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Deformable Mamba for Wide Field of View Segmentation

T0 review · 1 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper argues that distortion awareness for wide-field-of-view segmentation can be supplied entirely by the decoder, without modifying the backbone, and validates this with a decoder that works across CNN, Transformer, and Mamba…

desk verdict A useful plug-in decoder with a clean ablation story, held back by a misleading efficiency headline and uncontrolled re-implemented baselines. read the letter →

arxiv 2411.16481 v2 pith:2VUS5HSU submitted 2024-11-25 cs.CV

Jie Hu , Junwei Zheng , Jiale Wei , Jiaming Zhang , Rainer Stiefelhagen This is my paper · ORCID
classification cs.CV
keywords semanticsegmentationwidefieldofviewpanoramicimagesfisheyeMambadecoderdeformableconvolutionstatespacemodelsefficient
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 argues that distortion awareness for wide-field-of-view segmentation can be supplied entirely by the decoder, without modifying the backbone. It proposes the Deformable Mamba Decoder, whose fusion stages pair a single quadri-directional Mamba scan with a deformable-convolution branch, and plugs this head into CNN, Transformer, and Mamba backbones. Across five fisheye and panoramic benchmarks the decoder reports consistent mIoU gains, including +2.5 points on Stanford2D3D, while cutting decoder parameters by 72 percent and FLOPs by 97 percent relative to widely used heads such as UperHead. A sympathetic reader would care because existing wide-FoV methods usually couple distortion handling to a specific architecture, forcing retraining; a decoder-only fix would let pretrained backbones be reused across sensor types.

What carries the argument

The Deformable Mamba Fusion (DMF) block: a fusion-and-upsampling module that takes an encoder feature $E_i$ and a previously fused decoder feature $D_j$, applies one quadri-directional selective scan (SS2D) to $D_j$ and one DCNv2 deformable convolution to $E_i$, concatenates the two branches, fuses them with 3×3 convolutions, and upsamples with PixelShuffle. It is the mechanism that adds data-dependent offsets—the distortion-awareness—without touching the encoder, which is why the same decoder can be attached to CNN, Transformer, and Mamba backbones.

What would settle it

Run a head-swap experiment on Stanford2D3D where UperHead, MusterHead, CGRHead, and the proposed decoder share the exact same augmentation, data split, effective iterations, and seeds; if the proposed decoder does not beat the best baseline by a margin consistent with the paper's +2.5 points, the claim that the decoder alone imparts distortion awareness is not supported.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is that a Mamba-based decoder can be made distortion-aware by combining the quadri-directional 2D selective scan from VMamba with a parallel DCNv2 deformable-convolution branch in each Deformable Mamba Fusion block. This gives the decoder adaptive sampling offsets that compensate for fisheye and equirectangular deformation while retaining Mamba's linear complexity. With the same VMamba-T backbone, the proposed decoder raises mIoU on Stanford2D3D from 56.8 for the VMamba-plus-UperHead baseline to 59.3, improves results on Matterport3D, SynPASS, WoodScape, and SynWoodScape, and reduces decoder parameters by 72 percent and FLOPs by 97 percent compared with UperHead.

Load-bearing premise

The reported gains assume the re-implemented baselines were trained under the same data splits, preprocessing, augmentation, and optimization settings as the proposed model, so that swapping the decoder head is the only meaningful change.

Editorial extensions

If this is right

  • A single decoder design can replace task-specific heads across CNN, Transformer, and Mamba backbones, so wide-FoV models no longer need distortion handling baked into the encoder.
  • The decoder's compute is small enough that the head stops being the FLOP bottleneck: on VMamba-T, UperHead uses 206.9 GFLOPs while the proposed head uses 6.0 GFLOPs, making the total model much cheaper.
  • Consistent gains on synthetic and real, indoor and outdoor, 180-degree and 360-degree datasets indicate the approach generalizes across distortion types rather than overfitting one sensor.
  • Quadri-directional scanning contributes more than uni- or bi-directional scanning in the decoder, supporting the claim that scan diversity matters for spatially structured wide-FoV features.
  • Replacing deformable convolution with ordinary convolution drops mIoU by 1.85 points, so the deformable branch is the component responsible for the distortion-aware gain.

Reading between the lines

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

  • Editorial inference: because the decoder is the only component that changes, the same DMF block could plausibly transfer distortion-awareness to other dense prediction tasks on wide-FoV inputs, such as depth estimation, panoptic segmentation, or open-vocabulary segmentation.
  • Editorial inference: the small margin on Matterport3D (+0.3 to +0.9 mIoU) suggests the benefit of decoder-level deformation is larger on datasets with heavy distortion or fine elongated classes; a controlled head-swap study would reveal which property drives the gain.
  • Editorial inference: a testable extension is to visualize the learned DCN offsets on equirectangular images; if the offsets systematically align with the spherical-to-planar projection, that would confirm the mechanism is distortion compensation rather than generic capacity.
  • Editorial inference: since the paper trains all models with one schedule and no auxiliary losses, part of the gain could come from regularization; comparing against a matched-capacity conventional decoder with identical training would isolate the deformable mechanism.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

1 major / 6 minor

Summary. The paper proposes the Deformable Mamba Decoder, a decoder that combines a quadri-directional Mamba scan branch with a deformable convolution (DCNv2) branch and PixelShuffle upsampling, and demonstrates it as a plug-in decoder for CNN-, Transformer-, and Mamba-based backbones in wide field-of-view (180° fisheye and 360° panoramic) semantic segmentation. The method is evaluated on five datasets (Stanford2D3D, Matterport3D, SynPASS, WoodScape, SynWoodScape) and is reported to improve mIoU over prior decoders while reducing parameters and FLOPs, with a headline +2.5 mIoU gain on Stanford2D3D over a VMamba-based baseline. The paper includes ablations of scan direction, deformable convolution, and upsampling method, and releases code.

Significance. If the empirical claims are sustained, the paper makes a useful contribution: it identifies decoder-level distortion awareness as a modular component that can be attached to diverse backbones, and it provides one of the first decoder-focused uses of Mamba for dense prediction. The experimental scope is a strength: five datasets, three backbone families, and ablations that isolate DCN, scan direction, and upsampling. The availability of code is also a concrete asset. However, the main quantitative claims currently rest on comparisons whose fairness and precise accounting need to be verified before the stated advantages can be accepted.

major comments (1)
  1. [§4.2 and Tables 4-7] The performance gains over re-implemented baselines (VMamba†, SegNeXt†, 360SFUDA†) are load-bearing for the +2.5 mIoU claim, but the manuscript does not establish that these re-implementations were trained under identical conditions. Section 4.2 specifies the optimizer, schedule, and iterations for the proposed method but does not state whether the re-implemented baselines used the same augmentation, cropping/resolution policy, preprocessing, or loss weighting. Since the cleanest comparison (same VMamba-T backbone, only the decoder differs) is exactly the case where training details matter most, the paper should report the full training recipe for the re-implemented baselines and ideally release their configuration files, or compare against published numbers from the original papers.
minor comments (6)
  1. [Abstract] The opening sentence is grammatically incomplete: 'Recent advancements in the Mamba architecture, with its linear computational complexity, being a promising alternative...' should be revised.
  2. [§4.3] There is a typo: 'Deformable Mamaba Decoder' should be 'Deformable Mamba Decoder'.
  3. [§4.4] 'desin' in 'lacking deformable desin' should be 'design'.
  4. [§3.2, Eq. (3)] The PixelShuffle formula's subscript notation is unclear: 'PS(T)h,w,2c = T2h,2w,c/2' mixes input and output dimensions in a way that is hard to parse. Please rewrite the index notation to show the rearrangement explicitly.
  5. [§4.2 and Tables 1-3] It should be stated explicitly whether the reported parameter and FLOPs counts refer to the decoder alone or to the full encoder-decoder model; Tables 1-3 list decoder-level numbers while §4.5 discusses overall reductions.
  6. [Limitations and Future Work] The limitations paragraph does not actually state a limitation of the current method; it only points to future LLM integration. Please add a genuine discussion of limitations, for example the sensitivity of the reported gains to re-implementation details and the mixed efficiency baselines.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported decoder gains are empirical results measured against external benchmarks and existing baselines, not predictions derived from fitted constants or self-citation chains.

full rationale

The paper's central claim is an experimental one: replacing standard decoder heads with the proposed Deformable Mamba Decoder improves wide-FoV semantic segmentation while reducing parameters and FLOPs. The decoder is a concrete architecture (quadri-directional Mamba scan branch plus DCNv2 branch, fused and upsampled with PixelShuffle), and its performance is assessed on five external datasets against published and re-implemented baselines. No quantity is fitted to a subset of data and then renamed as a prediction; the mIoU numbers are measured on held-out evaluation splits. The ablation in Table 9 isolates the distortion-aware component by replacing deformable convolution with a regular convolution, and the scanning and upsampling ablations similarly vary one architectural choice at a time, giving the central attribution independent empirical content. The paper cites prior work by the same group (e.g., Trans4PASS, 360BEV) but uses those results as comparison baselines rather than as load-bearing justification for the proposed design; no uniqueness theorem or prior self-citation is invoked to rule out alternatives. The main substantive concerns in the manuscript are about experimental comparability: several baselines are marked as re-implementations (VMamba†, SegNeXt†, 360SFUDA†) without full sharing of augmentation and preprocessing configurations, and the headline efficiency figures compare different baselines across tables (72% parameter reduction versus CGRHead, 97% FLOPs reduction versus UperHead). These are legitimate threats to the strength of the empirical claim, but they are not circularity: they concern whether the comparison is fair and whether the reported margins would survive a strictly controlled protocol, not whether the conclusion is equivalent to its inputs by construction. The paper is self-contained against external benchmarks, and no derivation chain reduces to its own assumptions.

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

The central claim depends on the SS2D module, DCNv2, the fairness of re-implemented baselines, and the benchmark protocol, all of which are assumed from prior work or the paper's own setup rather than derived within the paper.

assumptions (4)
  • domain assumption The SS2D quadri-directional scan block from VMamba provides effective 2D state-space modeling with linear complexity in the decoder.
    The decoder reuses the SS2D module from [34] without re-deriving its properties; the paper's efficiency and feature-fusion claims depend on this module behaving as in the encoder context.
  • domain assumption DCNv2's learned offsets can represent and compensate for the geometric distortions present in 180° and 360° imagery.
    Section 3.2 replaces rigid convolution in the decoder with DCNv2 and attributes the resulting mIoU gain to distortion awareness; the paper does not characterize what distortion classes the offsets can express.
  • domain assumption Public dataset splits and mIoU evaluation are faithful indicators of segmentation quality.
    All comparisons are on mIoU computed with an 80/20 re-split for WoodScape and SynWoodScape, following prior practice; the method assumes these metrics and splits are meaningful.
  • domain assumption The re-implemented baselines are trained fairly under comparable settings.
    Tables 4-7 rely on re-implementations of SegNeXt, 360SFUDA, and VMamba; the paper does not provide full training details for those baselines, so their validity is assumed.

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Cite this review

Pith. "Pith review of Deformable Mamba for Wide Field of View Segmentation." pith.science (2026). https://pith.science/paper/2VUS5HSU

@misc{pith2026241116481,
  author       = {Pith},
  title        = {Pith review of: Deformable Mamba for Wide Field of View Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2VUS5HSU}},
  note         = {Machine review of arXiv:2411.16481}
}
read the original abstract

Recent advancements in the Mamba architecture, with its linear computational complexity, being a promising alternative to transformer architectures suffering from quadratic complexity. While existing works primarily focus on adapting Mamba as vision encoders, the critical role of task-specific Mamba decoders remains under-explored, particularly for distortion-prone dense prediction tasks. This paper addresses two interconnected challenges: (1) The design of a Mamba-based decoder that seamlessly adapts to various architectures (e.g., CNN-, Transformer-, and Mamba-based backbones), and (2) The performance degradation in decoders lacking distortion-aware capability when processing wide-FoV images (e.g., 180{\deg} fisheye and 360{\deg} panoramic settings). We propose the Deformable Mamba Decoder, an efficient distortion-aware decoder that integrates Mamba's computational efficiency with adaptive distortion awareness. Comprehensive experiments on five wide-FoV segmentation benchmarks validate its effectiveness. Notably, our decoder achieves a +2.5% performance improvement on the 360{\deg} Stanford2D3D segmentation benchmark while reducing 72% parameters and 97% FLOPs, as compared to the widely-used decoder heads.

Figures

Figures reproduced from arXiv: 2411.16481 by the authors.

Figure 1
Figure 1. Our deformable Mamba (a) achieves better results across [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Comparison of camera imaging with narrow-FoV (left) [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Overview of the Deformable Mamba (DMamba) framework. Given wide-FoV images (180° or 360°), the features extracted by an [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: For an embedded 2D sequence, Mamba [17] employs a uni-directional scan from start to end (1). [72] utilizes a bi￾directional scan combining (1) and (2), while [34] adopts a quadri￾directional scanning incorporating (1), (2), (3), and (4). feature maps output by the enc…
Figure 6
Figure 6. Figure 6: Visualization of feature maps. From left to right: fea￾tures extracted by Mamba-based encoder (Before DMF) and fea￾tures fused by the DMF module (After DMF). Effective detection and recognition of small objects in indoor scenarios (a), such as pictures on walls, requir…
Figure 5
Figure 5. Figure 5: Visualization of the wide-FoV segmentation results. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

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Forward citations

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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Reference graph

Works this paper leans on

76 extracted references · 44 canonical work pages · cited by 2 Pith papers

  1. [1]

    Joint 2d-3d semantic data for indoor scene un- derstanding

    I Armeni. Joint 2d-3d semantic data for indoor scene un- derstanding. arXiv preprint arXiv:1702.01105, 2017. 1, 2, 5

    arXiv 2017

  2. [2]

    Segnet: A deep convolutional encoder-decoder architecture for image segmentation

    Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern anal- ysis and machine intelligence , 39(12):2481–2495, 2017. 1, 2

    work page 2017

  3. [3]

    Matterport3d: Learning from rgb-d data in indoor environments

    Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niessner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang. Matterport3d: Learning from rgb-d data in indoor environments. arXiv preprint arXiv:1709.06158, 2017. 1, 2, 5

    arXiv 2017

  4. [4]

    Rsmamba: Remote sens- ing image classification with state space model

    Keyan Chen, Bowen Chen, Chenyang Liu, Wenyuan Li, Zhengxia Zou, and Zhenwei Shi. Rsmamba: Remote sens- ing image classification with state space model. IEEE Geo- science and Remote Sensing Letters, 2024. 2, 3, 4

    work page 2024

  5. [5]

    Encoder-decoder with atrous separable convolution for semantic image segmentation

    Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European conference on computer vision (ECCV), pages 801–818, 2018. 1, 2

    work page 2018

  6. [6]

    Cyclemlp: A mlp-like architecture for dense prediction

    Shoufa Chen, Enze Xie, Chongjian Ge, Runjian Chen, Ding Liang, and Ping Luo. Cyclemlp: A mlp-like architecture for dense prediction. arXiv preprint arXiv:2107.10224, 2021. 2

    arXiv 2021

  7. [7]

    Cyclemlp: A mlp-like architecture for dense visual predictions

    Shoufa Chen, Enze Xie, Chongjian Ge, Runjian Chen, Ding Liang, and Ping Luo. Cyclemlp: A mlp-like architecture for dense visual predictions. IEEE Transactions on Pattern Analysis and Machine Intelligence , 45(12):14284–14300,

  8. [8]

    Semi-supervised semantic segmentation with cross pseudo supervision

    Xiaokang Chen, Yuhui Yuan, Gang Zeng, and Jingdong Wang. Semi-supervised semantic segmentation with cross pseudo supervision. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , pages 2613–2622, 2021. 7

    work page 2021

Show all 76 references
  1. [9]

    Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks

    Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and An- drew Rabinovich. Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks. In In- ternational conference on machine learning, pages 794–803. PMLR, 2018. 7

    2018

  2. [10]

    Per- pixel classification is not all you need for semantic segmen- tation

    Bowen Cheng, Alex Schwing, and Alexander Kirillov. Per- pixel classification is not all you need for semantic segmen- tation. Advances in neural information processing systems , 34:17864–17875, 2021. 2

    2021

  3. [11]

    Deformable convolutional networks

    Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei. Deformable convolutional networks. In Proceedings of the IEEE international confer- ence on computer vision, pages 764–773, 2017. 4

    2017

  4. [12]

    Restricted deformable convolution-based road scene semantic segmentation using surround view cam- eras

    Liuyuan Deng, Ming Yang, Hao Li, Tianyi Li, Bing Hu, and Chunxiang Wang. Restricted deformable convolution-based road scene semantic segmentation using surround view cam- eras. IEEE Transactions on Intelligent Transportation Sys- tems, 21(10):4350–4362, 2019. 3

    2019

  5. [13]

    An image is worth 16x16 words: Transformers for image recognition at scale

    Alexey Dosovitskiy. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 3

    2010 arXiv

  6. [14]

    Carla: An open urban driv- ing simulator

    Alexey Dosovitskiy, German Ros, Felipe Codevilla, Anto- nio Lopez, and Vladlen Koltun. Carla: An open urban driv- ing simulator. In Conference on robot learning, pages 1–16. PMLR, 2017. 5

    2017

  7. [15]

    Tangent images for mitigating spherical distortion

    Marc Eder, Mykhailo Shvets, John Lim, and Jan-Michael Frahm. Tangent images for mitigating spherical distortion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12426–12434, 2020. 6

    2020

  8. [16]

    Dual attention network for scene seg- mentation

    Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. Dual attention network for scene seg- mentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3146–3154,

  9. [17]

    Mamba: Linear-time sequence modeling with selective state spaces

    Albert Gu and Tri Dao. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752, 2023. 2, 3, 4

    2023 arXiv

  10. [18]

    Multi-scale high-resolution vision transformer for se- mantic segmentation

    Jiaqi Gu, Hyoukjun Kwon, Dilin Wang, Wei Ye, Meng Li, Yu-Hsin Chen, Liangzhen Lai, Vikas Chandra, and David Z Pan. Multi-scale high-resolution vision transformer for se- mantic segmentation. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition...

    2022

  11. [19]

    Dynamic task prioritization for multitask learning

    Michelle Guo, Albert Haque, De-An Huang, Serena Yeung, and Li Fei-Fei. Dynamic task prioritization for multitask learning. In Proceedings of the European conference on com- puter vision (ECCV), pages 270–287, 2018. 7

    2018

  12. [20]

    Segnext: Rethink- ing convolutional attention design for semantic segmenta- tion

    Meng-Hao Guo, Cheng-Ze Lu, Qibin Hou, Zhengning Liu, Ming-Ming Cheng, and Shi-Min Hu. Segnext: Rethink- ing convolutional attention design for semantic segmenta- tion. Advances in Neural Information Processing Systems , 35:1140–1156, 2022. 1, 2, 6, 7

    2022

  13. [21]

    Single frame se- mantic segmentation using multi-modal spherical images

    Suresh Guttikonda and Jason Rambach. Single frame se- mantic segmentation using multi-modal spherical images. In Proceedings of the IEEE/CVF Winter Conference on Appli- cations of Computer Vision, pages 3222–3231, 2024. 5

    2024

  14. [22]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceed- ings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016. 5

    2016

  15. [23]

    Zigma: Zigzag mamba diffusion model

    Vincent Tao Hu, Stefan Andreas Baumann, Ming Gui, Olga Grebenkova, Pingchuan Ma, Johannes Fischer, and Bjorn Ommer. Zigma: Zigzag mamba diffusion model. arXiv preprint arXiv:2403.13802, 2024. 3, 4

    2024 arXiv

  16. [24]

    Localmamba: Visual state space model with windowed selective scan

    Tao Huang, Xiaohuan Pei, Shan You, Fei Wang, Chen Qian, and Chang Xu. Localmamba: Visual state space model with windowed selective scan. arXiv preprint arXiv:2403.09338,

    arXiv

  17. [25]

    Ccnet: Criss-cross attention for semantic segmentation

    Zilong Huang, Xinggang Wang, Lichao Huang, Chang Huang, Yunchao Wei, and Wenyu Liu. Ccnet: Criss-cross attention for semantic segmentation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 603–612, 2019. 2

    2019

  18. [26]

    Panoramic panoptic segmentation: Towards complete sur- rounding understanding via unsupervised contrastive learn- ing

    Alexander Jaus, Kailun Yang, and Rainer Stiefelhagen. Panoramic panoptic segmentation: Towards complete sur- rounding understanding via unsupervised contrastive learn- ing. In 2021 IEEE Intelligent Vehicles Symposium (IV) , pages 1421–1427. IEEE, 2021. 2

    2021

  19. [27]

    Multi-task learning using uncertainty to weigh losses for scene geome- try and semantics

    Alex Kendall, Yarin Gal, and Roberto Cipolla. Multi-task learning using uncertainty to weigh losses for scene geome- try and semantics. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7482–7491,

  20. [28]

    Imagenet classification with deep convolutional neural net- works

    Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural net- works. Advances in neural information processing systems , 25, 2012. 3

    2012

  21. [29]

    Omnidet: Surround view cameras based multi-task visual perception network for autonomous driv- ing

    Varun Ravi Kumar, Senthil Yogamani, Hazem Rashed, Ganesh Sitsu, Christian Witt, Isabelle Leang, Stefan Milz, and Patrick M¨ader. Omnidet: Surround view cameras based multi-task visual perception network for autonomous driv- ing. IEEE Robotics and Automation Letters , 6(2):2830...

    2021

  22. [30]

    Videomamba: State space model for efficient video understanding

    Kunchang Li, Xinhao Li, Yi Wang, Yinan He, Yali Wang, Limin Wang, and Yu Qiao. Videomamba: State space model for efficient video understanding. arXiv preprint arXiv:2403.06977, 2024. 2, 3, 4

    2024 arXiv

  23. [31]

    Sgat4pass: spherical geometry-aware trans- former for panoramic semantic segmentation

    Xuewei Li, Tao Wu, Zhongang Qi, Gaoang Wang, Ying Shan, and Xi Li. Sgat4pass: spherical geometry-aware trans- former for panoramic semantic segmentation. arXiv preprint arXiv:2306.03403, 2023. 6

    2023 arXiv

  24. [32]

    Ct- net: Context-based tandem network for semantic segmenta- tion

    Zechao Li, Yanpeng Sun, Liyan Zhang, and Jinhui Tang. Ct- net: Context-based tandem network for semantic segmenta- tion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12):9904–9917, 2021. 2

    2021

  25. [33]

    Covariance attention for semantic segmentation

    Yazhou Liu, Yuliang Chen, Pongsak Lasang, and Qunsen Sun. Covariance attention for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4):1805–1818, 2020. 2

    2020

  26. [34]

    Vmamba: Visual state space model

    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

  27. [35]

    Swin transformer: Hierarchical vision transformer using shifted windows

    Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012–10022, 2021. 5

    2021

  28. [36]

    Fully convolutional networks for semantic segmentation

    Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. In Pro- ceedings of the IEEE conference on computer vision and pat- tern recognition, pages 3431–3440, 2015. 2

    2015

  29. [37]

    De- formable convolution based road scene semantic segmenta- tion of fisheye images in autonomous driving

    Anam Manzoor, Aryan Singh, Ganesh Sistu, Reenu Mohan- das, Eoin Grua, Anthony Scanlan, and Ciar ´an Eising. De- formable convolution based road scene semantic segmenta- tion of fisheye images in autonomous driving. In IET Con- ference Proceedings CP887, pages 7–14. IET, 2024. 3

    2024

  30. [38]

    Context-guided spatial feature recon- struction for efficient semantic segmentation

    Zhenliang Ni, Xinghao Chen, Yingjie Zhai, Yehui Tang, and Yunhe Wang. Context-guided spatial feature recon- struction for efficient semantic segmentation. arXiv preprint arXiv:2405.06228, 2024. 2, 5

    2024 arXiv

  31. [39]

    Seman- tic segmentation using transfer learning on fisheye images

    Sneha Paul, Zachary Patterson, and Nizar Bouguila. Seman- tic segmentation using transfer learning on fisheye images. In 2023 International Conference on Machine Learning and Applications (ICMLA), pages 445–452. IEEE, 2023. 3

    2023

  32. [40]

    Fish- segssl: A semi-supervised semantic segmentation frame- work for fish-eye images

    Sneha Paul, Zachary Patterson, and Nizar Bouguila. Fish- segssl: A semi-supervised semantic segmentation frame- work for fish-eye images. Journal of Imaging , 10(3):71,

  33. [41]

    Efficientvmamba: Atrous selective scan for light weight visual mamba

    Xiaohuan Pei, Tao Huang, and Chang Xu. Efficientvmamba: Atrous selective scan for light weight visual mamba. arXiv preprint arXiv:2403.09977, 2024. 3, 4

    2024 arXiv

  34. [42]

    Adaptable deformable convolutions for semantic segmentation of fisheye images in autonomous driving sys- tems

    Cl ´ement Playout, Ola Ahmad, Freddy Lecue, and Farida Cheriet. Adaptable deformable convolutions for semantic segmentation of fisheye images in autonomous driving sys- tems. arXiv preprint arXiv:2102.10191, 2021. 3

    2021 arXiv

  35. [43]

    Fusionnet: A deep fully residual convo- lutional neural network for image segmentation in connec- tomics

    Tran Minh Quan, David Grant Colburn Hildebrand, and Won-Ki Jeong. Fusionnet: A deep fully residual convo- lutional neural network for image segmentation in connec- tomics. Frontiers in Computer Science, 3:613981, 2021. 3

    2021

  36. [44]

    U- net: Convolutional networks for biomedical image segmen- tation

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U- net: Convolutional networks for biomedical image segmen- tation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, pa...

    2015

  37. [45]

    Synwoodscape: Synthetic surround-view fisheye camera dataset for autonomous driving

    Ahmed Rida Sekkat, Yohan Dupuis, Varun Ravi Kumar, Hazem Rashed, Senthil Yogamani, Pascal Vasseur, and Paul Honeine. Synwoodscape: Synthetic surround-view fisheye camera dataset for autonomous driving. IEEE Robotics and Automation Letters, 7(3):8502–8509, 2022. 1, 2, 5

    2022

  38. [46]

    Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

    Wenzhe Shi, Jose Caballero, Ferenc Husz ´ar, Johannes Totz, Andrew P Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In Proceedings of the IEEE conference on compu...

    2016

  39. [47]

    Segmenter: Transformer for semantic segmenta- tion

    Robin Strudel, Ricardo Garcia, Ivan Laptev, and Cordelia Schmid. Segmenter: Transformer for semantic segmenta- tion. In Proceedings of the IEEE/CVF international confer- ence on computer vision, pages 7262–7272, 2021. 1, 2

    2021

  40. [48]

    Hohonet: 360 indoor holistic understanding with latent horizontal fea- tures

    Cheng Sun, Min Sun, and Hwann-Tzong Chen. Hohonet: 360 indoor holistic understanding with latent horizontal fea- tures. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition , pages 2573–2582,

  41. [49]

    Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

    Antti Tarvainen and Harri Valpola. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems, 30, 2017. 1, 7

    2017

  42. [50]

    360bev: Panoramic semantic mapping for indoor bird’s-eye view

    Zhifeng Teng, Jiaming Zhang, Kailun Yang, Kunyu Peng, Hao Shi, Simon Reiß, Ke Cao, and Rainer Stiefelhagen. 360bev: Panoramic semantic mapping for indoor bird’s-eye view. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages 373–382, 2024. 5, 6

    2024

  43. [51]

    Attention is all you need

    A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems, 2017. 3

    2017

  44. [52]

    Pvt v2: Improved baselines with pyramid vision transformer

    Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao. Pvt v2: Improved baselines with pyramid vision transformer. Computational Visual Media, 8(3):415–424, 2022. 7

    2022

  45. [53]

    Internimage: Exploring large-scale vi- sion foundation models with deformable convolutions

    Wenhai Wang, Jifeng Dai, Zhe Chen, Zhenhang Huang, Zhiqi Li, Xizhou Zhu, Xiaowei Hu, Tong Lu, Lewei Lu, Hongsheng Li, et al. Internimage: Exploring large-scale vi- sion foundation models with deformable convolutions. In Proceedings of the IEEE/CVF conference on computer vi- si...

    2023

  46. [54]

    Unified perceptual parsing for scene understand- ing

    Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, and Jian Sun. Unified perceptual parsing for scene understand- ing. In Proceedings of the European conference on computer vision (ECCV), pages 418–434, 2018. 2, 5, 6, 7

    2018

  47. [55]

    Segformer: Simple and efficient design for semantic segmentation with transform- ers

    Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transform- ers. Advances in neural information processing systems, 34: 12077–12090, 2021. 1, 2, 6, 7

    2021

  48. [56]

    Efficient deformable convnets: Rethinking dynamic and sparse operator for vision applications

    Yuwen Xiong, Zhiqi Li, Yuntao Chen, Feng Wang, Xizhou Zhu, Jiapeng Luo, Wenhai Wang, Tong Lu, Hongsheng Li, Yu Qiao, et al. Efficient deformable convnets: Rethinking dynamic and sparse operator for vision applications. In Pro- ceedings of the IEEE/CVF Conference on Computer Vi...

    2024

  49. [57]

    Uperformer: A multi-scale transformer- based decoder for semantic segmentation

    Jing Xu, Wentao Shi, Pan Gao, Zhengwei Wang, and Qizhu Li. Uperformer: A multi-scale transformer- based decoder for semantic segmentation. arXiv preprint arXiv:2211.13928, 2022. 2, 5

    2022 arXiv

  50. [58]

    Plainmamba: Improving non-hierarchical mamba in visual recognition

    Chenhongyi Yang, Zehui Chen, Miguel Espinosa, Linus Er- icsson, Zhenyu Wang, Jiaming Liu, and Elliot J Crowley. Plainmamba: Improving non-hierarchical mamba in visual recognition. arXiv preprint arXiv:2403.17695, 2024. 2

    2024 arXiv

  51. [59]

    Can we pass beyond the field of view? panoramic annular semantic segmentation for real-world surrounding perception

    Kailun Yang, Xinxin Hu, Luis M Bergasa, Eduardo Romera, Xiao Huang, Dongming Sun, and Kaiwei Wang. Can we pass beyond the field of view? panoramic annular semantic segmentation for real-world surrounding perception. In2019 IEEE Intelligent Vehicles Symposium (IV) , pages 446–4...

    2019

  52. [60]

    Pass: Panoramic annular semantic seg- mentation

    Kailun Yang, Xinxin Hu, Luis M Bergasa, Eduardo Romera, and Kaiwei Wang. Pass: Panoramic annular semantic seg- mentation. IEEE Transactions on Intelligent Transportation Systems, 21(10):4171–4185, 2019. 2

    2019

  53. [61]

    Vivim: a video vision mamba for medical video object segmentation

    Yijun Yang, Zhaohu Xing, and Lei Zhu. Vivim: a video vision mamba for medical video object segmentation. arXiv preprint arXiv:2401.14168, 2024. 3, 4

    2024 arXiv

  54. [62]

    Woodscape: A multi-task, multi-camera fisheye dataset for autonomous driving

    Senthil Yogamani, Ciar ´an Hughes, Jonathan Horgan, Ganesh Sistu, Padraig Varley, Derek O’Dea, Michal Uric ´ar, Ste- fan Milz, Martin Simon, Karl Amende, et al. Woodscape: A multi-task, multi-camera fisheye dataset for autonomous driving. In Proceedings of the IEEE/CVF Interna...

    2019

  55. [63]

    Ocnet: Object context for seman- tic segmentation

    Yuhui Yuan, Lang Huang, Jianyuan Guo, Chao Zhang, Xilin Chen, and Jingdong Wang. Ocnet: Object context for seman- tic segmentation. International Journal of Computer Vision, 129(8):2375–2398, 2021. 2

    2021

  56. [64]

    Bending reality: Distortion-aware transformers for adapting to panoramic se- mantic segmentation

    Jiaming Zhang, Kailun Yang, Chaoxiang Ma, Simon Reiß, Kunyu Peng, and Rainer Stiefelhagen. Bending reality: Distortion-aware transformers for adapting to panoramic se- mantic segmentation. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition ,...

    2022

  57. [65]

    Behind every domain there is a shift: Adapting distortion-aware vision transformers for panoramic semantic segmentation

    Jiaming Zhang, Kailun Yang, Hao Shi, Simon Reiß, Kunyu Peng, Chaoxiang Ma, Haodong Fu, Philip HS Torr, Kaiwei Wang, and Rainer Stiefelhagen. Behind every domain there is a shift: Adapting distortion-aware vision transformers for panoramic semantic segmentation. IEEE Transactio...

    2024

  58. [66]

    Motion mamba: Efficient and long sequence motion generation

    Zeyu Zhang, Akide Liu, Ian Reid, Richard Hartley, Bohan Zhuang, and Hao Tang. Motion mamba: Efficient and long sequence motion generation. In European Conference on Computer Vision, pages 265–282. Springer, 2025. 3, 4

    2025

  59. [67]

    Materobot: Material recognition in wearable robotics for people with visual impairments

    Junwei Zheng, Jiaming Zhang, Kailun Yang, Kunyu Peng, and Rainer Stiefelhagen. Materobot: Material recognition in wearable robotics for people with visual impairments. In 2024 IEEE International Conference on Robotics and Au- tomation (ICRA), pages 2303–2309. IEEE, 2024. 1, 2

    2024

  60. [68]

    Open panoramic segmentation

    Junwei Zheng, Ruiping Liu, Yufan Chen, Kunyu Peng, Chengzhi Wu, Kailun Yang, Jiaming Zhang, and Rainer Stiefelhagen. Open panoramic segmentation. In European Conference on Computer Vision , pages 164–182. Springer,

  61. [69]

    Rethinking semantic segmen- tation from a sequence-to-sequence perspective with trans- formers

    Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip HS Torr, et al. Rethinking semantic segmen- tation from a sequence-to-sequence perspective with trans- formers. In Proceedings of the IEEE/CVF conference...

  62. [70]

    Semantics distortion and style matter: Towards source-free uda for panoramic segmentation

    Xu Zheng, Pengyuan Zhou, Athanasios V Vasilakos, and Lin Wang. Semantics distortion and style matter: Towards source-free uda for panoramic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 27885–27895, 2024. 2, 6, 7

    2024

  63. [71]

    Complementary bi-directional fea- ture compression for indoor 360deg semantic segmentation with self-distillation

    Zishuo Zheng, Chunyu Lin, Lang Nie, Kang Liao, Zhijie Shen, and Yao Zhao. Complementary bi-directional fea- ture compression for indoor 360deg semantic segmentation with self-distillation. In Proceedings of the IEEE/CVF Win- ter Conference on Applications of Computer Vision , ...

    2023

  64. [72]

    Vision mamba: Efficient visual representation learning with bidirectional state space model

    Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang. Vision mamba: Efficient visual representation learning with bidirectional state space model. arXiv preprint arXiv:2401.09417, 2024. 2, 3, 4

    2024 arXiv

  65. [73]

    Samba: Semantic seg- mentation of remotely sensed images with state space model

    Qinfeng Zhu, Yuanzhi Cai, Yuan Fang, Yihan Yang, Cheng Chen, Lei Fan, and Anh Nguyen. Samba: Semantic seg- mentation of remotely sensed images with state space model. Heliyon, 10(19), 2024. 2

    2024

  66. [74]

    De- formable convnets v2: More deformable, better results

    Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai. De- formable convnets v2: More deformable, better results. In Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 9308–9316, 2019. 4

    2019

  67. [75]

    Deformable detr: Deformable trans- formers for end-to-end object detection

    Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable detr: Deformable trans- formers for end-to-end object detection. arXiv preprint arXiv:2010.04159, 2020. 4

    2010 arXiv

  68. [2024]

    1, 2, 3, 4, 5, 6, 7, 8

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.