Pith. sign in

REVIEW 5 major objections 7 minor 1 cited by

SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation

T0 review · 5 major / 7 minor · reviewed 2026-08-11 · deepseek-v4-flash

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

Pith's one-line read SegMAN claims that mixing a state-space global scan with sliding-window local attention gives state-of-the-art semantic segmentation at linear time and lower cost across ADE20K, Cityscapes, and COCO-Stuff.

desk verdict Useful hybrid architecture with honest ablations; headline claims about linear-time and SOTA need re-scoping. read the letter →

arxiv 2412.11890 v2 pith:VCALEJWR submitted 2024-12-16 cs.CV

Yunxiang Fu , Meng Lou , Yizhou Yu This is my paper · ORCID
classification cs.CV
keywords semanticsegmentationstatespacemodelsMambaneighborhoodattentionmulti-scalecontextlinear-timearchitectureADE20KCityscapes
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

This paper tries to establish that a single network can hold global context, local detail, and multi-scale representation at the same time, and that this combination is what pushes semantic segmentation accuracy forward. The proposed SegMAN fuses a two-dimensional selective-scan state space model with sliding-window neighborhood attention in a token mixer called LASS, and adds a decoder module, MMSCopE, that scans several scales in one pass. If the paper is right, high-resolution segmentation no longer needs quadratic self-attention or fixed predefined windows: SegMAN is linear-time, adapts to changing input resolutions, and reports higher mIoU than larger prior models on ADE20K, Cityscapes, and COCO-Stuff-164K.

What carries the argument

The load-bearing object is the LASS token mixer: a serial stack of Neighborhood Attention (a sliding-window attention that keeps translational equivalence and linear complexity) with the two-dimensional selective-scan block SS2D (a Mamba-style state space model that scans each feature map in four directions for global context in linear time), plus a residual connection around SS2D and a 1x1 convolution that fuses local and global streams. The second mechanism is MMSCopE, the decoder module: it creates coarser feature maps with strided convolutions, losslessly downsamples them with pixel unshuffle to a common resolution, concatenates them along channels, and extracts multi-scale context with a single SS2D scan, followed by a 1x1 convolution and upsampling. Together they let the network cover the full feature map at every resolution while preserving fine detail.

What would settle it

Re-run the main baselines (SegNeXt-L, SegFormer-B3, VWFormer-B3, EDAFormer-B) with the paper's own training settings, resolutions, and hardware, then remeasure mIoU, GFLOPs, and FPS; if SegMAN-B no longer beats them by roughly the reported margins (for example, 1.6 mIoU on ADE20K and 0.6 on Cityscapes), the central comparison claim would be falsified.

Watch

Extended reading notes

Core claim

The central claim is that omni-scale context modeling — global dependencies, local boundary detail, and multiple intermediate scales at once — can be delivered in linear time by a hybrid encoder-decoder. In the encoder, each LASS block serially stacks Neighborhood Attention (sliding-window local attention) and the VMamba SS2D block (a four-directional selective scan), with a residual shortcut around the SS2D; the last stage uses global attention. In the decoder, MMSCopE downsamples the fused feature map with strided convolutions, uses pixel unshuffle to bring the scales to one resolution without information loss, and runs a single SS2D scan over the channel-concatenated result to extract multi-scale context. The paper reports that the Base model reaches 52.6% mIoU on ADE20K, 83.8% on Cityscapes, and 48.4% on COCO-Stuff-164K, while the Base encoder reaches 85.1% ImageNet-1k top-1 accuracy.

Load-bearing premise

The comparison with prior models assumes that the published FLOPs, FPS, and mIoU numbers were measured under equivalent training recipes and hardware; if baseline numbers are not directly comparable, the reported accuracy and efficiency gains could be smaller.

Editorial extensions

If this is right

  • Quadratic global self-attention is not required for top segmentation accuracy; a four-direction state-space scan plus local attention matches or beats attention-based models at lower FLOPs.
  • Fixed-window multi-scale attention loses its advantage as input resolution grows, because a single scan over full multi-scale maps keeps complete feature-map coverage at any resolution.
  • SegMAN's encoder and decoder transfer independently: replacing a base model's encoder or decoder with either component raises mIoU while reducing parameters and FLOPs.
  • The encoder generalizes beyond semantic segmentation, improving panoptic and instance segmentation when used as a backbone in Mask DINO.

Reading between the lines

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

  • The paper's motivation implies a concrete test: the accuracy gap between SegMAN and fixed-window multi-scale models should grow with input resolution, since fixed windows lose full-map coverage.
  • The single-scan channel-concatenation trick is an efficiency choice; comparing it to independent per-scale scans at higher resolutions or on non-square inputs would separate architectural benefit from GPU convenience.
  • The local-plus-global recipe points to a general design pattern for high-resolution dense prediction, so extending the same hybrid mixer to video or 3D segmentation is a natural test of its scope.
  • Because the headline comparisons inherit published baseline numbers, the internal ablations (removing SS2D costs 3.9 mIoU; removing Neighborhood Attention costs 1.5 mIoU) are the part of the evidence that does not depend on external measurement conventions.
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

5 major / 7 minor

Summary. SegMAN proposes a semantic segmentation architecture with a hybrid encoder, SegMAN Encoder, that combines Neighborhood Attention (Natten) and a 2D selective-scan state-space model (SS2D) in its LASS token mixer, and a decoder whose MMSCopE module extracts multi-scale context using SS2D scans over concatenated feature maps at different resolutions. The paper reports ImageNet-1k classification results for the encoder and semantic segmentation results on ADE20K, Cityscapes, and COCO-Stuff-164K, claiming state-of-the-art accuracy at reduced FLOPs, with SegMAN-B reaching 52.6% mIoU on ADE20K, 83.8% on Cityscapes, and 48.4% on COCO-Stuff. The paper also provides systematic ablation studies of the LASS token mixer and MMSCopE decoder components, plus generalization experiments with SegNeXt and CGRSeg, and the code is promised publicly.

Significance. If the reported results are reproducible, the architecture is a useful contribution: it provides a concrete recipe for combining local attention with state-space models in dense prediction, and the decoder design respects linear-complexity scanning while preserving fine spatial detail through pixel-unshuffle. The ablation study is unusually thorough for this area and includes alternative token mixers, alternative fusion designs, and component-by-component deletions. The public code release is a further strength. However, the paper's headline claims are currently supported only by cross-paper baseline numbers and a strict 'linear-time' statement that conflicts with the use of global self-attention in Stage 4; the correctness of those claims is therefore not yet established at the level required for a journal.

major comments (5)
  1. [Abstract and §3.2] The paper calls SegMAN a 'linear-time model' in the Abstract and repeatedly stresses linear complexity, but §3.2 states that SS2D is replaced with global self-attention in Stage 4 because the H/32 × W/32 resolution makes global attention feasible. Global self-attention has quadratic complexity in its token count, so the full model is not linear-time. Please either remove the global-attention stage from the definition of SegMAN, report the exact stage-4 complexity together with a 'near-linear' claim, or keep SS2D in Stage 4 for the main results. This is load-bearing because the efficiency claim is one of the paper's two headline contributions.
  2. [§4.1] The training protocol states an AdamW learning rate of 1e-6 with 1500 warmup iterations for all segmentation runs, while saying that the SegFormer protocol is followed. SegFormer uses an initial learning rate of 6e-5 with poly decay; 1e-6 is 60× smaller and, as written, the reported accuracies are difficult to reconcile with such a schedule. Please clarify the exact learning-rate schedule (initial value, decay type, final value, and warmup length) and confirm whether '1e-6' is a typo.
  3. [Tables 2 and 4, §4.2] The headline efficiency comparisons mix measurement conventions: SegMAN FLOPs are computed with fvcore and SegMAN FPS are measured on an L40S GPU at batch size 2, while baseline FLOPs and FPS appear to be taken from their original papers. Table 4 illustrates the problem: SegMAN-B has roughly half the GFLOPs of SegFormer-B3 but only about 7% higher FPS, so the efficiency advantage is strongly metric-dependent. Please either re-run the closest baselines in the same harness or explicitly restrict the efficiency claim to FLOPs and mark which FPS numbers are not directly comparable.
  4. [Table 2 and §4.2] The 'state-of-the-art' claim is not fully supported by the selected comparison set. The table focuses on recent efficient segmentation models and includes Mask2Former only at its 47.7 mIoU configuration, omitting stronger mask-transformer baselines and other recently published results on ADE20K, Cityscapes, and COCO-Stuff. In addition, the table contains an inconsistent row labeled 'CGRSeg-T†' with 35.7M parameters in the small-model block, which does not match the CGRSeg-T configuration in the tiny block or Table 4. Please broaden the comparison to the current best published numbers and fix the labeling.
  5. [Tables 2, 3, and 4] All reported results appear to be single runs without error bars or multiple-seed statistics. Many of the claimed improvements are 0.6–1.6 mIoU, which can be within run-to-run variation for this training setup. Please provide variance estimates, specify the random seed protocol, or at minimum temper the claims and state the limitation explicitly.
minor comments (7)
  1. [Table 1] The caption says 'three SegMAN Encoder variants' but the table lists four variants (Tiny, Small, Base, Large); please adjust the caption.
  2. [Table 2] The row 'CGRSeg-T†' with 35.7M parameters in the SegMAN-S block appears to be CGRSeg-B; please align the naming with Table 4.
  3. [Supplementary Table 9] The baseline mIoU in Table 9 is 50.0, whereas the corresponding SegMAN-S row in main-paper Table 7 is 51.3; please explain this discrepancy or reconcile the two tables.
  4. [§4.1] It is not stated whether the baseline FLOPs in Tables 2–4 were recomputed with fvcore in the same way as SegMAN or taken verbatim from the original papers; please state this explicitly.
  5. [§4.2] The FPS measurement protocol (GPU, batch size, number of warmup steps) is described only for SegMAN; please indicate which baseline FPS numbers were measured under the same conditions and which are cited from the literature.
  6. [§3.1] The module is spelled 'MMSCoPE' in §3.1 and 'MMSCopE' elsewhere; please unify the spelling.
  7. [Table 7] The 'Independent scans' ablation gains +0.2 mIoU but drops FPS from 128 to 65; this trade-off deserves a sentence in the main text rather than only appearing in the table.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: SegMAN's capability and SOTA claims are empirical benchmark results with ablations; the cross-paper baseline comparison is a measurement-fairness concern, not a circular derivation.

full rationale

The paper contains no formal derivation chain whose outputs are assumed by its inputs. The central claims—that LASS combines local attention with SS2D for global and local modeling, and that MMSCopE extracts multi-scale context—are supported by measured ImageNet-1k accuracies, segmentation mIoU on three benchmarks, and systematic ablations (Tables 5, 6, 7, 8, 9, 10). The building blocks (Natten, VMamba's SS2D) are external prior work used as components, not as load-bearing self-citations. Self-citations [14, 31, 32, 33] appear only in related-work mentions, baseline tables, or general references and do not justify the paper's core results. Hyperparameter choices such as window size and SSM state dimension are tuned on validation via ablations, which is standard practice and not fitting to the test set. The main weakness is that FLOPs/FPS for baselines are taken from different papers with different hardware and counting conventions, so the efficiency comparison is not a single controlled measurement; however, this is a correctness/fairness issue, not circularity. No equation or claim reduces to its own input by construction, so the appropriate finding is no significant circularity.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The architecture parameters are tuned via validation, so they are free parameters, but no new physical entities are introduced. The main assumptions are the effectiveness of existing modules, the transferability of pretraining, and the comparability of computational cost measurements.

free parameters (4)
  • Neighborhood attention window sizes per stage = [11, 9, 7, 7]
    Chosen by ablation (Table 10) to maximize ImageNet accuracy and ADE20K mIoU.
  • SSM state dimension d_state = 1
    Default in Table 10; increasing to 16 improved ImageNet but hurt ADE20K, so kept at 1.
  • SSM expansion ratio = 1
    Default in Table 10; increasing to 2 gave mixed results on ImageNet and ADE20K.
  • MMSCopE downsampling factors = 1/2 and 1/4 of F
    Design choice validated in Table 7; removing any scale reduces mIoU.
assumptions (3)
  • domain assumption Natten and SS2D are effective modules that preserve translational equivariance and global context respectively.
    The paper relies on prior work [19, 28] showing these components work.
  • domain assumption ImageNet-1k pretraining transfers to downstream segmentation.
    Standard assumption in the field; used for all models compared.
  • ad hoc to paper The reported GFLOPs and FPS are comparable across methods despite being computed on different codebases and hardware.
    The paper computes its own FLOPs but cites GFLOPs of baselines from their papers, which may use different counting conventions.

how reviews work

0 comments
Cite this review

Pith. "Pith review of SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation." pith.science (2026). https://pith.science/paper/VCALEJWR

@misc{pith2026241211890,
  author       = {Pith},
  title        = {Pith review of: SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VCALEJWR}},
  note         = {Machine review of arXiv:2412.11890}
}
read the original abstract

High-quality semantic segmentation relies on three key capabilities: global context modeling, local detail encoding, and multi-scale feature extraction. However, recent methods struggle to possess all these capabilities simultaneously. Hence, we aim to empower segmentation networks to simultaneously carry out efficient global context modeling, high-quality local detail encoding, and rich multi-scale feature representation for varying input resolutions. In this paper, we introduce SegMAN, a novel linear-time model comprising a hybrid feature encoder dubbed SegMAN Encoder, and a decoder based on state space models. Specifically, the SegMAN Encoder synergistically integrates sliding local attention with dynamic state space models, enabling highly efficient global context modeling while preserving fine-grained local details. Meanwhile, the MMSCopE module in our decoder enhances multi-scale context feature extraction and adaptively scales with the input resolution. Our SegMAN-B Encoder achieves 85.1% ImageNet-1k accuracy (+1.5% over VMamba-S with fewer parameters). When paired with our decoder, the full SegMAN-B model achieves 52.6% mIoU on ADE20K (+1.6% over SegNeXt-L with 15% fewer GFLOPs), 83.8% mIoU on Cityscapes (+2.1% over SegFormer-B3 with half the GFLOPs), and 1.6% higher mIoU than VWFormer-B3 on COCO-Stuff with lower GFLOPs. Our code is available at https://github.com/yunxiangfu2001/SegMAN.

Figures

Figures reproduced from arXiv: 2412.11890 by the authors.

Figure 1
Figure 1. SegMAN Encoder classification performance compared [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Qualitative analysis of receptive field patterns and segmentation performance for small-sized models (27M-29M parameters). [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Overall Architecture of SegMAN. (a) Hierarchical SegMAN Encoder. (b) LASS for modeling global contexts and local details [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Qualitative results on ADE20K. Zoom in for best view [PITH_FULL_IMAGE:figures/full_fig_p015_4.png]
Figure 5
Figure 5. Figure 5: Qualitative results on Cityscapes. Zoom in for best view [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]
Figure 6
Figure 6. Figure 6: Qualitative results on COCO-Stuff-164K. We do not compare with SegFormer as its COCO-Stuff checkpoints are not released. Zoom in for best view [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. DishSeg24k: A Large-Scale Benchmark for Food Segmentation with Stochastic Expert Decoding

    cs.CV 2026-07 conditional novelty 6.0 of 10

    DishSeg24k is a 24k-image dish-level food segmentation benchmark, and the FEAST model reports +3.21 mIoU over prior methods, mostly from its mixture-of-experts decoder.

Reference graph

Works this paper leans on

67 extracted references · 38 canonical work pages · cited by 1 Pith paper

  1. [1]

    Coco- stuff: Thing and stuff classes in context

    Holger Caesar, Jasper Uijlings, and Vittorio Ferrari. Coco- stuff: Thing and stuff classes in context. In Proceedings of the IEEE conference on computer vision and pattern recog- nition, pages 1209–1218, 2018. 3, 5

    work page 2018

  2. [2]

    Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs

    Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4):834–848, 2017. 1, 2, 3

    work page 2017

  3. [3]

    Rethinking atrous convolution for semantic image segmentation

    Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. Rethinking atrous convolution for semantic image segmentation. ArXiv, abs/1706.05587, 2017

    arXiv 2017

  4. [4]

    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. 3

    2018

  5. [5]

    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. 6

    work page 2021

  6. [6]

    Masked-attention mask transformer for universal image segmentation

    Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexan- der Kirillov, and Rohit Girdhar. Masked-attention mask transformer for universal image segmentation. In Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1290–1299, 2022. 6

    work page 2022

  7. [7]

    MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark

    MMSegmentation Contributors. MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark. https : / / github . com / open - mmlab/mmsegmentation, 2020. 6

    2020

  8. [8]

    The cityscapes dataset for semantic urban scene understanding

    Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceed- ings of the IEEE conference on computer vision and pattern recognition, pages 3213–3223, 2016. 3, 5, 7

    work page 2016

Show all 67 references
  1. [9]

    Imagenet: A large-scale hierarchical image database

    Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009. 5, 7

    2009

  2. [10]

    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. 4

    2010 arXiv

  3. [11]

    FAIR. fvcore. https : / / github . com / facebookresearch/fvcore, 2021. 6

    2021

  4. [12]

    Scalable diffusion models with state space backbone

    Zhengcong Fei, Mingyuan Fan, Changqian Yu, and Junshi Huang. Scalable diffusion models with state space backbone. arXiv preprint arXiv:2402.05608, 2024. 3

    2024 arXiv

  5. [13]

    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,

  6. [14]

    Lamamba-diff: Linear-time high-fidelity diffusion models based on local at- tention and mamba

    Yunxiang Fu, Chaoqi Chen, and Yizhou Yu. Lamamba-diff: Linear-time high-fidelity diffusion models based on local at- tention and mamba. arXiv preprint arXiv:2408.02615, 2024. 3

    2024 arXiv

  7. [15]

    Is attention better than matrix decomposition? arXiv preprint arXiv:2109.04553, 2021

    Zhengyang Geng, Meng-Hao Guo, Hongxu Chen, Xia Li, Ke Wei, and Zhouchen Lin. Is attention better than matrix decomposition? arXiv preprint arXiv:2109.04553, 2021. 3

    2021 arXiv

  8. [16]

    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. 3, 4

    2023 arXiv

  9. [17]

    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. Advances in neural information processing sys- tems, 34:572–585, 2021. 3

    2021

  10. [18]

    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. 3, 5, 6, 7, 8, 2

    2022

  11. [19]

    Neighborhood attention transformer

    Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi. Neighborhood attention transformer. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6185–6194, 2023. 2, 3, 4, 7

    2023

  12. [20]

    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. 3

    2016

  13. [21]

    Bag of tricks for image classifica- tion with convolutional neural networks

    Tong He, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Jun- yuan Xie, and Mu Li. Bag of tricks for image classifica- tion with convolutional neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 558–567, 2019. 3

    2019

  14. [22]

    Transformers are rnns: Fast autoregressive transformers with linear attention

    Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and Franc ¸ois Fleuret. Transformers are rnns: Fast autoregressive transformers with linear attention. In International confer- ence on machine learning, pages 5156–5165. PMLR, 2020. 7, 8

    2020

  15. [23]

    Panoptic feature pyramid networks

    Alexander Kirillov, Ross Girshick, Kaiming He, and Piotr Doll´ar. Panoptic feature pyramid networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6399–6408, 2019. 3

    2019

  16. [24]

    Mask dino: Towards a unified transformer-based framework for object detection and segmentation

    Feng Li, Hao Zhang, Huaizhe Xu, Shilong Liu, Lei Zhang, Lionel M Ni, and Heung-Yeung Shum. Mask dino: Towards a unified transformer-based framework for object detection and segmentation. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , p...

    2023

  17. [25]

    Re- thinking vision transformers for mobilenet size and speed

    Yanyu Li, Ju Hu, Yang Wen, Georgios Evangelidis, Kamyar Salahi, Yanzhi Wang, Sergey Tulyakov, and Jian Ren. Re- thinking vision transformers for mobilenet size and speed. In Proceedings of the IEEE international conference on com- puter vision, 2023. 6

    2023

  18. [26]

    Swin-umamba: Mamba- based unet with imagenet-based pretraining

    Jiarun Liu, Hao Yang, Hong-Yu Zhou, Yan Xi, Lequan Yu, Yizhou Yu, Yong Liang, Guangming Shi, Shaoting Zhang, Hairong Zheng, et al. Swin-umamba: Mamba- based unet with imagenet-based pretraining. arXiv preprint arXiv:2402.03302, 2024. 3

    2024 arXiv

  19. [27]

    Point mamba: A novel point cloud backbone based on state space model with octree-based ordering strategy

    Jiuming Liu, Ruiji Yu, Yian Wang, Yu Zheng, Tianchen Deng, Weicai Ye, and Hesheng Wang. Point mamba: A novel point cloud backbone based on state space model with octree-based ordering strategy. arXiv preprint arXiv:2403.06467, 2024. 3

    2024 arXiv

  20. [28]

    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

  21. [29]

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

    2021

  22. [30]

    A convnet for the 2020s

    Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feicht- enhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF conference on com- puter vision and pattern recognition , pages 11976–11986,

  23. [31]

    Overlock: An overview-first- look-closely-next convnet with context-mixing dynamic ker- nels

    Meng Lou and Yizhou Yu. Overlock: An overview-first- look-closely-next convnet with context-mixing dynamic ker- nels. In IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, 2025. 3

    2025

  24. [32]

    Sparx: A sparse cross-layer connection mechanism for hierarchical vision mamba and transformer networks

    Meng Lou, Yunxiang Fu, and Yizhou Yu. Sparx: A sparse cross-layer connection mechanism for hierarchical vision mamba and transformer networks. In Proceedings of the AAAI Conference on Artificial Intelligence, 2025. 6, 7, 2

    2025

  25. [33]

    Transxnet: Learning both global and local dynamics with a dual dynamic token mixer for visual recognition

    Meng Lou, Shu Zhang, Hong-Yu Zhou, Chuan Wu, Sibei Yang, and Yizhou Yu. Transxnet: Learning both global and local dynamics with a dual dynamic token mixer for visual recognition. IEEE Transactions on Neural Networks and Learning Systems, 2025. 3

    2025

  26. [34]

    Content- aware token sharing for efficient semantic segmentation with vision transformers

    Chenyang Lu, Daan de Geus, and Gijs Dubbelman. Content- aware token sharing for efficient semantic segmentation with vision transformers. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 23631–23640, 2023. 4

    2023

  27. [35]

    Image seg- mentation using deep learning: A survey

    Shervin Minaee, Yuri Boykov, Fatih Porikli, Antonio Plaza, Nasser Kehtarnavaz, and Demetri Terzopoulos. Image seg- mentation using deep learning: A survey. IEEE transactions on pattern analysis and machine intelligence , 44(7):3523– 3542, 2021. 1

    2021

  28. [36]

    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. 3, 6, 7, 8

    2024 arXiv

  29. [37]

    On the integration of self- attention and convolution

    Xuran Pan, Chunjiang Ge, Rui Lu, Shiji Song, Guanfu Chen, Zeyi Huang, and Gao Huang. On the integration of self- attention and convolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 815–825, 2022. 7

    2022

  30. [38]

    Designing network design spaces

    Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Doll ´ar. Designing network design spaces. In Proceedings of the IEEE/CVF conference on com- puter vision and pattern recognition , pages 10428–10436,

  31. [39]

    Vi- sion transformers for dense prediction

    Ren ´e Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vi- sion transformers for dense prediction. In Proceedings of the IEEE/CVF international conference on computer vision, pages 12179–12188, 2021. 3, 4

    2021

  32. [40]

    Vm-unet: Vision mamba unet for medical image segmentation

    Jiacheng Ruan and Suncheng Xiang. Vm-unet: Vision mamba unet for medical image segmentation. arXiv preprint arXiv:2402.02491, 2024. 3

    2024 arXiv

  33. [41]

    Transnext: Robust foveal visual perception for vi- sion transformers

    Dai Shi. Transnext: Robust foveal visual perception for vi- sion transformers. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , pages 17773–17783, 2024. 3

    2024

  34. [42]

    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

  35. [43]

    Feedformer: Revisiting transformer decoder for efficient semantic segmentation

    Jae-hun Shim, Hyunwoo Yu, Kyeongbo Kong, and Suk- Ju Kang. Feedformer: Revisiting transformer decoder for efficient semantic segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence , pages 2263– 2271, 2023. 3, 6

    2023

  36. [44]

    Simplified state space layers for sequence modeling

    Jimmy TH Smith, Andrew Warrington, and Scott W Linder- man. Simplified state space layers for sequence modeling. arXiv preprint arXiv:2208.04933, 2022. 3

    2022 arXiv

  37. [45]

    Dynamic token pruning in plain vision transformers for semantic segmentation

    Quan Tang, Bowen Zhang, Jiajun Liu, Fagui Liu, and Yifan Liu. Dynamic token pruning in plain vision transformers for semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 777– 786, 2023. 3

    2023

  38. [46]

    Dim: Diffusion mamba for efficient high-resolution image synthesis

    Yao Teng, Yue Wu, Han Shi, Xuefei Ning, Guohao Dai, Yu Wang, Zhenguo Li, and Xihui Liu. Dim: Diffusion mamba for efficient high-resolution image synthesis. arXiv preprint arXiv:2405.14224, 2024. 3

    2024 arXiv

  39. [47]

    Maxvit: Multi-axis vision transformer

    Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, and Yinxiao Li. Maxvit: Multi-axis vision transformer. In European conference on computer vision, pages 459–479. Springer, 2022. 7, 2

    2022

  40. [48]

    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. Advances in neural information processing systems, 30, 2017. 5

    2017

  41. [49]

    Pyramid vision transformer: A versatile backbone for dense prediction without convolutions

    Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. In Proceedings of the IEEE/CVF international conference on computer vision , p...

    2021

  42. [50]

    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. 2

    2022

  43. [51]

    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

  44. [52]

    Pytorch image models

    Ross Wightman. Pytorch image models. https : / / github . com / rwightman / pytorch - image - models, 2019. 5

    2019

  45. [53]

    Vit-comer: Vision transformer with convolu- tional multi-scale feature interaction for dense predictions

    Chunlong Xia, Xinliang Wang, Feng Lv, Xin Hao, and Yifeng Shi. Vit-comer: Vision transformer with convolu- tional multi-scale feature interaction for dense predictions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5493–5502, 2024. 3, 6

    2024

  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. 3, 8

    2018

  47. [55]

    Mambatree: Tree topology is all you need in state space model

    Yicheng Xiao, Lin Song, Jiangshan Wang, Siyu Song, Yixiao Ge, Xiu Li, Ying Shan, et al. Mambatree: Tree topology is all you need in state space model. Advances in Neural Information Processing Systems, 37:75329–75354, 2025. 2

    2025

  48. [56]

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

    2021

  49. [57]

    Multi-scale rep- resentations by varying window attention for semantic seg- mentation

    Haotian Yan, Ming Wu, and Chuang Zhang. Multi-scale rep- resentations by varying window attention for semantic seg- mentation. ICLR, 2024. 1, 2, 3, 5, 6, 7, 8

    2024

  50. [58]

    Lite vision trans- former with enhanced self-attention

    Chenglin Yang, Yilin Wang, Jianming Zhang, He Zhang, Zijun Wei, Zhe Lin, and Alan Yuille. Lite vision trans- former with enhanced self-attention. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11998–12008, 2022. 3

    2022

  51. [59]

    Learning a discriminative fea- ture network for semantic segmentation

    Changqian Yu, Jingbo Wang, Chao Peng, Changxin Gao, Gang Yu, and Nong Sang. Learning a discriminative fea- ture network for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recog- nition, pages 1857–1866, 2018. 1

    2018

  52. [60]

    Embedding-free transformer with inference spatial reduction for efficient se- mantic segmentation

    Hyunwoo Yu, Yubin Cho, Beoungwoo Kang, Seunghun Moon, Kyeongbo Kong, and Suk-Ju Kang. Embedding-free transformer with inference spatial reduction for efficient se- mantic segmentation. In European Conference on Computer Vision, pages 92–110. Springer, 2024. 2, 3, 4, 5, 6, 7, 8

  53. [61]

    Object- contextual representations for semantic segmentation

    Yuhui Yuan, Xilin Chen, and Jingdong Wang. Object- contextual representations for semantic segmentation. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VI 16, pages 173–190. Springer, 2020. 6

    2020

  54. [62]

    Point could mamba: Point cloud learning via state space model

    Tao Zhang, Xiangtai Li, Haobo Yuan, Shunping Ji, and Shuicheng Yan. Point could mamba: Point cloud learning via state space model. arXiv preprint arXiv:2403.00762, 2024. 3

    2024 arXiv

  55. [63]

    Pyramid scene parsing network

    Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2881–2890, 2017. 1, 3

    2017

  56. [64]

    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...

  57. [65]

    Scene parsing through ade20k dataset

    Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Scene parsing through ade20k dataset. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 633–641,

  58. [66]

    Biformer: Vision transformer with bi-level routing attention

    Lei Zhu, Xinjiang Wang, Zhanghan Ke, Wayne Zhang, and Rynson WH Lau. Biformer: Vision transformer with bi-level routing attention. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , pages 10323–10333, 2023. 7

    2023

  59. [67]

    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. 3, 6, 7, 2 SegMAN: Omni-scale Context Modeling with State Sp...

    2024 arXiv

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.