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REVIEW 3 major objections 3 minor 26 references

BEVANet: Bilateral Efficient Visual Attention Network for Real-Time Semantic Segmentation

T0 review · 3 major / 3 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read BEVANet bundles large-kernel attention modules into a bilateral network, claiming 81.0% mIoU on Cityscapes at 33 FPS.

desk verdict BEVANet is a competently assembled architecture-engineering paper that recombines known LKA components, but the state-of-the-art claim is undercut by single-run results and adopted baselines. read the letter →

arxiv 2508.07300 v1 pith:QYBTBYWJ submitted 2025-08-10 cs.CV

Ping-Mao Huang , I-Tien Chao , Ping-Chia Huang , Jia-Wei Liao , Yung-Yu Chuang This is my paper · ORCID
classification cs.CV
keywords semanticsegmentationreal-timelargekernelattentionbilateralnetworkfeaturefusionCityscapesboundaryguidanceselection
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 proposes BEVANet, a real-time semantic segmentation network that uses large kernel attention to capture both broad context and fine detail. Its central claim is that a bilateral architecture with four new modules—SDLSKA, CKS, DLKPPM, and BGAF—achieves 81.0 percent mIoU on Cityscapes after pretraining and 79.3 percent without, running at 33 frames per second. If correct, this shows that large-kernel attention can be made efficient enough for real-time use while improving accuracy over the PIDNet baseline. The authors intend to establish that each module contributes meaningfully, attributing a cumulative 1.5 percent mIoU gain to them.

What carries the argument

The central mechanism is the Efficient Visual Attention (EVA) block built from Sparse Decomposed Large Separable Kernel Attention (SDLSKA) and Comprehensive Kernel Selection (CKS). SDLSKA expands the receptive field to 35 through a 5×5 convolution plus 1×11 and 11×1 strip kernels with dilation 3; CKS fuses small, large-horizontal, and large-vertical kernel features using joint channel and spatial attention, letting the network adapt its effective field per feature.

What would settle it

Run the Table 4 ablation five or more times with different random seeds and report mean and standard deviation. If the cumulative 1.5 percent mIoU gain of BEVANet over the PIDNet baseline is within one standard deviation of run-to-run variance, the central claim that the new modules cause the improvement is falsified. Alternatively, replace SDLSKA with plain LSKA at matched FLOPs and parameters; if mIoU does not drop, the sparse decomposition is not the cause.

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Extended reading notes

Core claim

BEVANet introduces the LKA mechanism into a two-branch network: a high-level branch for semantics and a low-level branch for details, with frequent communication via a Boundary Guided Adaptive Fusion module. The Sparse Decomposed Large Separable Kernel Attention expands the receptive field to 35 with low computation by decomposing a large kernel into a 5×5 convolution and two dilated strip kernels, while the Comprehensive Kernel Selection mechanism dynamically fuses features from kernels of different sizes. The Deep Large Kernel Pyramid Pooling Module enriches context while reducing pooling information loss. On Cityscapes, BEVANet reaches 81.0 percent mIoU after ImageNet pretraining and 79.3

Load-bearing premise

The load-bearing premise is that the single-run mIoU differences of 0.1 to 0.5 points in Table 4 are statistically real; if they fall within run-to-run noise, the claimed contributions of the four modules are not supported.

Editorial extensions

If this is right

  • If the reported results hold, BEVANet establishes that large-kernel attention can be computed in real time and still outperform standard convolutional real-time baselines on Cityscapes.
  • The 79.3 percent mIoU without ImageNet pretraining suggests the design reduces dependence on large pretraining datasets, easing deployment in data-limited settings.
  • The bilateral architecture with boundary-guided fusion may generalize to other pixel-labeling tasks, such as depth estimation or edge detection, where boundary accuracy matters.
  • The modular design (SDLSKA, CKS, DLKPPM, BGAF) provides a recipe that other lightweight networks can adopt.

Reading between the lines

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

  • If the per-module gains are real, a natural extension is to apply the SDLSKA-CKS pair to backbone networks in detection or instance segmentation, where large receptive fields also help.
  • The claim that CKS outperforms decoupled channel-spatial selection (LSKNet/SKNet) predicts a testable hypothesis: coupling channel and spatial attention during kernel fusion should consistently beat separate selection modules in other architectures.
  • A direct test of the non-pretraining claim would be to scale BEVANet up without pretraining on tougher benchmarks like ADE20K; if the advantage persists, the design may genuinely compensate for missing pretraining.
  • The absence of repeated-seed experiments leaves open the possibility that some of the 0.1-point improvements are noise; a multi-seed rerun of Table 4 would clarify which modules are load-bearing.
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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

3 major / 3 minor

Summary. The paper proposes BEVANet, a bilateral network for real-time semantic segmentation. It combines two branches (detail and semantic) with four new components: an Efficient Visual Attention (EVA) block built from Sparse Decomposed Large Separable Kernel Attention (SDLSKA), a Comprehensive Kernel Selection (CKS) mechanism, a Deep Large Kernel Pyramid Pooling Module (DLKPPM), and a Boundary Guided Adaptive Fusion (BGAF) module. The main empirical claims are 81.0% mIoU on Cityscapes val with ImageNet pretraining, 79.3% without pretraining, and 33 FPS on an RTX 3090, which the paper labels state-of-the-art. Ablations in Tables 4 and 5 attribute gains of 0.26-0.8 mIoU points to individual modules and 1.5 points cumulatively.

Significance. If confirmed, BEVANet would offer a modest but useful accuracy improvement over PIDNet-M at real-time speeds, with the interesting property of retaining most of the gain without ImageNet pretraining. The paper contributes a concrete combination of large-kernel attention, selective kernel fusion, pyramid pooling, and boundary-guided fusion, and it releases code. These are real strengths. However, the headline margin (0.9 points over PIDNet-M with pretraining, 1.05 points without) rests entirely on single-run experiments and on baseline numbers adopted from prior papers. That is insufficient evidence for the 'state-of-the-art' label as it stands, though the issue is fixable with additional experiments or more cautious claims.

major comments (3)
  1. [Section 3.3, Table 4] The ablation study reports one run per configuration and no error bars or significance tests. The individual increments (0.8, 0.26, 0.4, and the 0.53 in Table 5) and the cumulative 1.5-point 'Performance Enhancement' are within the range of seed-to-seed variation commonly observed for Cityscapes mIoU. The statement that 'SDLSKA significantly outperforms its components' and the attribution of the 1.5-point gain to the proposed modules are therefore not statistically supported. Please provide repeated-seed results (mean and standard deviation) for the key rows, or at least for the baseline, final model, and the four module increments, and state whether differences are reproducible.
  2. [Tables 1-3 and Section 3.1] The SoTA comparison is cross-paper: Table 1 states that most baselines are 'adopted from PIDNet [8]', and Table 3 reuses PIDNet's no-pretraining numbers. BEVANet is trained with a different recipe (batch size 12, LR 0.008, 484 epochs, OHEM) from that used by PIDNet in its original paper. Thus the 0.9-point advantage over PIDNet-M in Table 1 and the 1.05-point advantage in Table 3 could be due to protocol or environment differences rather than the architecture. Please re-run PIDNet-M and, if feasible, one or two other baselines under the exact same training and evaluation protocol, and report the comparison including variance.
  3. [Section 3.2, Table 1] The paper calls BEVANet 'state-of-the-art' and 'a superior balance of speed and accuracy,' but the accuracy gain over PIDNet-M is 0.9 mIoU at the cost of 40.8 additional GFLOPs, 24.2 million extra parameters, and 7 FPS. Without an accuracy-efficiency metric or a controlled comparison, the 'superior balance' claim is not established by the table alone. This is secondary to the reproducibility issue, but the claim should be calibrated to the actual cost difference.
minor comments (3)
  1. [Global] The model name is typeset inconsistently as 'BEV ANet' and 'BEVANet'; please standardize. Also 'Resolusion' in Table 1 should be 'Resolution'.
  2. [Section 2.1] The sentence 'This enables continuous branch interaction to improve semantic understanding and object boundary .' appears incomplete; the section ends abruptly without explaining how the two branches communicate beyond referring to Fig. 3.
  3. [Section 3.2, Table 2] The introduction says BEVANet-S 'further achieves 83% mIoU on CamVid,' while Table 2 reports 83.1; please be precise. Also, the claim that BEVANet-S beats PIDNet-S-Wider by 1.1% mIoU while having 40 GFLOPs fewer should be checked against the same single-run caveat as the Cityscapes numbers.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: BEVANet is an empirical architecture evaluation against external PIDNet baselines; no fitted parameter is renamed as a prediction and no load-bearing self-citation chain is present.

full rationale

BEVANet is an empirical architecture paper; its contributions are new modules (SDLSKA, CKS, DLKPPM, BGAF) whose effectiveness is measured on Cityscapes/CamVid validation, not derived from a first-principles equation in which the conclusion is an input. The baselines in Tables 1–3 are explicitly 'adopted from PIDNet [8]', an external prior paper by different authors, so this is an independent cross-paper comparison rather than a self-citation chain. No parameter is fitted to the headline mIoU and then reported as a prediction; ablation rows compare architectural variants under the same training recipe. The main weaknesses—single-run mIoU differences without error bars, possible validation-set selection, and adopted rather than re-measured baselines—are concerns about statistical evidence and experimental protocol, not about definitional circularity. No step in the paper reduces to its own inputs by construction, so the circularity score is 0.

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

The central claim rests on standard transfer-learning and benchmark-comparison assumptions rather than on mathematical axioms. The proposed modules are new architecture components introduced for this paper and validated only by its own ablations.

free parameters (3)
  • SDLSKA dilation rate d=3 = 3
    Hand-chosen dilation for strip kernels 1x11 and 11x1; combined with 5x5 kernel yields receptive field 35. Introduced ad hoc in Sec. 2.2.1.
  • SDLSKA base kernel size 5x5 = 5
    Hand-chosen base kernel in SDLSKA; no ablation of kernel size reported.
  • DLKPPM pooling kernel sizes = 9/stride4, 5/stride2
    Hand-chosen hierarchical pooling sizes in DLKPPM, Fig. 4; no ablation.
assumptions (3)
  • domain assumption ImageNet pretraining transfers to Cityscapes/CamVid segmentation
    The method relies on standard transfer learning assumptions; pretraining protocol in Sec. 3.1.
  • domain assumption Cityscapes validation mIoU is a reliable proxy for real-time segmentation performance
    The paper's SOTA claims rest entirely on single-run mIoU on the validation set; no test set evaluation.
  • domain assumption Baseline numbers adopted from PIDNet were measured under comparable settings
    Tables 1-2 say 'Most of the results are adopted from PIDNet [8]', so fair comparison depends on that assumption.
invented entities (4)
  • SDLSKA module
    purpose: Capture long-range dependencies in the EVA block via decomposed strip kernels
    New module introduced in Sec. 2.2.1; only evaluated in the paper's own ablation.
  • CKS module
    purpose: Dynamically fuse multi-scale kernel features in channel and spatial dimensions
    New module introduced in Sec. 2.2.2; evaluated only in-house.
  • DLKPPM module
    purpose: Context enrichment via dilated convolution and LKA pyramid pooling
    New module from Sec. 2.3; evaluated only in Table 5 ablation.
  • BGAF module
    purpose: Adaptive fusion of semantic and detail features guided by boundaries
    New module from Sec. 2.4; evaluated only in Table 4 ablation.

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Cite this review

Pith. "Pith review of BEVANet: Bilateral Efficient Visual Attention Network for Real-Time Semantic Segmentation." pith.science (2026). https://pith.science/paper/QYBTBYWJ

@misc{pith2026250807300,
  author       = {Pith},
  title        = {Pith review of: BEVANet: Bilateral Efficient Visual Attention Network for Real-Time Semantic Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QYBTBYWJ}},
  note         = {Machine review of arXiv:2508.07300}
}
read the original abstract

Real-time semantic segmentation presents the dual challenge of designing efficient architectures that capture large receptive fields for semantic understanding while also refining detailed contours. Vision transformers model long-range dependencies effectively but incur high computational cost. To address these challenges, we introduce the Large Kernel Attention (LKA) mechanism. Our proposed Bilateral Efficient Visual Attention Network (BEVANet) expands the receptive field to capture contextual information and extracts visual and structural features using Sparse Decomposed Large Separable Kernel Attentions (SDLSKA). The Comprehensive Kernel Selection (CKS) mechanism dynamically adapts the receptive field to further enhance performance. Furthermore, the Deep Large Kernel Pyramid Pooling Module (DLKPPM) enriches contextual features by synergistically combining dilated convolutions and large kernel attention. The bilateral architecture facilitates frequent branch communication, and the Boundary Guided Adaptive Fusion (BGAF) module enhances boundary delineation by integrating spatial and semantic features under boundary guidance. BEVANet achieves real-time segmentation at 33 FPS, yielding 79.3% mIoU without pretraining and 81.0% mIoU on Cityscapes after ImageNet pretraining, demonstrating state-of-the-art performance. The code and model is available at https://github.com/maomao0819/BEVANet.

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Reference graph

Works this paper leans on

26 extracted references · 22 canonical work pages

  1. [8]

    U-net: Convolutional networks for biomedical im- age segmentation,

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox, “U-net: Convolutional networks for biomedical im- age segmentation,” in Medical image computing and computer-assisted intervention–MICCAI 2015: 18th in- ternational conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 . Springer, 2015. 1

    work page 2015

  2. [1]

    INTRODUCTION Semantic segmentation, assigning class labels to every pixel, is vital in computer vision. Initially reliant on hand-crafted features, it evolved with Fully Convolutional Networks [1] and models like UNet [2], improving performance with encoder-decoder structures and skip connections. PSPNet [3] enhanced results using pyramid pooling. However...

    work page

  3. [2]

    The Deep Large Kernel Pyramid Pooling Module (DLKPPM) leverages large kernels for contextual enrichment

    METHODOLOGY We propose the Efficient Visual Attention (EV A) module, uti- lizing Sparse Decomposed Large Separable Kernel Attentions (SDLSKA) and Comprehensive Kernel Selection (CKS) to adaptively enlarge the receptive field. The Deep Large Kernel Pyramid Pooling Module (DLKPPM) leverages large kernels for contextual enrichment. Additionally, the Bilatera...

    work page

  4. [3]

    EXPERIMENTS 3.1. The Datasets and Implementation Details We mainly evaluated on Cityscapes [4], a widely recognized benchmark dataset for urban scene parsing, containing 2,975 training, 500 validation, and 1,525 testing images with a high resolution of 2048 × 1024. It includes 19 classes for seman- tic segmentation evaluation. We also use the CamVid [19] ...

    work page

  5. [4]

    CONCLUSION Our BEV ANet model achieves competitive performance compared to state-of-the-art methods while reaching real- time processing at 33 FPS. Its key features, including the SDLSKA block for expanding receptive fields and the CKS mechanism for dynamic adjustments, enable accurate small object detection and refined boundaries. The bilateral archi- te...

    work page

  6. [5]

    Bisenet v2: Bilateral network with guided aggregation for real-time seman- tic segmentation,

    Changqian Yu, Changxin Gao, Jingbo Wang, Gang Yu, Chunhua Shen, and Nong Sang, “Bisenet v2: Bilateral network with guided aggregation for real-time seman- tic segmentation,” International journal of computer vi- sion, vol. 129, pp. 3051–3068, 2021. 1, 4

    work page 2021

  7. [6]

    Re- thinking bisenet for real-time semantic segmentation,

    Mingyuan Fan, Shenqi Lai, Junshi Huang, Xiaoming Wei, Zhenhua Chai, Junfeng Luo, and Xiaolin Wei, “Re- thinking bisenet for real-time semantic segmentation,” in Proceedings of the IEEE/CVF conference on com- puter vision and pattern recognition , 2021. 1, 4

    work page 2021

  8. [7]

    Fully convolutional networks for semantic segmenta- tion,

    Jonathan Long, Evan Shelhamer, and Trevor Darrell, “Fully convolutional networks for semantic segmenta- tion,” in Proceedings of the IEEE conference on com- puter vision and pattern recognition , 2015. 1

    work page 2015

Show all 26 references
  1. [9]

    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 vi- sion and pattern recognition, 2017, pp. 2881–2890. 1

    2017

  2. [10]

    attempt to optimize this, but challenges remain. Recent approaches like RepLKNet [11] highlight the ad- vantages of Large Kernel Attention (LKA), integrating con- volution and attention mechanisms to capture global context effectively. SLaK [12] expanded kernel sizes to 51 by ...

    2025 arXiv

  3. [11]

    The cityscapes dataset for semantic urban scene understand- ing,

    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 understand- ing,” in Proceedings of the IEEE conference on com- puter vision and patter...

    2016

  4. [12]

    More convnets in the 2020s: Scaling up ker- nels beyond 51x51 using sparsity,

    Shiwei Liu, Tianlong Chen, Xiaohan Chen, Xuxi Chen, Qiao Xiao, Boqian Wu, Tommi K ¨arkk¨ainen, Mykola Pechenizkiy, Decebal Mocanu, and Zhangyang Wang, “More convnets in the 2020s: Scaling up ker- nels beyond 51x51 using sparsity,” arXiv preprint arXiv:2207.03620, 2022. 1, 3, 5

    2022 arXiv

  5. [13]

    and LSKA [14], demonstrated in Fig. 2. It consists of two main components: the Large Kernel Attention (LKA) and the Convolution Feed-Forward Network (CFFN). The LKA block captures long-range dependencies. By utilizing SDLSKA, it expands the receptive field to a broader context...

  6. [14]

    Deep dual-resolution networks for real-time and accurate semantic segmentation of road scenes,

    Yuanduo Hong, Huihui Pan, Weichao Sun, and Yisong Jia, “Deep dual-resolution networks for real-time and accurate semantic segmentation of road scenes,” arXiv preprint arXiv:2101.06085, 2021. 1, 3, 4, 5

    2021 arXiv

  7. [15]

    Pidnet: A real-time semantic segmentation network inspired by pid controllers,

    Jiacong Xu, Zixiang Xiong, and Shankar P Bhat- tacharyya, “Pidnet: A real-time semantic segmentation network inspired by pid controllers,” in Proceedings of the IEEE/CVF conference on computer vision and pat- tern recognition, 2023, pp. 19529–19539. 1, 2, 4, 5

    2023

  8. [16]

    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. 1

    2010 arXiv

  9. [17]

    Seaformer: Squeeze-enhanced axial trans- former for mobile semantic segmentation,

    Qiang Wan, Zilong Huang, Jiachen Lu, YU Gang, and Li Zhang, “Seaformer: Squeeze-enhanced axial trans- former for mobile semantic segmentation,” in The eleventh international conference on learning represen- tations, 2023. 1

    2023

  10. [18]

    Scaling up your kernels to 31x31: Re- visiting large kernel design in cnns,

    Xiaohan Ding, Xiangyu Zhang, Jungong Han, and Guiguang Ding, “Scaling up your kernels to 31x31: Re- visiting large kernel design in cnns,” in Proceedings of the IEEE/CVF conference on computer vision and pat- tern recognition, 2022, pp. 11963–11975. 1

    2022

  11. [19]

    Visual attention net- work,

    Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming- Ming Cheng, and Shi-Min Hu, “Visual attention net- work,” Computational Visual Media, vol. 9, no. 4, 2023. 1, 2

    2023

  12. [20]

    Large separable kernel attention: Rethinking the large kernel attention design in cnn,

    Kin Wai Lau, Lai-Man Po, and Yasar Abbas Ur Rehman, “Large separable kernel attention: Rethinking the large kernel attention design in cnn,” Expert Systems with Ap- plications, vol. 236, pp. 121352, 2024. 1, 2, 3, 5

    2024

  13. [21]

    Large selective kernel network for remote sensing object detection,

    Yuxuan Li, Qibin Hou, Zhaohui Zheng, Ming-Ming Cheng, Jian Yang, and Xiang Li, “Large selective kernel network for remote sensing object detection,” in Pro- ceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 16794–16805. 1, 3, 5

    2023

  14. [22]

    Selective kernel networks,

    Xiang Li, Wenhai Wang, Xiaolin Hu, and Jian Yang, “Selective kernel networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 510–519. 1, 3

    2019

  15. [23]

    Pp-liteseg: A superior real- time semantic segmentation model,

    Juncai Peng, Yi Liu, Shiyu Tang, Yuying Hao, Lutao Chu, Guowei Chen, Zewu Wu, Zeyu Chen, Zhiliang Yu, Yuning Du, et al., “Pp-liteseg: A superior real- time semantic segmentation model,” arXiv preprint arXiv:2204.02681, 2022. 4

    2022 arXiv

  16. [24]

    Semantic flow for fast and accurate scene parsing,

    Xiangtai Li, Ansheng You, Zhen Zhu, Houlong Zhao, Maoke Yang, Kuiyuan Yang, Shaohua Tan, and Yun- hai Tong, “Semantic flow for fast and accurate scene parsing,” in Computer Vision–ECCV 2020: 16th Eu- ropean Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16. S...

    2020

  17. [25]

    Semantic object classes in video: A high- definition ground truth database,

    Gabriel J Brostow, Julien Fauqueur, and Roberto Cipolla, “Semantic object classes in video: A high- definition ground truth database,” Pattern recognition letters, vol. 30, no. 2, pp. 88–97, 2009. 4

    2009

  18. [26]

    Imagenet large scale visual recognition challenge,

    Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al., “Imagenet large scale visual recognition challenge,” In- ternational journal of computer vision , vol. 115, 2015. 4

    2015

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Reviewed August 5, 2026 · model on record in the stance chip above.