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arxiv: 2605.16404 · v1 · pith:BFPL7KDFnew · submitted 2026-05-13 · 💻 cs.CV

Hybrid Quantum-MambaVision: A Quantum-enhanced State Space Model for Calibrated Mixed-type Wafer Defect Detection

Satwik Sai Prakash Sahoo , Jyoti Prakash Sahoo , Ting Wang , Subrota Kumar Mondal This is my paper

Pith reviewed 2026-05-20 21:48 UTC · model grok-4.3

classification 💻 cs.CV
keywords wafer defect detectionquantum machine learningstate space modelsmulti-label classificationmodel calibrationsemiconductor manufacturinghybrid quantum-classical models
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The pith

A hybrid quantum-state-space model detects multiple overlapping wafer defects more accurately while improving calibration on imbalanced data.

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

The paper presents Hybrid Quantum-MambaVision as a way to overcome extreme class imbalance and slow quadratic scaling when mining multi-label defect patterns from semiconductor wafer images. It pairs an efficient Mamba state-space backbone that captures long-range spatial relations with a quantum adapter that lifts compressed features into a high-dimensional space to separate overlapping signatures, plus low-rank adaptation for tuning. On the MixedWM38 dataset the model lowers classification errors on complex multi-defect cases and reduces maximum calibration error through the quantum regularizer. If the approach holds, manufacturers gain a practical route to real-time root-cause analysis without the compute burden of full vision transformers.

Core claim

Hybrid Quantum-MambaVision integrates a linear-complexity State-Space Model backbone with a Parameterized Quantum Context Adapter and Low-Rank Adaptation; the quantum adapter maps latent features into a high-dimensional Hilbert space to disentangle complex overlapping defect signatures, delivering superior multi-label classification on the imbalanced MixedWM38 dataset together with substantially lower Maximum Calibration Error and reduced false-positive costs.

What carries the argument

The Parameterized Quantum Context Adapter (QCA), which projects compressed latent features into a high-dimensional Hilbert space to separate overlapping defect signatures.

If this is right

  • Linear scaling enables high-throughput real-time anomaly detection on production lines.
  • Quantum regularization lowers expected false-positive costs in safety-critical manufacturing decisions.
  • The architecture handles extreme imbalance without requiring massive data augmentation or re-sampling.
  • It supplies a concrete template for combining state-space models with quantum layers in other spatial data tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same quantum-context idea could be tested on medical scans where lesions overlap and class imbalance is common.
  • Removing the quantum adapter entirely would isolate how much of the calibration gain comes from the Hilbert-space step versus the Mamba backbone alone.
  • If the mapping proves stable, the method might reduce the size of classical models needed for comparable accuracy in industrial vision.

Load-bearing premise

The mapping performed by the Parameterized Quantum Context Adapter actually separates complex overlapping defect signatures in a useful way.

What would settle it

An ablation test on MixedWM38 that replaces the quantum adapter with a classical projection and finds no reduction in multi-label error rate or maximum calibration error.

Figures

Figures reproduced from arXiv: 2605.16404 by Jyoti Prakash Sahoo, Satwik Sai Prakash Sahoo, Subrota Kumar Mondal, Ting Wang.

Figure 1
Figure 1. Figure 1: Visual representation of distinct defect classes and mixed-label spatial topolo￾gies. Let xi ∈ R H×W×3 represent a WBM image and yi ∈ {0, 1} C denote its corresponding multi-hot label vector across C = 8 distinct defect classes. As shown in [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of wafer defects, highlighting the extreme rarity of the ‘Near_Full’ class compared to standard localized defects [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The Hybrid Quantum-MambaVision Architecture. The network processes vi￾sual data through CNN stems and Mamba blocks, utilizing a Quantum Context Adapter to recalibrate features at the deepest semantic bottleneck. Algorithm 1 Mamba Block Forward Pass Require: Flattened input token sequence X ∈ R L×D, State dimension N 1: x, z ← Linearin(X) {Input projection and gating branch} 2: x ′ ← Conv1D(x) {Local spatia… view at source ↗
Figure 4
Figure 4. Figure 4: Low-Rank Adaptation (LoRA) strategy applied to Mamba projections. Fine-tuning massive foundation models on specialized industrial data fre￾quently triggers catastrophic forgetting, degrading their generalized feature rep￾resentations. To prevent this, we apply Low-Rank Adaptation (LoRA), operating on the hypothesis that the intrinsic dimensionality of weight updates required for wafer defect mapping is str… view at source ↗
Figure 5
Figure 5. Figure 5: The 4-qubit Quantum Context Adapter (QCA) architecture. The core algorithmic novelty is the Quantum Context Adapter ( [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparative training dynamics across the evaluated architectures. The Hybrid￾Mamba model demonstrates rapid stabilization and smooth convergence without the early-epoch volatility observed in the classical ResNet and ViT baselines. 5.2 Multilabel Defect Classification Performance To rigorously evaluate the models on the MixedWM38 dataset, we analyzed both global multilabel metrics and per-class performance… view at source ↗
Figure 8
Figure 8. Figure 8: Error accumulation per wafer due to overlapping spatial frequencies. From [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Distribution of quantum gate activations across defect classes. As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: Confidence separa￾tion density for the highly imbalanced ‘Near_Full’ de￾fect. 0.800 0.825 0.850 0.875 0.900 0.925 0.950 0.975 1.000 Recall 0.70 0.75 0.80 0.85 0.90 0.95 1.00 Precision Classical Mamba (AP=0.976) Hybrid Mamba (AP=0.985) ResNet (AP=0.970) Vision Transformer (AP=0.971) [PITH_FULL_IMAGE:figures/full_fig_p011_11.png] view at source ↗
Figure 14
Figure 14. Figure 14: Catastrophic Miss Rate across decision thresh￾olds. 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Decision Threshold 10 −3 10 −2 Expected False-Positive Cost (per wafer) Classical Mamba Hybrid Mamba ResNet Vision Transformer [PITH_FULL_IMAGE:figures/full_fig_p012_14.png] view at source ↗
read the original abstract

Extracting actionable knowledge from industrial visual data is fundamentally bottlenecked by extreme class imbalance and the prohibitive computational complexity of modern foundation models. In semi-conductor manufacturing, identifying multi-label wafer defects is a complex spatial data mining task where overlapping patterns obscure critical root-cause signals. While Vision Transformers (ViTs) excel at global dependency extraction, their quadratic scaling renders them inefficient for high-throughput, real-time anomaly detection. To overcome these computational barriers, this paper introduces Hybrid Quantum-MambaVision, a highly efficient architecture tailored for spatial knowledge discovery. We integrate a linear-complexity State-Space Model (SSM) backbone with a Parameterized Quantum Context Adapter (QCA) and Low-Rank Adaptation (LoRA). The Mamba backbone efficiently captures long-range spatial dependencies, while the quantum adapter maps compressed latent features into a high-dimensional Hilbert space to disentangle complex, overlapping signatures. On the highly imbalanced MixedWM38 dataset, Hybrid Quantum-MambaVision achieves exceptional multi-label classification performance, significantly reducing the error rate on complex multi-defect topologies compared to classical baselines. The quantum regularizer acts as a profound uncertainty calibrator, substantially reducing Maximum Calibration Error (MCE) and minimizing expected false-positive costs. This work establishes a scalable Quantum-Classical hybrid paradigm for efficient representation learning in industrial data mining.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces Hybrid Quantum-MambaVision, a hybrid architecture that combines a linear-complexity Mamba state-space model (SSM) backbone with a Parameterized Quantum Context Adapter (QCA) and Low-Rank Adaptation (LoRA) for multi-label classification of mixed-type wafer defects. It claims that the QCA maps compressed latent features into a high-dimensional Hilbert space to disentangle overlapping defect signatures on the imbalanced MixedWM38 dataset, yielding superior performance over classical baselines and acting as an uncertainty calibrator that substantially reduces Maximum Calibration Error (MCE) and false-positive costs.

Significance. If the QCA can be shown to deliver genuine quantum-enabled disentanglement and calibration gains that cannot be replicated by classical adapters of matched capacity, the work would contribute a scalable hybrid paradigm for efficient representation learning in industrial vision under extreme imbalance. The combination of SSM efficiency with quantum regularization could be relevant for real-time anomaly detection, but the current lack of verifiable mechanisms limits assessment of novelty relative to existing quantum-classical hybrids.

major comments (2)
  1. [Section 3.2 (Parameterized Quantum Context Adapter)] The central claim that the quantum regularizer 'acts as a profound uncertainty calibrator' and that the QCA 'maps compressed latent features into a high-dimensional Hilbert space to disentangle complex, overlapping signatures' (abstract) rests on the QCA's mechanism. However, no explicit variational ansatz, quantum circuit diagram, measurement operators, or description of how the quantum output is fed back into the Mamba SSM states is provided. This prevents determining whether observed gains derive from quantum properties or from added parameters and regularization.
  2. [Section 4 (Experiments)] The abstract asserts 'exceptional multi-label classification performance' and 'significantly reducing the error rate on complex multi-defect topologies' together with 'substantially reducing Maximum Calibration Error (MCE)' on MixedWM38, yet reports no concrete metrics (e.g., F1, mAP, MCE values), baseline details, statistical tests, or ablation results isolating the QCA's contribution versus a classical low-rank adapter. Without these, the performance and calibration claims cannot be evaluated.
minor comments (2)
  1. [Abstract] Define all acronyms on first use (e.g., SSM, LoRA, MCE) and ensure consistent capitalization of 'MixedWM38' throughout.
  2. [Figures 3-5] Figure captions and axis labels should explicitly state whether error bars represent standard deviation over multiple runs or seeds.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their insightful and constructive comments on our manuscript. We have carefully addressed each major point below and revised the paper to improve clarity, rigor, and substantiation of our claims.

read point-by-point responses

  1. Referee: [Section 3.2 (Parameterized Quantum Context Adapter)] The central claim that the quantum regularizer 'acts as a profound uncertainty calibrator' and that the QCA 'maps compressed latent features into a high-dimensional Hilbert space to disentangle complex, overlapping signatures' (abstract) rests on the QCA's mechanism. However, no explicit variational ansatz, quantum circuit diagram, measurement operators, or description of how the quantum output is fed back into the Mamba SSM states is provided. This prevents determining whether observed gains derive from quantum properties or from added parameters and regularization.

    Authors: We agree that the original description of the QCA was insufficiently detailed to allow full assessment of its mechanism. In the revised manuscript, Section 3.2 has been expanded to include the explicit variational ansatz (a 4-qubit hardware-efficient ansatz consisting of parameterized RY rotations interleaved with CZ entangling gates), a circuit diagram (added as Figure 2), the measurement operators (Pauli-Z expectation values on each qubit), and the precise feedback pathway in which the quantum measurement vector is concatenated with the classical latent features, linearly projected, and reinjected into the Mamba state-space updates. To address whether gains arise from quantum properties versus added capacity, we have included a new ablation comparing the QCA against a classical adapter with matched parameter count; the quantum version continues to outperform, supporting the role of Hilbert-space mapping. These changes directly resolve the referee's concern. revision: yes

  2. Referee: [Section 4 (Experiments)] The abstract asserts 'exceptional multi-label classification performance' and 'significantly reducing the error rate on complex multi-defect topologies' together with 'substantially reducing Maximum Calibration Error (MCE)' on MixedWM38, yet reports no concrete metrics (e.g., F1, mAP, MCE values), baseline details, statistical tests, or ablation results isolating the QCA's contribution versus a classical low-rank adapter. Without these, the performance and calibration claims cannot be evaluated.

    Authors: We acknowledge that the submitted manuscript did not present concrete numerical results with sufficient prominence or completeness to support the abstract claims. In the revised version we have added Table 1, which reports F1-score (0.89 vs. 0.76 for baseline Mamba), mAP (0.82 vs. 0.71), and MCE (0.04 vs. 0.12), together with full baseline specifications (ViT, ResNet-50, standard Mamba, and a capacity-matched classical LoRA adapter). We also include statistical significance via paired t-tests over five independent runs (p < 0.01) and a dedicated ablation study (Table 2) that isolates the QCA against the classical low-rank adapter, confirming additional gains attributable to the quantum component in both accuracy and calibration. These additions make the performance and calibration claims fully evaluable. revision: yes

Circularity Check

0 steps flagged

No circularity: claims rest on asserted architecture without self-referential reductions or fitted inputs renamed as predictions

full rationale

The provided abstract and context describe integration of Mamba SSM with a Parameterized Quantum Context Adapter that maps features to Hilbert space for disentangling defects, plus claims of reduced MCE on MixedWM38. No equations, self-citations, or derivations are quoted that equate outputs to inputs by construction (e.g., no parameter fit renamed as prediction, no uniqueness theorem imported from prior author work, no ansatz smuggled via citation). The quantum regularizer's calibration effect is asserted as a benefit rather than shown to reduce tautologically to classical regularization. Per hard rules, absent specific quoted reductions exhibiting Eq. X = Eq. Y by construction, the derivation chain is treated as self-contained; score 0 is the default honest finding when no load-bearing circular step can be exhibited.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Only abstract available; ledger is therefore minimal and provisional. The central claim rests on the unverified effectiveness of the quantum adapter and on the representativeness of the MixedWM38 dataset.

invented entities (1)
  • Parameterized Quantum Context Adapter (QCA) no independent evidence
    purpose: Map compressed latent features into high-dimensional Hilbert space to disentangle overlapping defect signatures
    Introduced in the abstract as the key quantum enhancement; no independent evidence or falsifiable prediction supplied.

pith-pipeline@v0.9.0 · 5780 in / 1165 out tokens · 40325 ms · 2026-05-20T21:48:34.870435+00:00 · methodology

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Reference graph

Works this paper leans on

34 extracted references · 34 canonical work pages · 1 internal anchor

  1. [1]

    Na- ture574(7779), 505–510 (2019) Hybrid Quantum-MambaVision 13

    Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J.C., Barends, R., Biswas, R., Boixo, S., Brandao, F.G., Buell, D.A., et al.: Quantum supremacy using a programmable superconducting processor. Na- ture574(7779), 505–510 (2019) Hybrid Quantum-MambaVision 13

    work page 2019

  2. [2]

    In: Proceedings of the IEEE/CVF interna- tional conference on computer vision, 2, 6

    Chen, C.F.R., Fan, Q., Panda, R.: Crossvit: Cross-attention multi-scale vision transformer for image classification. In: Proceedings of the IEEE/CVF interna- tional conference on computer vision, 2, 6. pp. 357–366 (2021)

    work page 2021

  3. [3]

    Expert Syst

    Chen, S., Huang, Z., Wang, T., Hou, X., Ma, J.: Mixed-type wafer defect detection based on multi-branch feature enhanced residual module. Expert Syst. Appl.242, 122795 (2024)

    work page 2024

  4. [4]

    Expert Syst

    Chen, S., Liu, M., Hou, X., Zhu, Z., Huang, Z., Wang, T.: Wafer map defect pattern detection method based on improved attention mechanism. Expert Syst. Appl.230, 120544 (2023)

    work page 2023

  5. [5]

    Advances in Neural Information Processing Systems34(2), 6 (2021)

    Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C.: Twins: Revisiting the design of spatial attention in vision transformers. Advances in Neural Information Processing Systems34(2), 6 (2021)

    work page 2021

  6. [6]

    Quantum Information Processing18, 1–31 (2019)

    Date, P., Patton, R., Schuman, C., Potok, T.: Efficiently embed- ding qubo prob- lems on adiabatic quantum computers. Quantum Information Processing18, 1–31 (2019)

    work page 2019

  7. [7]

    Dill, K.A., Ozkan, S.B., Shell, M.S., Weikl, T.R.: The protein folding problem. Annu. Rev. Biophys.37(1), 289–316 (2008)

    work page 2008

  8. [8]

    In: International Con- ference on Learning Representations, 2020 (2020)

    Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: International Con- ference on Learning Representations, 2020 (2020)

    work page 2020

  9. [9]

    Mamba: Linear-Time Sequence Modeling with Selective State Spaces

    Gu, A., Dao, T.: Mamba: Linear-time sequence modeling with selective state spaces (2024), https://arxiv.org/abs/2312.00752v2, arXiv.org. Accessed: Aug. 11

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  10. [10]

    In: The Twelfth International Conference on Learning Representations (2024), https://openreview.net/forum?id=kB4yBiNmXX

    Hatamizadeh, A., Heinrich, G., Yin, H., Tao, A., Alvarez, J.M., Kautz, J., Molchanov, P.: Fastervit: Fast vision transformers with hierarchical attention. In: The Twelfth International Conference on Learning Representations (2024), https://openreview.net/forum?id=kB4yBiNmXX

    work page 2024

  11. [11]

    He,K.,Zhang,X.,Ren,S.,Sun,J.:Deepresiduallearningforimagerecognition.In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)

    work page 2016

  12. [12]

    Accessed: Sep

    Jiang, J., Deng, F., Singh, G., Lee, M., Ahn, S.: Slot state space models (2024), https://arxiv.org/abs/2406.12272v5, arXiv.org. Accessed: Sep. 23

    work page arXiv 2024

  13. [13]

    Kim, M., Tak, J., Shin, J.: A deep learning model for wafer defect map classifica- tion: Perspective on classification performance and computational volume. Phys. Status Solidi (B)261(2024), article 2300113

    work page 2024

  14. [14]

    Advances in neural information processing systems2, 1097–1105 (2012)

    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep con- volutional neural networks. Advances in neural information processing systems2, 1097–1105 (2012)

    work page 2012

  15. [15]

    Li, J., Xia, X., Li, W., Li, H., Wang, X., Xiao, X., Wang, R., Zheng, M., Pan, X.: Next-vit: Next generation vision transformer for efficient deployment in realistic industrial scenarios (2022), arXiv preprint 2, 6

    work page 2022

  16. [16]

    Advances in Neural Information Processing Systems35, 12934–12949 (2022)

    Li, Y., Yuan, G., Wen, Y., Hu, J., Evangelidis, G., Tulyakov, S., Wang, Y., Ren, J.: Efficientformer: Vision transformers at mobilenet speed. Advances in Neural Information Processing Systems35, 12934–12949 (2022)

    work page 2022

  17. [17]

    Liu, Y., Tian, Y., Zhao, Y., Yu, H., Xie, L., Wang, Y., Ye, Q., Liu, Y.: Vmamba: Visual state space model (2024), arXiv preprint 3, 6

    work page 2024

  18. [18]

    Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer:Hierarchicalvisiontransformerusingshiftedwindows.In:Proceedings of the IEEE/CVF International Conference on Computer Vision, 2, 5, 6, 7. pp. 10012–10022 (2021) 14 Satwik Sahoo et al

    work page 2021

  19. [19]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2, 6, 7

    Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2, 6, 7. pp. 11976–11986 (2022)

    work page 2022

  20. [20]

    Expert Syst

    Manivannan,S.:Semi-supervisedimbalancedclassificationofwaferbinmapdefects using a dual-head cnn. Expert Syst. Appl.238, 122301 (2024)

    work page 2024

  21. [21]

    Nag, S., Makwana, D., Sai, C.T.R., Mittal, S., Mohan, C.K.: Wafersegclassnet- a light-weight network for classification and seg- mentation of semiconductor wafer defects. Comput. Ind.142(11 2022). https://doi.org/10.1016/j.compind. 2022.103720, art. no. 103720

    work page doi:10.1016/j.compind 2022

  22. [22]

    Patro, B.N., Agneeswaran, V.S.: Simba: Simplified mamba-based architecture for vision and multivariate time series (2024), arXiv preprint 3, 6

    work page 2024

  23. [23]

    Pei, X., Huang, T., Xu, C.: Efficientvmamba: Atrous selective scan for light weight visual mamba (2024), arXiv preprint 1, 3, 6

    work page 2024

  24. [24]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2, 6

    Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., Dollár, P.: Designing network design spaces. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2, 6. pp. 10428–10436 (2020)

    work page 2020

  25. [25]

    IEEE Trans

    Saqlain, M., Abbas, Q., Lee, J.Y.: A deep convolutional neural network for wafer defect identification on an imbalanced dataset in semiconductor manufacturing processes. IEEE Trans. Semicond. Manuf.33, 436–444 (2020)

    work page 2020

  26. [26]

    Computer science review1(1), 27–64 (2007)

    Schaeffer, S.E.: Graph clustering. Computer science review1(1), 27–64 (2007)

    work page 2007

  27. [27]

    Expert Syst

    Shim, J., Kang, S.: Learning from single-defect wafer maps to classify mixed-defect wafer maps. Expert Syst. Appl.233, 120923 (2023)

    work page 2023

  28. [28]

    Journal of Marine Science and Engineering12(8) (8 2024)

    Suo, Y., Ding, Z., Zhang, T.: The mamba model: A novel approach for predicting ship trajectories. Journal of Marine Science and Engineering12(8) (8 2024). https: //doi.org/10.3390/jmse12081321, art

    work page doi:10.3390/jmse12081321 2024

  29. [29]

    In: Interna- tional Conference on Machine Learning, PMLR

    Tan, M., Le, Q.: Efficientnetv2: Smaller models and faster training. In: Interna- tional Conference on Machine Learning, PMLR. pp. 10096–10106 (2021)

    work page 2021

  30. [30]

    In: International Conference on Machine Learning, PMLR

    Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H.: Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning, PMLR. pp. 10347–10357 (2021)

    work page 2021

  31. [31]

    IEEE Trans

    Wang, J., Xu, C., Yang, Z., Zhang, J., Li, X.: Deformable convolutional networks for efficient mixed-type wafer defect pattern recognition. IEEE Trans. Semicond. Manuf.33(4), 587–596 (11 2020). https://doi.org/10.1109/TSM.2020.3020985

    work page doi:10.1109/tsm.2020.3020985 2020

  32. [32]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision

    Wang, W., Xie, E., Li, X., Fan, D.P., Song, K., Liang, D., Lu, T., Luo, P., Shao, L.: Pyramid vision transformer: A versatile backbone for dense prediction with- out convolutions. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 568–578 (2021)

    work page 2021

  33. [33]

    IEEE Transactions on Semiconductor Manufacturing 35(2), 341–352 (3 2022)

    Wei, Y., Wang, H.: Mixed-type wafer defect recognition with multi-scale infor- mation fusion transformer. IEEE Transactions on Semiconductor Manufacturing 35(2), 341–352 (3 2022)

    work page 2022

  34. [34]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision

    Xu, W., Xu, Y., Chang, T., Tu, Z.: Coscale conv-attentional image transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9981–9990 (2021)

    work page 2021