paper's strengths include broad benchmark coverage, comparisons against strong CNN and transformer baselines, ablations of the scanning and GLSSB modules, and the supplementary LAM/DI and large-motion experiments. However, the central mechanism is currently underspecified, the advertised 'rigorous theoretical analysis' is not delivered, and the headline Table 1 results are not reconciled with the ablation settings. These issues are load-bearing for the paper's central claims and must be resolved before the results can be accepted.

major comments (4)
  1. [Sections 3.2-3.3 and Contribution bullet 3] SCC and CAS are described only in prose: no equation or algorithm block defines the sparse-attention similarity matrix, the graph Laplacian, the Fiedler-vector computation, the spectral-clustering step, the PatchAlignment module, or the interleaving rule that produces the final scan sequence. Consequently, the claimed linear complexity cannot be checked, and the 'rigorous theoretical analysis' advertised in the third contribution bullet is not present anywhere in the paper. Please provide formal definitions and algorithm pseudocode, and either add the theoretical analysis or remove the claim.
  2. [Table 1 versus Table 2 (Sections 4.1 and 4.3)] The 6-frame MambaVSR row in Table 1 reports 14.1M parameters, 300k iterations, and 31.95 dB on REDS4, while the corresponding full content-aware model in Table 2 (14.13M parameters, 200k iterations) reports 31.82 dB, with the raster baseline at 31.68 dB. These numbers are not reconciled without an explicit statement of which configuration changes from the ablation to the main table, and no Table 2 configuration exactly matches Table 1's 6-frame entry. As a result, the headline 16-frame 32.75 dB value is not tied to a reproducible configuration, and the claimed 0.58 dB advantage over VRT lacks independent support. Please report the exact configuration of each Table 1 row and align the ablation protocol with the main training schedule.
  3. [Section 4.3, Table 2] The causal attribution of the performance advantage to content-aware scanning is not supported by the ablation evidence. Table 2 shows only a 0.09 dB REDS4 gain (31.68 to 31.82) for content-aware over raster scanning at 200k iterations, whereas the large gains over EDVR and VRT in Table 1 are measured at 300k/600k iterations. Nothing in the text demonstrates that these larger gains are caused by CAS rather than by the longer training schedule or other components. Please provide ablations at the same training schedule as the main comparison, or explicitly quantify the effect of the schedule.
  4. [Sections 3.3 and 4.3] The reused-order assumption underlying CAS is unexamined. The authors state that PatchAlignment enables reuse of the current frame's Fiedler-vector order across neighboring frames, but no analysis or experiment tests whether this order remains semantically stable under the motions present in REDS and Vid4. If the scan order does not keep similar patches adjacent after alignment, the proposed mechanism would not produce the observed gains. Please add a quantitative stability analysis (e.g., order-overlap or adjacency-consistency metrics across frames), an ablation that recomputes the order per frame, and ideally visualizations of the learned orders against motion.
minor comments (6)
  1. [Throughout (e.g., Table 2, Section 4.3)] The name 'Fielder' should be spelled 'Fiedler' throughout the paper.
  2. [Figure 2 captions] The word 'Cilp' should be 'Clip' in the figure captions.
  3. [Contribution bullet 3] The contribution list promises 'rigorous theoretical analysis', but no theoretical analysis appears in Sections 3 or 4; either provide it or reword the contribution.
  4. [Table 1 and Section 4.1] The 'Frames REDS/Vimeo' column is ambiguous for rows with a single number; please clarify which dataset each frame count refers to, especially for rows like '16/14'.
  5. [Figure 4] Axis labels and units are missing in the large-motion evaluation plot, and it is unclear whether the reported PSNR improvements are averaged over all REDS4 clips.
  6. [Section 5] The conclusion contains minor grammatical issues (e.g., 'In this paper, We present' and the missing final period after 'datasets').

Circularity Check

0 steps flagged · score 0.0 of 10

No circular reasoning found; the scanning order is computed from input features and validated against external benchmarks.

full rationale

I examined the derivation chain in Sections 3.2 through 3.4 and the experimental validation in Section 4. The central mechanism, Shared Compass Construction and Content-Aware Sequentialization, computes a Fiedler-vector spatial scan order from downsampled input features via sparse attention and spectral clustering, then interleaves temporally aligned features along that order. This order is a function of the low-resolution input, not of the target high-resolution frame, and no fitted constant is renamed as a prediction. The reported gains over VRT, PSRT, EDVR, and BasicVSR++ are empirical comparisons on held-out benchmarks such as REDS4, Vimeo-90K-T, and Vid4 under fixed training protocols, so the central claim is externally falsifiable and does not reduce to its own inputs. I found no case where a fitted parameter is called a prediction, no self-citation chain that carries a load-bearing premise, and no uniqueness theorem imported from the authors' own prior work. The ablation studies in Tables 2 and 3 compare variants under identical training settings, so attributing performance differences to the scan order or the GLSSB fusion is an ordinary controlled comparison rather than a circular reduction. The manuscript has reproducibility weaknesses, such as no code release, an underspecified graph construction, and a 6-frame Table 1 row that does not exactly match any Table 2 configuration, but those are concerns about support and correctness, not circularity, and per the review rules they do not raise the circularity score. Each stage of the method takes the preceding stage's features and produces a transformation that is evaluated against ground truth, and no step equates a definition to a target quantity. Therefore the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical entities; 'Shared Compass' is a name for a learned scan order, not a new force or particle. Free parameters are limited to unstated hyperparameters (e.g., sparsity ratio, cluster count, window size) that are not reported, so they cannot be listed with values. The design rests on two ad hoc assumptions (spectral ordering semantics and cross-frame order stability) plus standard Mamba and BasicVSR++ properties. These assumptions are empirical design choices, not circularity.

assumptions (4)
  • ad hoc to paper The Fiedler vector of a Laplacian built from sparse attention similarity gives an ordering in which semantically similar patches are adjacent.
    Invoked in Section 3.2 to justify the shared compass as a scanning order that groups salient features; no proof or empirical validation that this ordering is optimal or stable for VSR.
  • ad hoc to paper A scan order computed on a downsampled version of the current frame remains valid for neighboring frames after coarse patch alignment.
    Stated in Section 3.3: PatchAlignment 'enables reuse of the current frame's order O.' The stability of the order across frames is assumed without analysis.
  • standard math The Mamba S6 selective state-space layer provides a global receptive field with linear complexity when applied to a 1D sequence.
    Standard property cited from [7]; the paper's own complexity claims inherit this result but do not account for the added cost of sparse attention and spectral clustering.
  • domain assumption The BasicVSR++ backbone (bidirectional propagation, optical flow guidance) is a reliable base architecture for VSR.
    Used as the foundation in Section 3.1; performance is benchmarked relative to this lineage, so the method's success partly depends on the quality of this prior backbone.

how reviews work

0 comments
Cite this review

Pith. "Pith review of MambaVSR: Content-Aware Scanning State Space Model for Video Super-Resolution." pith.science (2026). https://pith.science/paper/WFCW6NT4

@misc{pith2026250611768,
  author       = {Pith},
  title        = {Pith review of: MambaVSR: Content-Aware Scanning State Space Model for Video Super-Resolution},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WFCW6NT4}},
  note         = {Machine review of arXiv:2506.11768}
}
read the original abstract

Video super-resolution (VSR) faces critical challenges in effectively modeling non-local dependencies across misaligned frames while preserving computational efficiency. Existing VSR methods typically rely on optical flow strategies or transformer architectures, which struggle with large motion displacements and long video sequences. To address this, we propose MambaVSR, the first state-space model framework for VSR that incorporates an innovative content-aware scanning mechanism. Unlike rigid 1D sequential processing in conventional vision Mamba methods, our MambaVSR enables dynamic spatiotemporal interactions through the Shared Compass Construction (SCC) and the Content-Aware Sequentialization (CAS). Specifically, the SCC module constructs intra-frame semantic connectivity graphs via efficient sparse attention and generates adaptive spatial scanning sequences through spectral clustering. Building upon SCC, the CAS module effectively aligns and aggregates non-local similar content across multiple frames by interleaving temporal features along the learned spatial order. To bridge global dependencies with local details, the Global-Local State Space Block (GLSSB) synergistically integrates window self-attention operations with SSM-based feature propagation, enabling high-frequency detail recovery under global dependency guidance. Extensive experiments validate MambaVSR's superiority, outperforming the Transformer-based method by 0.58 dB PSNR on the REDS dataset with 55% fewer parameters.

Figures

Figures reproduced from arXiv: 2506.11768 by the authors.

Figure 1
Figure 1. Illustration of four methods in VSR tasks. CNN-based methods use optical flow to warp frames, capturing limited temporal [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Visual comparison results for 4× VSR on REDS and Vid4 dataset. MambaVSR effectively restores realistic details. BasicVSR++ MambaVSR PSRT-recurrent DI: 18.13 DI: 16.51 DI: 14.75 DI: 14.64 DI: 12.86 DI: 14.68 DI: 38.41 DI: 36.98 DI: 41.23 Support Frame #1 Reference Frame Support Frame #2 Reference HR Frame and the Attribution Target Window [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Local Attribution Map (LAM) [10] results and Diffusion Index (DI) for different networks. LAM visualizes the pixel-wise importance and receptive field size in the input LR frames for re￾constructing the red-box region of the SR target frame. DI quanti￾fies the spatial extent of relevant and utilized pixels. long-range spatiotemporal dependencies, thereby attaining a larger theoretical receptive field [4]. To further… view at source ↗
Figures from the paper (1 more)
Figure 5
Figure 5. Figure 5: Effectiveness of our proposed Content-Aware Scanning. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

41 extracted references · 32 canonical work pages

  1. [1]

    Video super-resolution transformer.arXiv preprint arXiv:2106.06847, 2021

    Jiezhang Cao, Yawei Li, Kai Zhang, and Luc Van Gool. Video super-resolution transformer.arXiv preprint arXiv:2106.06847, 2021. 3, 4

    arXiv 2021

  2. [2]

    BasicVSR: The search for essential com- ponents in video super-resolution and beyond

    Kelvin CK Chan, Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. BasicVSR: The search for essential com- ponents in video super-resolution and beyond. InProceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4947–4956, 2021. 2, 4

    work page 2021

  3. [3]

    BasicVSR++: Improving video super- resolution with enhanced propagation and alignment

    Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. BasicVSR++: Improving video super- resolution with enhanced propagation and alignment. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5972–5981, 2022. 2, 3, 4

    work page 2022

  4. [4]

    Activating more pixels in image super- resolution transformer

    Xiangyu Chen, Xintao Wang, Jiantao Zhou, Yu Qiao, 6 and Chao Dong. Activating more pixels in image super- resolution transformer. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22367–22377, 2023. 5

    work page 2023

  5. [5]

    Transformers are ssms: General- ized models and efficient algorithms through structured state space duality.arXiv preprint arXiv:2405.21060, 2024

    Tri Dao and Albert Gu. Transformers are ssms: General- ized models and efficient algorithms through structured state space duality.arXiv preprint arXiv:2405.21060, 2024. 2

    arXiv 2024

  6. [6]

    An image is worth 16x16 words: Trans- formers for image recognition at scale.arXiv preprint arXiv:2010.11929, 2020

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, et al. An image is worth 16x16 words: Trans- formers for image recognition at scale.arXiv preprint arXiv:2010.11929, 2020. 3

    arXiv 2010

  7. [7]

    Mamba: Linear-time sequence modeling with selective state spaces.arXiv preprint arXiv:2312.00752, 2023

    Albert Gu and Tri Dao. Mamba: Linear-time sequence modeling with selective state spaces.arXiv preprint arXiv:2312.00752, 2023. 2, 3

    arXiv 2023

  8. [8]

    Efficiently modeling long sequences with structured state spaces.arXiv preprint arXiv:2111.00396, 2021

    Albert Gu, Karan Goel, and Christopher R ´e. Efficiently modeling long sequences with structured state spaces.arXiv preprint arXiv:2111.00396, 2021. 3

    arXiv 2021

Show all 41 references
  1. [9]

    Combining recurrent, convolutional, and continuous-time models with linear state space layers

    Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, and Christopher R ´e. Combining recurrent, convolutional, and continuous-time models with linear state space layers. InAdvances in Neural Information Processing Systems, pages 572–585, 2021. 3

    2021

  2. [10]

    Interpreting super-resolution networks with local attribution maps

    Jinjin Gu and Chao Dong. Interpreting super-resolution networks with local attribution maps. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9199–9208, 2021. 5

    2021

  3. [11]

    MambaIRv2: Attentive state space restoration.arXiv preprint arXiv:2411.15269,

    Hang Guo, Yong Guo, Yaohua Zha, Yulun Zhang, Wenbo Li, Tao Dai, Shu-Tao Xia, and Yawei Li. MambaIRv2: Attentive state space restoration.arXiv preprint arXiv:2411.15269,

    arXiv

  4. [12]

    MambaIR: A simple baseline for im- age restoration with state-space model

    Hang Guo, Jinmin Li, Tao Dai, Zhihao Ouyang, Xudong Ren, and Shu-Tao Xia. MambaIR: A simple baseline for im- age restoration with state-space model. InProceedings of the European Conference on Computer Vision, pages 222–241,

  5. [13]

    Adam: A method for stochastic optimization.arXiv preprint arXiv:1412.6980,

    Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization.arXiv preprint arXiv:1412.6980,

    arXiv

  6. [14]

    Deep laplacian pyramid networks for fast and accurate super-resolution

    WeiSheng Lai, JiaBin Huang, Narendra Ahuja, and MingH- suan Yang. Deep laplacian pyramid networks for fast and accurate super-resolution. InProceedings of the IEEE Con- ference on Computer Vision and Pattern Recognition, pages 624–632, 2017. 4

    2017

  7. [15]

    Multi-frequency representation enhancement with privi- lege information for video super-resolution

    Fei Li, Linfeng Zhang, Zikun Liu, Juan Lei, and Zhenbo Li. Multi-frequency representation enhancement with privi- lege information for video super-resolution. InProceedings of the IEEE/CVF International Conference on Computer Vi- sion, pages 12814–12825, 2023. 2

    2023

  8. [16]

    MuCAN: Multi-correspondence aggregation network for video super-resolution

    Wenbo Li, Xin Tao, Taian Guo, Lu Qi, Jiangbo Lu, and Jiaya Jia. MuCAN: Multi-correspondence aggregation network for video super-resolution. InProceedings of the European Con- ference on Computer Vision, pages 335–351, 2020. 4

    2020

  9. [17]

    Recurrent video restoration transformer with guided deformable attention

    Jingyun Liang, Yuchen Fan, Xiaoyu Xiang, Rakesh Ran- jan, Eddy Ilg, Simon Green, Jiezhang Cao, Kai Zhang, Radu Timofte, and Luc V Gool. Recurrent video restoration transformer with guided deformable attention. InAdvances in Neural Information Processing Systems, pages 378–393,

  10. [18]

    VRT: A video restoration transformer.IEEE Transactions on Image Processing, 33:2171–2182, 2024

    Jingyun Liang, Jiezhang Cao, Yuchen Fan, Kai Zhang, Rakesh Ranjan, Yawei Li, Radu Timofte, and Luc Van Gool. VRT: A video restoration transformer.IEEE Transactions on Image Processing, 33:2171–2182, 2024. 2, 3, 4

    2024

  11. [19]

    On bayesian adaptive video super resolution.IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 36(2):346–360, 2013

    Ce Liu and Deqing Sun. On bayesian adaptive video super resolution.IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 36(2):346–360, 2013. 3, 4

    2013

  12. [20]

    Learning trajectory-aware transformer for video super- resolution

    Chengxu Liu, Huan Yang, Jianlong Fu, and Xueming Qian. Learning trajectory-aware transformer for video super- resolution. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5687– 5696, 2022. 2, 4

    2022

  13. [21]

    VMamba: Visual state space model.arXiv preprint arXiv:2401.10166,

    Yue Liu, Yunjie Tian, Yuzhong Zhao, Hongtian Yu, Lingxi Xie, Yaowei Wang, Qixiang Ye, and Yunfan Liu. VMamba: Visual state space model.arXiv preprint arXiv:2401.10166,

    arXiv

  14. [22]

    SGDR: Stochas- tic gradient descent with warm restarts.arXiv preprint arXiv:1608.03983, 2016

    Ilya Loshchilov and Frank Hutter. SGDR: Stochas- tic gradient descent with warm restarts.arXiv preprint arXiv:1608.03983, 2016. 3

    2016 arXiv

  15. [23]

    You watch, you give, and you engage: a study of live streaming practices in china

    Zhicong Lu, Haijun Xia, Seongkook Heo, and Daniel Wig- dor. You watch, you give, and you engage: a study of live streaming practices in china. InProceedings of the CHI Con- ference on Human Factors in Computing Systems, pages 1– 13, 2018. 2

    2018

  16. [24]

    NTIRE 2019 challenge on video deblurring and super- resolution: Dataset and study

    Seungjun Nah, Sungyong Baik, Seokil Hong, Gyeongsik Moon, Sanghyun Son, Radu Timofte, and Kyoung Mu Lee. NTIRE 2019 challenge on video deblurring and super- resolution: Dataset and study. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Works...

    2019

  17. [25]

    Anurag Ranjan and Michael J. Black. Optical flow estima- tion using a spatial pyramid network. InProceedings of the IEEE Conference on Computer Vision and Pattern Recogni- tion, pages 2720–2729, 2016. 2, 3

    2016

  18. [26]

    Rethinking alignment in video super- resolution transformers

    Shuwei Shi, Jinjin Gu, Liangbin Xie, Xintao Wang, Yujiu Yang, and Chao Dong. Rethinking alignment in video super- resolution transformers. InAdvances in Neural Information Processing Systems, pages 36081–36093, 2022. 2, 3, 4

    2022

  19. [27]

    CTVSR: Collaborative spatial–temporal transformer for video super-resolution.IEEE Transactions on Circuits and Systems for Video Technology, 34(6):5018– 5032, 2024

    Jun Tang, Chenyan Lu, Zhengxue Liu, Jiale Li, Hang Dai, and Yong Ding. CTVSR: Collaborative spatial–temporal transformer for video super-resolution.IEEE Transactions on Circuits and Systems for Video Technology, 34(6):5018– 5032, 2024. 2, 4

    2024

  20. [28]

    TDAN: Temporally-deformable alignment network for video super-resolution

    Yapeng Tian, Yulun Zhang, Yun Fu, and Chenliang Xu. TDAN: Temporally-deformable alignment network for video super-resolution. InProceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pages 3360–3369, 2020. 2

    2020

  21. [29]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszko- reit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. InAdvances in Neural Information Processing Systems, page 6000–6010, 2017. 3

    2017

  22. [30]

    Bring- ing old films back to life

    Ziyu Wan, Bo Zhang, Dongdong Chen, and Jing Liao. Bring- ing old films back to life. InProceedings of the IEEE/CVF 7 Conference on Computer Vision and Pattern Recognition, pages 17694–17703, 2022. 2

    2022

  23. [31]

    EDVR: Video restoration with en- hanced deformable convolutional networks

    Xintao Wang, Kelvin CK Chan, Ke Yu, Chao Dong, and Chen Change Loy. EDVR: Video restoration with en- hanced deformable convolutional networks. InProceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition Workshops, pages 1954–1963, 2019. 2, 4

    1954

  24. [32]

    MambaLLIE: Implicit retinex-aware low light enhancement with global-then-local state space

    Jiangwei Weng, Zhiqiang Yan, Ying Tai, Jianjun Qian, Jian Yang, and Jun Li. MambaLLIE: Implicit retinex-aware low light enhancement with global-then-local state space. In Advances in Neural Information Processing Systems, pages 27440–27462, 2025. 2, 3

    2025

  25. [33]

    RainMamba: Enhanced locality learning with state space models for video deraining

    Hongtao Wu, Yijun Yang, Huihui Xu, Weiming Wang, Jinni Zhou, and Lei Zhu. RainMamba: Enhanced locality learning with state space models for video deraining. InProceedings of the ACM International Conference on Multimedia, pages 7881–7890, 2024. 3

    2024

  26. [34]

    Enhancing video super-resolution via im- plicit resampling-based alignment

    Kai Xu, Ziwei Yu, Xin Wang, Michael Bi Mi, and Angela Yao. Enhancing video super-resolution via im- plicit resampling-based alignment. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2546–2555, 2024. 4

    2024

  27. [35]

    Videogigagan: Towards detail-rich video super-resolution

    Yiran Xu, Taesung Park, Richard Zhang, Yang Zhou, Eli Shechtman, Feng Liu, Jia-Bin Huang, and Difan Liu. Videogigagan: Towards detail-rich video super-resolution. InProceedings of the Computer Vision and Pattern Recog- nition Conference, pages 2139–2149, 2025. 4

    2025

  28. [36]

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

    Tianfan Xue, Baian Chen, Jiajun Wu, Donglai Wei, and William T Freeman. Video enhancement with task-oriented flow.International Journal of Computer Vision, 127:1106– 1125, 2019. 3, 4

    2019

  29. [37]

    Learning texture transformer network for image super-resolution

    Fuzhi Yang, Huan Yang, Jianlong Fu, Hongtao Lu, and Bain- ing Guo. Learning texture transformer network for image super-resolution. InProceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pages 5791–5800, 2020. 3

    2020

  30. [38]

    Progressive fusion video super-resolution net- work via exploiting non-local spatio-temporal correlations

    Peng Yi, Zhongyuan Wang, Kui Jiang, Junjun Jiang, and Jiayi Ma. Progressive fusion video super-resolution net- work via exploiting non-local spatio-temporal correlations. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 3106–3115, 2019. 2, 4

    2019

  31. [39]

    VFIMamba: Video frame inter- polation with state space models

    Guozhen Zhang, Chuxnu Liu, Yutao Cui, Xiaotong Zhao, Kai Ma, and Limin Wang. VFIMamba: Video frame inter- polation with state space models. InAdvances in Neural In- formation Processing Systems, pages 107225–107248, 2024. 3

    2024

  32. [40]

    A super-resolution reconstruction algorithm for surveillance images.Signal Processing, 90(3):848–859,

    Liangpei Zhang, Hongyan Zhang, Huanfeng Shen, and Pingxiang Li. A super-resolution reconstruction algorithm for surveillance images.Signal Processing, 90(3):848–859,

  33. [41]

    Video super-resolution trans- former with masked inter&intra-frame attention

    Xingyu Zhou, Leheng Zhang, Xiaorui Zhao, Keze Wang, Leida Li, and Shuhang Gu. Video super-resolution trans- former with masked inter&intra-frame attention. InProceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 25399–25408, 2024. 2, 4 8

    2024

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.