Pith. sign in

REVIEW 5 major objections 6 minor 58 references

Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural Perspective

T0 review · 5 major / 6 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 3D-ADNAS claims that the topology of multimodal fusion, searched by differentiable NAS, is what drives 3D anomaly detection accuracy and efficiency, reporting 95.1 I-AUROC at 24.7 FPS and 269 MB memory.

desk verdict Useful empirical study of 3D-AD fusion architectures, but the NAS-specific claim is undercut by the ablation table and the causal story needs fair-budget baselines. read the letter →

arxiv 2412.17297 v1 pith:BU24PT7M submitted 2024-12-23 cs.CV

Kaifang Long , Guoyang Xie , Lianbo Ma , Jiaqi Liu , Zhichao Lu This is my paper · ORCID
classification cs.CV
keywords 3DanomalydetectionmultimodalfusionneuralarchitecturesearchtopologyRGB-Dindustrialfew-shotdifferentiable
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 asks whether the topology of multimodal fusion, rather than the fusion strategy alone, is what determines 3D anomaly detection performance, and answers yes. It studies two levels of architectural choice: intra-module fusion (which features and operations inside each early, middle, or late modality-specific module) and inter-module fusion (how those modules combine). After empirical and theoretical analysis, the paper builds 3D-ADNAS, a differentiable neural architecture search method that searches both levels at once. 3D-ADNAS reports 95.1 I-AUROC on MVTec 3D-AD with 24.7 frames per second and 269 MB memory, beating prior multimodal methods in accuracy while using far less compute. The central message is that a searched fusion architecture, not additional feature banks or large models, can be the main lever for accurate and efficient 3D anomaly detection.

What carries the argument

The central object is the Multimodal Fusion Network (MFN), composed of three modality-specific modules for early, middle, and late features, each represented as a small directed acyclic graph with a candidate feature pool and a candidate operation set (addition, concatenation, GLU, and guided attention). The search optimizes architectural parameters $\alpha_{\text{ex}}$ for feature selection, $\alpha_{\text{in}}$ for node connections, and $\beta_{\text{op}}$ for fusion operations, using the DARTS-style continuous relaxation and gradient descent. The mechanism is a two-level search space: at the intra-module level it decides what happens inside each fusion cell, and at the inter-module level it decides how module outputs are combined, with the early module's output becoming a candidate input for the middle and late modules and the middle module's output becoming a candidate input for the late module. Dempster-Shafer evidence theory is used to justify why combining an additional module opinion tends not to degrade and can improve the fused decision.

What would settle it

Retrain M3DM, AST, and CFM under 3D-ADNAS's exact input size, optimizer, epoch count, and total compute, and also train 3D-ADNAS with a randomly sampled fusion topology at equal compute; if the accuracy, frame rate, and memory gaps vanish or invert, the searched two-level topology is not the cause of the reported improvements.

Watch

Extended reading notes

Core claim

On the paper's own terms, multimodal fusion architecture design is a primary determinant of 3D anomaly detection performance. The proposed 3D-ADNAS formulates fusion as a two-level search problem: inside each modality-specific module it selects which features are fused and which fusion operations are used, and between modules it searches how early, middle, and late fusion cells feed into one another. The paper finds that middle feature fusion alone is better than early or late alone; late fusion alone hurts accuracy but helps when combined with other modules; selecting only the first two layers of middle features is better than using all middle features; and using multiple fusion operations, especially guided attention and weighted summation, beats using a single operation. With all three modules optimally combined, 3D-ADNAS achieves 95.1 I-AUROC on MVTec 3D-AD, 24.7 FPS, and 269 MB memory, improving on M3DM (94.5, 0.514 FPS, 6526 MB) and AST (93.7, 4.966 FPS, 464 MB).

Load-bearing premise

The paper attributes its gains to the searched fusion topology, but it compares against published baseline numbers that were not retrained under the same input size, training schedule, or search compute; if equal-budget retraining closes the gap, the central claim fails.

Editorial extensions

If this is right

  • If the searched two-level fusion topology is the cause of the gains, then 3D-ADNAS should beat state-of-the-art baselines on both MVTec 3D-AD and Eyecandies across I-AUROC, P-AUROC, and AUPRO metrics.
  • The reported efficiency gains mean a compact searched MFN can replace memory-bank-based fusion strategies: 24.7 FPS and 269 MB versus 0.5 FPS and 6526 MB for M3DM, while improving I-AUROC from 94.5 to 95.1.
  • Late fusion alone is harmful for 3D anomaly detection but consistently useful in combination with middle fusion, giving a concrete design rule for future multimodal 3D-AD architectures.
  • Selecting a subset of middle-layer features and multiple fusion operations per module, especially guided attention and weighted summation, outperforms single-operation and full-feature choices.
  • The method retains competitive accuracy under few-shot training (5, 10, or 50 training images), suggesting the searched topology transfers to low-data regimes.

Reading between the lines

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

  • Editorial inference: the comparison reuses published baseline numbers, so the accuracy and efficiency margins are only as strong as the assumption that those baselines were not handicapped by training settings; equal-budget retraining is the test that would confirm the topology is the cause.
  • Editorial inference: the same two-level fusion search idea could transfer to other multimodal industrial tasks, such as audio-visual or sensor fusion, where fusion topology is still hand-designed.
  • Editorial inference: a concrete testable prediction is that randomizing the searched topology while holding compute constant should reduce accuracy; if instead a random topology performs equally, then the search itself, not the found architecture, is not the explanatory factor.
  • Editorial inference: the theoretical support via Dempster-Shafer evidence theory suggests that uncertainty-aware fusion, not just feature concatenation, is worth exploring further in 3D anomaly detection.
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 / 6 minor

Summary. The paper studies how the topology of multimodal fusion networks affects 3D anomaly detection (3D-AD). It decomposes fusion architecture into an intra-module level (feature selection and fusion operations within early, middle, and late modality-specific modules) and an inter-module level (how these modules combine), and provides empirical ablations (Fig. 4, Table 4) and a Dempster-Shafer-based theoretical analysis (Propositions 1 and 2). Based on these insights, it proposes 3D-ADNAS, a differentiable two-level NAS method that searches the fusion cell operations, feature inputs, and inter-module connections. The searched architecture is evaluated on Eyecandies and MVTec 3D-AD, reporting mean I-AUROC improvements (e.g., 95.1 on MVTec 3D-AD), five-shot/ten-shot few-shot results, and improved frame rate and memory usage (Table 5).

Significance. If the claimed causal attribution is established, the paper makes a useful contribution: it is, to my knowledge, the first NAS treatment of multimodal fusion topology specifically for 3D-AD, and it introduces a clean two-level search space with a publicly linked implementation. The ablation analysis in Fig. 4 is informative, and the table of results on two standard benchmarks is encouraging. However, the current evidence does not separate the contribution of the searched topology from the contributions of added fusion modules, additional training budget, benchmark-driven search-space design, and removal of memory banks and language models. The central claim therefore needs stronger controlled experiments before the results can be taken as a proof that architecture design is the cause of the gains.

major comments (5)
  1. [Impact of MSM Fusion Components (Table 4)] The central claim that the searched topology is the cause of the improvements is not tested against a strong hand-designed fusion configuration. Table 4 shows that simply activating all three MSMs already yields mean I-AUROC 95.1 on MVTec 3D-AD, equal to the reported final result, and the paper's own Fig. 4-c identifies guided attention and weighted summation as the best operations. There is no reported configuration that combines these known-good operations with all three MSMs without the NAS search, nor a random-search or hand-crafted baseline from the same search space, trained under the same 600-epoch budget. Without such a control, the accuracy gain cannot be attributed to the architecture search rather than to the presence of the additional fusion modules or the 600-epoch training recipe.
  2. [Implementation Details; Tables 1–3 and 5] The comparisons with AST, M3DM, CFM, BTF, and ShapeGuided use numbers from the respective publications, with no evidence that baselines were retrained under the same input size (256×256), optimizer, number of epochs, or total compute budget. The proposed method consumes an 80-epoch DARTS search plus a 600-epoch full training, while published baselines may use different schedules; see 'Implementation Details'. The frame-rate and memory comparisons in Table 5 may also be from different hardware, since only the proposed method is reported as tested on a single NVIDIA RTX 4090. To make the claimed 'consistent improvements' attributable to the architecture, the paper should include budget-matched retraining of the main baselines or report FLOPs, parameter counts, and hardware-normalized timings.
  3. [Theoretical Analysis (Eq. (1)–(3), Propositions 1 and 2)] The theoretical support in Propositions 1 and 2 is conditional in a way that nearly assumes the conclusion. Proposition 1 requires b_g^m >= b_l^max, i.e., the added opinion already assigns to the ground-truth class a belief at least as large as the largest belief of the original opinion; under this assumption, the derived b_g^f >= b_g^l is unsurprising. Proposition 2's high-uncertainty case (u_m = 1) reduces to a statement about a vacuous opinion. Moreover, the DST analysis concerns classification opinions, whereas the MSMs in 3D-ADNAS fuse features in a reconstruction-based framework; the paper does not argue that the DST opinion model is the right abstraction for these features. The theoretical analysis should either be weakened to a formal analogy or extended with a non-trivial sufficient condition that is actually satisfied by the searched architectures.
  4. [Revisiting 3D-AD Fusion Architecture; Performance Evaluation] The search space is engineered from the same two benchmarks on which the final architecture is evaluated. The ablations in Fig. 4 that motivate the choice of candidate features, fusion operations, and the two-level space are conducted on Eyecandies and MVTec 3D-AD, and the final model is then evaluated on these same datasets. This creates a risk that the reported gains reflect benchmark-specific tuning of the search space rather than a generally superior fusion topology. A held-out 3D-AD dataset (e.g., the PD-REAL split or an unseen object category) would provide a cleaner test of the general claim.
  5. [Higher Frame Rate and Lower Memory Usage (Table 5)] The efficiency comparison conflates architectural choice with component omission. In the paragraph 'Higher Frame Rate and Lower Memory Usage', the paper states that the competitive performance of 3D-ADNAS benefits from using neither memory-bank-based strategies nor large pre-trained language models, and M3DM, the strongest memory consumer, does use both. Consequently, the lower memory and higher frame rate in Table 5 are partly a consequence of removing these components rather than of the searched fusion topology. A fair comparison would vary the fusion topology while holding the memory-bank and language-model components fixed, or explicitly report the ablation that isolates the topology's contribution to speed and memory.
minor comments (6)
  1. [Preliminaries] The paper states that each ablation test is repeated with multiple random seeds, but no variance or standard deviation is reported in Fig. 4 or Table 4; adding error bars or confidence intervals would strengthen the reliability of the architectural insights.
  2. [Introduction] The phrase 'servers as foundation framework' should read 'serves as'; this typo appears in the Introduction.
  3. [3D-ADNAS Method] In the inter-module fusion level paragraph, 'integrate these MSNs' should be 'integrate these MSMs'.
  4. [Table 5] The I-AUROC value reported for BTF in Table 5 is 86.5, whereas Table 3 lists BTF as 87.3; this discrepancy should be reconciled or explained.
  5. [Eq. (4) and Eq. (6)] The notation in the softmax weights is ambiguous: Eq. (4) uses 'αexi i' and Eq. (6) uses 'βop' without an index; please use indexed notation such as α_{s,i}^{ex} and β_{op,k} for clarity.
  6. [Tables 1–3 and Setup text] The paper alternates between 'Shape Guide' (Setup text) and 'ShapeGuided' (Table 3); the name should be standardized to match the cited paper.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's empirical comparisons are against external benchmarks and its theoretical claims are explicit conditionals, not conclusions built into their assumptions.

full rationale

The central claim that the searched two-level multimodal fusion topology improves 3D-AD is supported by direct evaluations on the public MVTec 3D-AD and Eyecandies benchmarks, with the reported accuracy, frame rate, and memory usage compared against published external baselines. The NAS search is conducted within a defined search space and the final model is then trained and evaluated; no fitted parameter is renamed as a prediction. The theoretical propositions in the 'Revisiting 3D-AD Fusion Architecture' section are explicit conditional inequalities: Proposition 1 assumes the added opinion already assigns at least as much belief to the ground-truth class as the original opinion's maximum, and then proves that fusion does not decrease that belief. That assumption is strong, and the paper's later informal statement that fusion 'has great potential to boost' accuracy goes beyond what the proposition alone guarantees, but this is a logical-import issue rather than circularity: the theorem does not define its conclusion into its premise, and the paper's main evidence is experimental. Self-citations, including the use of EasyNet as a backbone and the cited surveys, are contextual or comparative rather than load-bearing. Concerns about unequal training budgets, baseline recipes, and the lack of a controlled hand-designed-topology comparison under identical compute are experimental-design and attribution risks, not instances where the claimed derivation reduces by construction to its own inputs.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

No new physical or conceptual entities are postulated; the two-level search space and MSM cells are engineering constructions made of existing operations, not invented entities with independent falsifiable handles.

free parameters (2)
  • Architecture weights (alpha_ex, alpha_in, beta_op) = Discretized by argmax after DARTS search; values not reported
    These weights select features, cell connections, and fusion operations. They are optimized on the target datasets, and the final searched architecture is the paper's main output, so the claim depends on these fitted choices.
  • Search space design choices = Features: early/middle/late; operations: addition, concatenation, GLU, guided attention; K=2 intermediate nodes
    Hand-chosen based on the ablation insights in Figure 4 on the same benchmarks that are later used for final evaluation.
assumptions (3)
  • domain assumption Dempster-Shafer belief combination is an appropriate model of the proposed multimodal fusion operations
    Invoked in Theoretical Analysis, Eq. 1, to analyze early/middle/late MSMs. Actual fusion operations include concatenation, GLU, and guided attention, which are not belief-mass combination rules.
  • ad hoc to paper Proposition 1 condition b_g^m >= b_l^max
    The theorem that fusion improves accuracy requires the added modality's ground-truth belief to exceed the original modality's largest belief; this condition is not verified anywhere in the experiments and essentially assumes the improvement at issue.
  • domain assumption Continuous relaxation weights in DARTS faithfully predict the best discrete architecture after argmax discretization
    Equations 4 to 7 use alpha and beta as softmax-weighted mixtures, then select by argmax. This proxy assumption is a known limitation of DARTS and is not separately validated for 3D-AD.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural Perspective." pith.science (2026). https://pith.science/paper/BU24PT7M

@misc{pith2026241217297,
  author       = {Pith},
  title        = {Pith review of: Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural Perspective},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BU24PT7M}},
  note         = {Machine review of arXiv:2412.17297}
}
read the original abstract

Existing efforts to boost multimodal fusion of 3D anomaly detection (3D-AD) primarily concentrate on devising more effective multimodal fusion strategies. However, little attention was devoted to analyzing the role of multimodal fusion architecture (topology) design in contributing to 3D-AD. In this paper, we aim to bridge this gap and present a systematic study on the impact of multimodal fusion architecture design on 3D-AD. This work considers the multimodal fusion architecture design at the intra-module fusion level, i.e., independent modality-specific modules, involving early, middle or late multimodal features with specific fusion operations, and also at the inter-module fusion level, i.e., the strategies to fuse those modules. In both cases, we first derive insights through theoretically and experimentally exploring how architectural designs influence 3D-AD. Then, we extend SOTA neural architecture search (NAS) paradigm and propose 3D-ADNAS to simultaneously search across multimodal fusion strategies and modality-specific modules for the first time.Extensive experiments show that 3D-ADNAS obtains consistent improvements in 3D-AD across various model capacities in terms of accuracy, frame rate, and memory usage, and it exhibits great potential in dealing with few-shot 3D-AD tasks.

Figures

Figures reproduced from arXiv: 2412.17297 by the authors.

Figure 1
Figure 1. (Left) The impact of multimodal fusion architec￾ture design on 3D-AD performance. This shows the distribu￾tion of 3D-AD performance with variations at the intra- and inter-module fusion levels. (Right) 3D-ADNAS vs. SOTA methods in terms of accuracy, FPS, and memory usage. fusion network such that the integration of depth information will not interfere with color information (Gu et al. 2024a). Regarding to the multim… view at source ↗
Figure 2
Figure 2. The overall framework of 3D-ADNAS, where the MFN architecture design is the core of this work, which is specified [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The inner structure of an MSM, where the early [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: The impact of multimodal fusion architecture de [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Visualizations results on MVTec 3D-AD. fusion architectures in improving the 3D-AD performance. Evaluations on MVTec 3D-AD [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

58 extracted references · 51 canonical work pages

  1. [1]

    Baker, B.; Gupta, O.; Naik, N.; and Raskar, R. 2017. Designing Neural Network Architectures using Reinforcement Learning. In International Conference on Learning Representations

    work page 2017

  2. [2]

    Bergmann, P.; Fauser, M.; Sattlegger, D.; and Steger, C. 2019. MVTec AD - A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection. In Conference on CVPR, 9592--9600

    work page 2019

  3. [3]

    Bergmann, P.; Fauser, M.; Sattlegger, D.; and Steger, C. 2020. Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. In Proceedings of the IEEE/CVF conference on CVPR

    work page 2020

  4. [4]

    Bergmann, P.; Jin, X.; Sattlegger, D.; and Steger, C. 2022. The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 202--213

    work page 2022

  5. [5]

    Bergmann, P.; and Sattlegger, D. 2023. Anomaly detection in 3d point clouds using deep geometric descriptors. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2613--2623

    work page 2023

  6. [6]

    Bonfiglioli, L.; Toschi, M.; Silvestri, D.; Fioraio, N.; and De Gregorio, D. 2022. The eyecandies dataset for unsupervised multimodal anomaly detection and localization. In Proceedings of the Asian Conference on Computer Vision, 3586--3602

    work page 2022

  7. [7]

    Cao, Y.; Xu, X.; and Shen, W. 2024. Complementary pseudo multimodal feature for point cloud anomaly detection. Pattern Recognition, 110761

    work page 2024

  8. [8]

    Chen, R.; Xie, G.; Liu, J.; Wang, J.; Luo, Z.; Wang, J.; and Zheng, F. 2023. Easynet: An easy network for 3d industrial anomaly detection. In Proceedings of the 31st ACM International Conference on Multimedia, 7038--7046

    work page 2023

Show all 58 references
  1. [9]

    Chu, Y.-M.; Liu, C.; Hsieh, T.-I.; Chen, H.-T.; and Liu, T.-L. 2023. Shape-guided dual-memory learning for 3D anomaly detection. In Proceedings of the 40th International Conference on Machine Learning, 6185--6194

    2023

  2. [10]

    Cohen, N.; and Hoshen, Y. 2020. Sub-image anomaly detection with deep pyramid correspondences. arXiv preprint arXiv:2005.02357

    2020 arXiv

  3. [11]

    Z.; Lisanti, G.; and Di Stefano, L

    Costanzino, A.; Ramirez, P. Z.; Lisanti, G.; and Di Stefano, L. 2024. Multimodal industrial anomaly detection by crossmodal feature mapping. In Proceedings of the IEEE/CVF Conference on CVPR, 17234--17243

    2024

  4. [12]

    Dai, S.; Wu, Y.; Li, X.; and Xue, X. 2024. Generating and reweighting dense contrastive patterns for unsupervised anomaly detection. In Proceedings of the AAAI Conference on Artificial Intelligence, 1454--1462

    2024

  5. [13]

    Defard, T.; Setkov, A.; Loesch, A.; and Audigier, R. 2021. PaDiM: a patch distribution modeling framework for anomaly detection and localization. In International Conference on Pattern Recognition, 475--489

    2021

  6. [14]

    Deng, H.; and Li, X. 2022. Anomaly detection via reverse distillation from one-class embedding. In Proceedings of the IEEE/CVF conference on CVPR, 9737--9746

    2022

  7. [15]

    Duan, Y.; Hong, Y.; Niu, L.; and Zhang, L. 2023. Few-shot defect image generation via defect-aware feature manipulation. In Proceedings of the AAAI Conference on Artificial Intelligence, 571--578

    2023

  8. [16]

    Gu, Z.; Zhang, J.; Liu, L.; Chen, X.; Peng, J.; Gan, Z.; Jiang, G.; Shu, A.; Wang, Y.; and Ma, L. 2024 a . Rethinking Reverse Distillation for Multi-Modal Anomaly Detection. In Proceedings of the AAAI Conference on Artificial Intelligence, 8445--8453

    2024

  9. [17]

    Gu, Z.; Zhu, B.; Zhu, G.; Chen, Y.; Tang, M.; and Wang, J. 2024 b . Anomalygpt: Detecting industrial anomalies using large vision-language models. In Proceedings of the AAAI Conference on Artificial Intelligence, 1932--1940

    2024

  10. [18]

    Han, Z.; Zhang, C.; Fu, H.; and Zhou, J. T. 2021. Trusted Multi-View Classification. In International Conference on Learning Representations

    2021

  11. [19]

    Horwitz, E.; and Hoshen, Y. 2023. Back to the feature: classical 3d features are (almost) all you need for 3d anomaly detection. In Proceedings of the IEEE/CVF Conference on CVPR, 2968--2977

    2023

  12. [20]

    Hu, T.; Zhang, J.; Yi, R.; Du, Y.; Chen, X.; Liu, L.; Wang, Y.; and Wang, C. 2024. Anomalydiffusion: Few-shot anomaly image generation with diffusion model. In Proceedings of the AAAI Conference on Artificial Intelligence, 8526--8534

    2024

  13. [21]

    Jiang, X.; Liu, J.; Wang, J.; Nie, Q.; Wu, K.; Liu, Y.; Wang, C.; and Zheng, F. 2022. Softpatch: Unsupervised anomaly detection with noisy data. Advances in Neural Information Processing Systems, 35: 15433--15445

    2022

  14. [22]

    M.; and Park, S

    Kim, S.; An, S.; Chikontwe, P.; Kang, M.; Adeli, E.; Pohl, K. M.; and Park, S. H. 2024. Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection. In Proceedings of the AAAI Conference on Artificial Intelligence, 8591--8599

    2024

  15. [23]

    Lei, J.; Hu, X.; Wang, Y.; and Liu, D. 2023. Pyramidflow: High-resolution defect contrastive localization using pyramid normalizing flow. In Proceedings of the IEEE/CVF conference on CVPR, 14143--14152

    2023

  16. [24]

    Li, C.-L.; Sohn, K.; Yoon, J.; and Pfister, T. 2021. Cutpaste: Self-supervised learning for anomaly detection and localization. In Proceedings of the IEEE/CVF conference on CVPR, 9664--9674

    2021

  17. [25]

    Li, W.; Xu, X.; Gu, Y.; Zheng, B.; Gao, S.; and Wu, Y. 2024. Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network. In Proceedings of the IEEE/CVF Conference on CVPR, 22207--22216

    2024

  18. [26]

    Liu, H.; Simonyan, K.; and Yang, Y. 2018. DARTS: Differentiable Architecture Search. In International Conference on Learning Representations

    2018

  19. [27]

    Liu, J.; Xie, G.; Wang, J.; Li, S.; Wang, C.; Zheng, F.; and Jin, Y. 2024. Deep industrial image anomaly detection: A survey. Machine Intelligence Research, 21(1): 104--135

    2024

  20. [28]

    R.; Marathe, A

    Liu, Y.-T.; Pal, N. R.; Marathe, A. R.; and Lin, C.-T. 2017. Weighted fuzzy Dempster--Shafer framework for multimodal information integration. IEEE Transactions on Fuzzy Systems, 26(1): 338--352

    2017

  21. [29]

    Liu, Z.; Zhou, Y.; Xu, Y.; and Wang, Z. 2023. Simplenet: A simple network for image anomaly detection and localization. In Proceedings of the IEEE/CVF conference on CVPR, 20402--20411

    2023

  22. [30]

    Lv, J.; Sun, Y.; Ye, Q.; Feng, W.; and Lv, J. 2024. A multiscale neural architecture search framework for multimodal fusion. Information Sciences, 121005

    2024

  23. [31]

    P \'e rez-R \'u a, J.-M.; Vielzeuf, V.; Pateux, S.; Baccouche, M.; and Jurie, F. 2019. Mfas: Multimodal fusion architecture search. In Proceedings of the IEEE/CVF Conference on CVPR, 6966--6975

    2019

  24. [32]

    Qin, J.; Gu, C.; Yu, J.; and Zhang, C. 2023. Image-Pointcloud Fusion based Anomaly Detection using PD-REAL Dataset. arXiv preprint arXiv:2311.04095

    2023

  25. [33]

    Reiss, T.; Cohen, N.; Horwitz, E.; Abutbul, R.; and Hoshen, Y. 2022. Anomaly detection requires better representations. In European Conference on Computer Vision, 56--68

    2022

  26. [34]

    Rudolph, M.; Wandt, B.; and Rosenhahn, B. 2021. Same same but differnet: Semi-supervised defect detection with normalizing flows. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 1907--1916

    2021

  27. [35]

    Rudolph, M.; Wehrbein, T.; Rosenhahn, B.; and Wandt, B. 2022. Fully convolutional cross-scale-flows for image-based defect detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision

    2022

  28. [36]

    Rudolph, M.; Wehrbein, T.; Rosenhahn, B.; and Wandt, B. 2023. Asymmetric student-teacher networks for industrial anomaly detection. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, 2592--2602

    2023

  29. [37]

    M.; Tan, J.; Hou, B.; and Kainz, B

    Schl \"u ter, H. M.; Tan, J.; Hou, B.; and Kainz, B. 2022. Natural synthetic anomalies for self-supervised anomaly detection and localization. In European Conference on Computer Vision, 474--489

    2022

  30. [38]

    Sui, W.; Lichau, D.; Lef \`e vre, J.; and Phelippeau, H. 2024. Cross-Modal Distillation in Industrial Anomaly Detection: Exploring Efficient Multi-Modal IAD. arXiv preprint arXiv:2405.13571

    2024

  31. [39]

    D.; Nguyen, A

    Tien, T. D.; Nguyen, A. T.; Tran, N. H.; Huy, T. D.; Duong, S.; Nguyen, C. D. T.; and Truong, S. Q. 2023. Revisiting reverse distillation for anomaly detection. In Proceedings of the IEEE/CVF conference on CVPR, 24511--24520

    2023

  32. [40]

    Tu, Y.; Zhang, B.; Liu, L.; Li, Y.; Xu, C.; Zhang, J.; Wang, Y.; Wang, C.; and Zhao, C. R. 2024. Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection. arXiv preprint arXiv:2401.03145

    2024 arXiv

  33. [41]

    Wang, C.; Zhu, H.; Peng, J.; Wang, Y.; Yi, R.; Wu, Y.; Ma, L.; and Zhang, J. 2024 a . M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising. arXiv preprint arXiv:2406.02263

    2024 arXiv

  34. [42]

    Wang, J.; Wang, X.; Hao, R.; Yin, H.; Huang, B.; Xu, X.; and Liu, J. 2024 b . Incremental Template Neighborhood Matching for 3D anomaly detection. Neurocomputing, 581: 127483

    2024

  35. [43]

    Wang, Y.; Peng, J.; Zhang, J.; Yi, R.; Wang, Y.; and Wang, C. 2023. Multimodal industrial anomaly detection via hybrid fusion. In Proceedings of the IEEE/CVF Conference on CVPR, 8032--8041

    2023

  36. [44]

    Xie, G.; Wang, J.; Liu, J.; Lyu, J.; Liu, Y.; Wang, C.; Zheng, F.; and Jin, Y. 2024. Im-iad: Industrial image anomaly detection benchmark in manufacturing. IEEE Transactions on Cybernetics

    2024

  37. [45]

    R.; and Dai, A

    Xu, Z.; So, D. R.; and Dai, A. M. 2021. Mufasa: Multimodal fusion architecture search for electronic health records. In Proceedings of the AAAI Conference on Artificial Intelligence, 10532--10540

    2021

  38. [46]

    Yin, Y.; Huang, S.; and Zhang, X. 2022. Bm-nas: Bilevel multimodal neural architecture search. In Proceedings of the AAAI Conference on Artificial Intelligence, 8901--8909

    2022

  39. [47]

    You, Z.; Cui, L.; Shen, Y.; Yang, K.; Lu, X.; Zheng, Y.; and Le, X. 2022. A unified model for multi-class anomaly detection. Advances in Neural Information Processing Systems, 35: 4571--4584

    2022

  40. [48]

    Yu, Z.; Cui, Y.; Yu, J.; Wang, M.; Tao, D.; and Tian, Q. 2020. Deep multimodal neural architecture search. In Proceedings of the 28th ACM International Conference on Multimedia, 3743--3752

    2020

  41. [49]

    Zavrtanik, V.; Kristan, M.; and Sko c aj, D. 2021. Draem-a discriminatively trained reconstruction embedding for surface anomaly detection. In Proceedings of the IEEE/CVF international conference on computer vision, 8330--8339

    2021

  42. [50]

    Zavrtanik, V.; Kristan, M.; and Sko c aj, D. 2022. DSR--A Dual Subspace Re-Projection Network for Surface Anomaly Detection. In European Conference on Computer Vision, 539--554

    2022

  43. [51]

    Zavrtanik, V.; Kristan, M.; and Sko c aj, D. 2024 a . Cheating depth: Enhancing 3d surface anomaly detection via depth simulation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2164--2172

    2024

  44. [52]

    Zavrtanik, V.; Kristan, M.; and Sko c aj, D. 2024 b . Keep DR Ming: Discriminative 3D anomaly detection through anomaly simulation. Pattern Recognition Letters, 181: 113--119

    2024

  45. [53]

    Zhang, J.; and Li, W. 2023. Multi-Modal and Multi-Scale Temporal Fusion Architecture Search for Audio-Visual Video Parsing. In Proceedings of the 31st ACM International Conference on Multimedia, 3328--3336

    2023

  46. [54]

    Zhao, B.; Xiong, Q.; Zhang, X.; Guo, J.; Liu, Q.; Xing, X.; and Xu, X. 2024. PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Features. arXiv preprint arXiv:2403.01804

    2024 arXiv

  47. [55]

    Zhou, Z.; Wang, L.; Fang, N.; Wang, Z.; Qiu, L.; and Zhang, S. 2024. R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection. arXiv preprint arXiv:2407.10862

    2024 arXiv

  48. [56]

    Zoph, B.; Vasudevan, V.; Shlens, J.; and Le, Q. V. 2018. Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE conference on CVPR, 8697--8710

    2018

  49. [57]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all...

  50. [58]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

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