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REVIEW 3 major objections 5 minor 87 references

Learning Dynamic Local Context Representations for Infrared Small Target Detection

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read LCRNet claims that a 1.65M-parameter U-Net which learns dynamic local context representations outperforms 33 prior methods on three infrared small target detection benchmarks.

desk verdict Plausible and well-ablated efficiency play for ISTD, but the SOTA claim is only as good as Table I's unverified baseline protocol. read the letter →

arxiv 2412.17401 v1 pith:V7JDAKYD submitted 2024-12-23 cs.CV

Guoyi Zhang , Guangsheng Xu , Han Wang , Siyang Chen , Yunxiao Shan , Xiaohu Zhang This is my paper · ORCID
classification cs.CV
keywords infraredsmalltargetdetectiondynamiclocalcontextattentionlarge-kernelconvolutionU-Netmultigridmethodcoarse-to-finerepresentationSoft-IoUlossefficientnetwork
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 argues that infrared small target detection is best served by representations that are local and dynamically scaled, not by global attention or fixed small kernels. It proposes LCRNet, a U-Net with only 1.65M parameters, whose core block mixes a dynamic large-kernel attention mechanism (DLC-Attention) with a hierarchical convolution operator (HLKConv) to adapt the effective receptive field to each target's size and shape. On IRSTD-1k, SIRSTAUG, and NUDT-SIRST, the authors report that LCRNet achieves the best normalized IoU and false-alarm rate and near-best detection probability while using far less compute than most competing models. If the claim holds, the field's recent turn toward transformers and very large hybrid models may be unnecessary for this task; a compact, carefully structured convolutional network can carry it.

What carries the argument

The carrying mechanism is the C2FBlock, a residual unit that simulates a multigrid PDE solver: a DLC-Attention branch provides coarse, large-context refinement while a pre-activation BasicBlock provides fine, local refinement, and the two alternate under learnable LayerScale weights. DLC-Attention splits feature maps into groups, applies HLKConv at multiple downsampling scales, and fuses the branches with a residual split-attention that uses an r-softmax with a learnable temperature to allocate receptive field dynamically. HLKConv decomposes a large K×K kernel into a depthwise convolution, a dilated depthwise convolution, and a 1×1 convolution, but concatenates the two depthwise outputs rather than merely adding them, which preserves fine detail and avoids the block-like artifacts of dilated convolutions.

What would settle it

Run LCRNet and at least the three strongest baselines (SeRankDet, ABC, and IRSAM) on identical train/test splits with identical preprocessing, a fixed threshold of 0.5, and no data augmentation; if LCRNet does not lead on nIoU and Fa across IRSTD-1k, SIRSTAUG, and NUDT-SIRST, the central claim fails.

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Extended reading notes

Core claim

The central discovery is that two priors of infrared small target detection—the locality of small-target information and the need for context at a scale that varies with each target—can be operationalized in a single lightweight network, and that doing so outperforms global-attention and fixed-shape approaches. Concretely, the paper shows that LCRNet leads on nIoU and Fa across IRSTD-1k, SIRSTAUG, and NUDT-SIRST, and is second-best on Pd, while using 1.65M parameters and 59.3G FLOPs; prior state-of-the-art methods such as SeRankDet use roughly 66 times the parameters and 9.6 times the compute. The ablations attribute the gain to the specific components: replacing C2FBlock with a conventional MetaFormer-style block, DLC-Attention with LKA/LSKA/ConvMod/SAA, or HLKConv with plain large-kernel decompositions consistently lowers IoU, nIoU, and Pd. The authors also report that removing the nonlinear activation in the attention hurts performance most among the ablated choices, pointing to higher-order feature interactions as part of the mechanism.

Load-bearing premise

The comparison with the 33 prior methods assumes their published numbers were produced under the same protocol as LCRNet's—same data splits, input preprocessing, threshold of 0.5, and no data augmentation—since the paper does not re-run any baseline.

Editorial extensions

If this is right

  • A compact U-Net with dynamic local context attention can match or exceed much larger transformer and hybrid models on infrared small target detection, so accuracy does not have to be traded against efficiency.
  • The multigrid-inspired coarse-to-fine iteration is a viable replacement for FFN or MLP blocks in fine-grained vision tasks, improving detection without adding parameters.
  • Large-kernel attention can be built with sparse hierarchical convolutions that avoid dilated-convolution artifacts, making large receptive fields usable for small-object tasks.
  • The same architecture adapts to datasets dominated by targets under ten pixels (SIRSTAUG) and to cross-scale datasets like NUDT-SIRST without shape-bias priors or data augmentation.
  • The fixed threshold of 0.5 and the lack of data augmentation mean the reported gains come from the model itself rather than from evaluation tricks.

Reading between the lines

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

  • A testable extension the paper leaves implicit: swapping DLC-Attention for a fixed large-kernel attention of matched cost on the same benchmarks would isolate whether dynamic scale allocation, rather than large context alone, drives the gain.
  • The ablation shows that removing the nonlinear activation hurts the most, which suggests part of the benefit may come from higher-order feature interactions rather than from the attention mechanism per se; a gated linear unit could be compared directly.
  • The paper notes a speed limitation of 20.3 FPS on an RTX 3080ti even with low FLOPs, due to memory-bound depthwise convolutions; an optimized CUDA implementation could substantially change the deployment comparison.
  • Because no baseline is re-run, a fair-comparison test is to re-evaluate the top baselines under LCRNet's exact protocol; if rankings shift under identical preprocessing and thresholds, the SOTA claim would need revisiting.
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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 / 5 minor

Summary. The paper proposes LCRNet, a U-Net-style network for infrared small target detection, built from three components: C2FBlock (a residual block inspired by multigrid PDE solvers), DLC-Attention (a grouped large-kernel attention mechanism with multi-scale aggregation and split attention), and HLKConv (a hierarchical decomposed large-kernel convolution). The authors report that LCRNet, with 1.65M parameters and 59.3G FLOPs, achieves state-of-the-art nIoU and Fa on IRSTD-1k, SIRSTAUG, and NUDT-SIRST, outperforming 33 prior methods, and they provide ablation studies on IRSTD-1k. The central claim is that a lightweight dynamic-local-context U-Net can surpass much heavier transformer- and SAM-based detectors on these benchmarks.

Significance. If the empirical results are reproducible, the paper makes a useful contribution: it demonstrates that a carefully designed large-kernel attention U-Net can achieve competitive or superior ISTD accuracy at a fraction of the parameter/FLOP budget of recent transformer and foundation-model baselines. The component-wise ablations (Tables II) are reasonably thorough and the design is clearly described. However, the paper does not provide code, data splits, or error bars, and the model configuration is selected and evaluated on the same benchmark, so the headline SOTA claim is currently a verification gap rather than a fully established result. The efficiency claim is supported only by FLOPs/Params and a single self-reported FPS number, not by a head-to-head speed comparison.

major comments (3)
  1. [Section IV-A and Section IV-B, Table I] The comparability of the 33 baseline numbers in Table I is not established. The paper does not state which train/test splits are used for IRSTD-1k, SIRSTAUG, or NUDT-SIRST, and it does not say whether the baseline results were re-run under the authors' protocol or transcribed from the original papers. Since the paper applies a fixed threshold of 0.5 and a specific normalization (division by 255), and since these datasets are commonly evaluated under differing split and preprocessing conventions, published baseline numbers may not be directly comparable. Several reported margins are small (e.g., SIRSTAUG nIoU 71.93 vs. ABC 71.83; IRSTD-1k Fa 1.8 vs. RPCANet 4.39), so protocol drift could change the ranking. The authors should specify the exact splits, describe the evaluation protocol for object-level Pd/Fa, and either re-run baselines under a common protocol or justify why published numbers are directly comparable.
  2. [Section IV-C, Table II] The hyperparameters of the final model (layer depths Li, group count K, radix r, HLKConv kernel size and dilation, LayerScale) are selected by ablation experiments on IRSTD-1k, and the same IRSTD-1k test set is then used to report the final results in Table I. This selection-on-the-test-set procedure makes the reported performance optimistically biased, especially because the paper reports a single run with no error bars. The authors should either use a held-out validation split for model selection, or report multiple runs with different seeds as mean±std for the final configuration and for the key ablations. Without this, it is unclear how much of the claimed advantage over baselines survives when the configuration is fixed before seeing the test set.
  3. [Section IV-D and Table I] The paper's efficiency claim rests predominantly on parameter count and FLOPs, but FLOPs are not the same as deployment-relevant speed. The only measured speed, 20.3 FPS in Section IV-D, is reported for LCRNet on a single GPU with no comparison to any of the 33 baselines under the same hardware, batch size, and resolution. Since the paper itself notes that DWConv operations have a low computation-to-memory-access ratio and that PyTorch's DWConv is not fully optimized, the efficiency advantage over existing methods should be demonstrated with an inference-speed comparison on identical hardware and software settings, at least for representative baselines.
minor comments (5)
  1. [Section III-B, Eq. (2)] Equation (2) applies pre-BasicBlock twice, i.e., pre-BasicBlock(pre-BasicBlock(X')), which may be intentional but is not explained in the text. Please clarify whether this is a typo or a deliberate double application, and how it relates to the multigrid analogy.
  2. [Table I] Several cells in Table I are left blank (e.g., MDvsFA, ALCNet, FC3-Net and others on SIRSTAUG, and RPCANet nIoU is missing). The paper does not explain whether the baseline was not evaluated on that dataset or whether the result is unavailable from the source. Please add a note explaining the missing entries.
  3. [Table II] In the C2FBlock row for Li={2,2,2,2}, the IoU is reported as 72.67 (-0.00), identical to the full LCRNet IoU, while nIoU and Pd change. This is suspicious and likely a typo; please verify the reported value and the delta.
  4. [Figures 1 and 9] The rendered text in Figures 1 and 9 appears corrupted (e.g., axis labels and legend entries contain unreadable glyphs). Please regenerate these figures with clean, legible fonts.
  5. [Section I and Section II-B] The paper repeatedly uses the phrase "for the first time" for the proposed components (e.g., "the first approach in this field to explore the dynamic construction..."). These claims are difficult to verify and are not essential to the technical contribution; consider removing them or tempering the wording.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: LCRNet is an empirical architecture paper; its benchmark claims rely on external comparisons and ablations, not on a derivation equivalent to its inputs.

full rationale

The paper contains no claimed first-principles derivation whose output could coincide with its input by construction. The method sections build on external prior art (MetaFormer [59], [60]; VAN large-kernel decomposition [27]; split attention [73]; pre-act ResBlock [63]; multigrid analogy [26], [64]; Soft-IoU [80]), and the authors evaluate the resulting architecture against 33 external methods and component ablations. The only clear author-overlap reference, [1], supports the introductory statement that ISTD is vital for infrared search and tracking systems; it is not load-bearing for the architecture or the SOTA claim. The Table I comparison is an empirical benchmark compilation rather than a derivation, so protocol/split comparability concerns are verification risks, not circularity. The hyperparameter tuning described in Section IV-C is performed on IRSTD-1k and the final configuration is then reported on IRSTD-1k in Table I; this is a possible selection-on-test-set caveat, but it does not make the reported performance equivalent to the fitted hyperparameters by construction, and it is not a fitted parameter relabeled as a prediction. Section II-A also states that "our goal is not to achieve state-of-the-art performance," which conflicts with the abstract's SOTA claim, but this is a rhetorical inconsistency rather than a circular reduction. No equation in the paper reduces to itself, and no load-bearing premise is imported from the authors' own prior work. Under the hard-rule standard requiring a quoted reduction or a self-citation chain that forces the result, no circular step can be identified.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

LCRNet is an empirical architecture paper, so the ledger is dominated by hand-chosen hyperparameters and evaluation assumptions rather than derivation constants. The main circularity risk is that several hyperparameters were tuned on IRSTD-1k before the final numbers on that same dataset were reported.

free parameters (6)
  • C2FBlock layer depths L_i = {3,3,3,3}
    Tuned on IRSTD-1k in Section IV-C1; ablation shows this configuration best.
  • Number of feature groups K in DLC-Attention = 4
    Ablation in Section IV-C3 shows K=4 beats K=1 and K=2.
  • Radix r in split attention = 4
    Set based on empirical evidence from prior work [72]; not ablated in this paper.
  • HLKConv kernel size K and dilation d = 7x7 with d=2
    Ablation in Section IV-C3 shows 7x7 outperforms 11x11; d=2 follows the decomposition rule.
  • Channel widths C1, C2, C3, C4 = 16, 32, 64, 64
    Chosen by hand in Section III-A; no ablation reported.
  • Training schedule = Adan, lr 1e-3, 400 epochs, batch 8
    Standard settings listed in Section IV-A3; not thoroughly ablated.
assumptions (4)
  • domain assumption The two ISTD priors (locality and dynamic context) are valid and motivate the architecture.
    Stated in Section I with Fig. 2; the paper does not prove these are necessary or sufficient.
  • standard math Large-kernel decomposition rule (DWConv, DWDConv, PWConv) is a valid reparameterization of a standard KxK convolution.
    Taken from VAN [27] and used in Section III-D for HLKConv.
  • domain assumption Soft-IoU loss is an appropriate training objective for ISTD.
    Adopted from [80] in Section III-E; not ablated against other losses.
  • domain assumption Reported metrics for 33 comparison methods are reliable and comparable.
    Table I relies on reported metrics from various papers without re-running baselines; any inconsistency affects the SOTA claim.

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Cite this review

Pith. "Pith review of Learning Dynamic Local Context Representations for Infrared Small Target Detection." pith.science (2026). https://pith.science/paper/V7JDAKYD

@misc{pith2026241217401,
  author       = {Pith},
  title        = {Pith review of: Learning Dynamic Local Context Representations for Infrared Small Target Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/V7JDAKYD}},
  note         = {Machine review of arXiv:2412.17401}
}
read the original abstract

Infrared small target detection (ISTD) is challenging due to complex backgrounds, low signal-to-clutter ratios, and varying target sizes and shapes. Effective detection relies on capturing local contextual information at the appropriate scale. However, small-kernel CNNs have limited receptive fields, leading to false alarms, while transformer models, with global receptive fields, often treat small targets as noise, resulting in miss-detections. Hybrid models struggle to bridge the semantic gap between CNNs and transformers, causing high complexity.To address these challenges, we propose LCRNet, a novel method that learns dynamic local context representations for ISTD. The model consists of three components: (1) C2FBlock, inspired by PDE solvers, for efficient small target information capture; (2) DLC-Attention, a large-kernel attention mechanism that dynamically builds context and reduces feature redundancy; and (3) HLKConv, a hierarchical convolution operator based on large-kernel decomposition that preserves sparsity and mitigates the drawbacks of dilated convolutions. Despite its simplicity, with only 1.65M parameters, LCRNet achieves state-of-the-art (SOTA) performance.Experiments on multiple datasets, comparing LCRNet with 33 SOTA methods, demonstrate its superior performance and efficiency.

Figures

Figures reproduced from arXiv: 2412.17401 by the authors.

Figure 1
Figure 1. Comparison of the proposed LCRNet with other data-driven methods [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Two key priors for infrared small target detection. (a) Small target [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. The overall architecture of LCRNet follows the typical U-Net structure [ [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: The overall architecture of the proposed Coarse-to-fine Convolution [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Overall architecture of the proposed attention. For simplicity, we show proposed attention in cardinality-major view (the featuremap groups with same [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: The multi-scale aggregation in each group (for [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 7
Figure 7. Figure 7: The overall architecture of the proposed HLKConv. It is based on the [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 9
Figure 9. Figure 9: ROC curves of our LCRNet and other approaches on IRSTD-1k. [PITH_FULL_IMAGE:figures/full_fig_p009_9.png]
Figure 10
Figure 10. Figure 10: Visualization comparison of detection results via different methods on [PITH_FULL_IMAGE:figures/full_fig_p009_10.png]

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Works this paper leans on

87 extracted references · 49 canonical work pages

  1. [1]

    A feature enhancement and augmentation-based infrared small target detection network,

    S. Chen, H. Wang, Z. Shen, G. Zhang, C. Ning, and X. Zhang, “A feature enhancement and augmentation-based infrared small target detection network,” IEEE Geoscience and Remote Sensing Letters , vol. 21, pp. 1–5, 2024. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 11

    2024

  2. [2]

    Single-frame infrared small-target detection: A survey,

    M. Zhao, W. Li, L. Li, J. Hu, P. Ma, and R. Tao, “Single-frame infrared small-target detection: A survey,” IEEE Geoscience and Remote Sensing Magazine, vol. 10, no. 2, pp. 87–119, 2022

    2022

  3. [3]

    Infrared techniques for military applications,

    R. N. Strickland, “Infrared techniques for military applications,” in Infrared Methodology and Technology . CRC Press, 2023, pp. 397–427

    2023

  4. [4]

    Spatial- temporal tensor ring norm regularization for infrared small target detection,

    H. Yi, C. Yang, R. Qie, J. Liao, F. Wu, T. Pu, and Z. Peng, “Spatial- temporal tensor ring norm regularization for infrared small target detection,” IEEE Geoscience and Remote Sensing Letters , vol. 20, pp. 1–5, 2023

    2023

  5. [5]

    Pick of the bunch: Detecting infrared small targets beyond hit-miss trade-offs via selective rank-aware attention,

    Y . Dai, P. Pan, Y . Qian, Y . Li, X. Li, J. Yang, and H. Wang, “Pick of the bunch: Detecting infrared small targets beyond hit-miss trade-offs via selective rank-aware attention,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–15, 2024

    2024

  6. [6]

    Infrared small and dim target detection with transformer under complex backgrounds,

    F. Liu, C. Gao, F. Chen, D. Meng, W. Zuo, and X. Gao, “Infrared small and dim target detection with transformer under complex backgrounds,” IEEE Transactions on Image Processing , vol. 32, pp. 5921–5932, 2023

    2023

  7. [7]

    Isnet: Shape matters for infrared small target detection,

    M. Zhang, R. Zhang, Y . Yang, H. Bai, J. Zhang, and J. Guo, “Isnet: Shape matters for infrared small target detection,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 867–876

    2022

  8. [8]

    Learning shape-biased representations for infrared small target detection,

    F. Lin, S. Ge, K. Bao, C. Yan, and D. Zeng, “Learning shape-biased representations for infrared small target detection,” IEEE Transactions on Multimedia, vol. 26, pp. 4681–4692, 2024

    2024

Show all 87 references
  1. [9]

    Learning contrast-enhanced shape-biased representations for infrared small target detection,

    F. Lin, K. Bao, Y . Li, D. Zeng, and S. Ge, “Learning contrast-enhanced shape-biased representations for infrared small target detection,” IEEE Transactions on Image Processing , vol. 33, pp. 3047–3058, 2024

    2024

  2. [10]

    Local patch network with global attention for infrared small target detection,

    F. Chen, C. Gao, F. Liu, Y . Zhao, Y . Zhou, D. Meng, and W. Zuo, “Local patch network with global attention for infrared small target detection,” IEEE Transactions on Aerospace and Electronic Systems , vol. 58, no. 5, pp. 3979–3991, 2022

    2022

  3. [11]

    Infrared small target detection with scale and location sensitivity,

    Q. Liu, R. Liu, B. Zheng, H. Wang, and Y . Fu, “Infrared small target detection with scale and location sensitivity,” in Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition , 2024

    2024

  4. [12]

    Dense nested attention network for infrared small target detection,

    B. Li, C. Xiao, L. Wang, Y . Wang, Z. Lin, M. Li, W. An, and Y . Guo, “Dense nested attention network for infrared small target detection,” IEEE Transactions on Image Processing , vol. 32, pp. 1745–1758, 2023

    2023

  5. [13]

    Uiu-net: U-net in u-net for infrared small object detection,

    X. Wu, D. Hong, and J. Chanussot, “Uiu-net: U-net in u-net for infrared small object detection,” IEEE Transactions on Image Processing, vol. 32, pp. 364–376, 2023

    2023

  6. [14]

    Attentional local contrast networks for infrared small target detection,

    Y . Dai, Y . Wu, F. Zhou, and K. Barnard, “Attentional local contrast networks for infrared small target detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 59, no. 11, pp. 9813–9824, 2021

    2021

  7. [15]

    Asymmetric contextual modulation for infrared small target detection,

    ——, “Asymmetric contextual modulation for infrared small target detection,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , January 2021, pp. 950–959

    2021

  8. [16]

    Receptive-field and direction induced attention network for infrared dim small target detection with a large-scale dataset irdst,

    H. Sun, J. Bai, F. Yang, and X. Bai, “Receptive-field and direction induced attention network for infrared dim small target detection with a large-scale dataset irdst,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–13, 2023

    2023

  9. [17]

    Mtu-net: Multilevel transunet for space-based infrared tiny ship detection,

    T. Wu, B. Li, Y . Luo, Y . Wang, C. Xiao, T. Liu, J. Yang, W. An, and Y . Guo, “Mtu-net: Multilevel transunet for space-based infrared tiny ship detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–15, 2023

    2023

  10. [18]

    Abc: Attention with bilinear correlation for infrared small target detection,

    P. Pan, H. Wang, C. Wang, and C. Nie, “Abc: Attention with bilinear correlation for infrared small target detection,” in 2023 IEEE International Conference on Multimedia and Expo (ICME) , 2023, pp. 2381–2386

    2023

  11. [19]

    Sctransnet: Spatial- channel cross transformer network for infrared small target detection,

    S. Yuan, H. Qin, X. Yan, N. Akhtar, and A. Mian, “Sctransnet: Spatial- channel cross transformer network for infrared small target detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–15, 2024

    2024

  12. [20]

    Irsam: Advancing segment anything model for infrared small target detection,

    M. Zhang, Y . Wang, J. Guo, Y . Li, X. Gao, and J. Zhang, “Irsam: Advancing segment anything model for infrared small target detection,” in Computer Vision – ECCV 2024 , A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, and G. Varol, Eds. Cham: Springer Nature Switz...

    2024

  13. [21]

    Segment anything,

    A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y . Lo, P. Dollár, and R. Girshick, “Segment anything,” in 2023 IEEE/CVF International Conference on Computer Vision (ICCV) , 2023, pp. 3992–4003

    2023

  14. [22]

    A robust infrared small target detection algorithm based on human visual system,

    J. Han, Y . Ma, B. Zhou, F. Fan, K. Liang, and Y . Fang, “A robust infrared small target detection algorithm based on human visual system,” IEEE Geoscience and Remote Sensing Letters , vol. 11, no. 12, pp. 2168–2172, 2014

    2014

  15. [23]

    Infrared patch-image model for small target detection in a single image,

    C. Gao, D. Meng, Y . Yang, Y . Wang, X. Zhou, and A. G. Hauptmann, “Infrared patch-image model for small target detection in a single image,” IEEE Transactions on Image Processing , vol. 22, no. 12, pp. 4996–5009, 2013

    2013

  16. [24]

    Understanding the effective receptive field in deep convolutional neural networks,

    W. Luo, Y . Li, R. Urtasun, and R. Zemel, “Understanding the effective receptive field in deep convolutional neural networks,”Advances in neural information processing systems , vol. 29, 2016

    2016

  17. [25]

    Ode-inspired network design for single image super-resolution,

    X. He, Z. Mo, P. Wang, Y . Liu, M. Yang, and J. Cheng, “Ode-inspired network design for single image super-resolution,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 1732–1741

    2019

  18. [26]

    Fast multi-grid methods for minimizing curvature energies,

    Z. Zhang, K. Chen, K. Tang, and Y . Duan, “Fast multi-grid methods for minimizing curvature energies,” IEEE Transactions on Image Processing, vol. 32, pp. 1716–1731, 2023

    2023

  19. [27]

    Visual attention network,

    M.-H. Guo, C.-Z. Lu, Z.-N. Liu, M.-M. Cheng, and S.-M. Hu, “Visual attention network,” Computational Visual Media , vol. 9, no. 4, pp. 733– 752, 2023

    2023

  20. [28]

    Analysis of new top-hat transformation and the application for infrared dim small target detection,

    X. Bai and F. Zhou, “Analysis of new top-hat transformation and the application for infrared dim small target detection,” Pattern Recognition, vol. 43, no. 6, pp. 2145–2156, 2010

    2010

  21. [29]

    Multiscale patch-based contrast measure for small infrared target detection,

    Y . Wei, X. You, and H. Li, “Multiscale patch-based contrast measure for small infrared target detection,” Pattern Recognition, vol. 58, pp. 216–226, 2016

    2016

  22. [30]

    Small infrared target detection based on weighted local difference measure,

    H. Deng, X. Sun, M. Liu, C. Ye, and X. Zhou, “Small infrared target detection based on weighted local difference measure,”IEEE Transactions on Geoscience and Remote Sensing , vol. 54, no. 7, pp. 4204–4214, 2016

    2016

  23. [31]

    Infrared small target detection utilizing the multiscale relative local contrast measure,

    J. Han, K. Liang, B. Zhou, X. Zhu, J. Zhao, and L. Zhao, “Infrared small target detection utilizing the multiscale relative local contrast measure,” IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 4, pp. 612–616, 2018

    2018

  24. [32]

    Infrared small target detection based on facet kernel and random walker,

    Y . Qin, L. Bruzzone, C. Gao, and B. Li, “Infrared small target detection based on facet kernel and random walker,” IEEE Transactions on Geoscience and Remote Sensing , vol. 57, no. 9, pp. 7104–7118, 2019

    2019

  25. [33]

    A local contrast method combined with adaptive background estimation for infrared small target detection,

    J. Han, S. Liu, G. Qin, Q. Zhao, H. Zhang, and N. Li, “A local contrast method combined with adaptive background estimation for infrared small target detection,” IEEE Geoscience and Remote Sensing Letters , vol. 16, no. 9, pp. 1442–1446, 2019

    2019

  26. [34]

    Global sparsity- weighted local contrast measure for infrared small target detection,

    Z. Qiu, Y . Ma, F. Fan, J. Huang, and L. Wu, “Global sparsity- weighted local contrast measure for infrared small target detection,” IEEE Geoscience and Remote Sensing Letters , vol. 19, pp. 1–5, 2022

    2022

  27. [35]

    Non-negative infrared patch-image model: Robust target-background separation via partial sum minimization of singular values,

    Y . Dai, Y . Wu, Y . Song, and J. Guo, “Non-negative infrared patch-image model: Robust target-background separation via partial sum minimization of singular values,” Infrared Physics & Technology, vol. 81, pp. 182–194, 2017

    2017

  28. [36]

    Reweighted infrared patch-tensor model with both nonlocal and local priors for single-frame small target detection,

    Y . Dai and Y . Wu, “Reweighted infrared patch-tensor model with both nonlocal and local priors for single-frame small target detection,” IEEE journal of selected topics in applied earth observations and remote sensing, vol. 10, no. 8, pp. 3752–3767, 2017

    2017

  29. [37]

    Infrared small target detection via non-convex rank approximation minimization joint ℓ2,1 norm,

    L. Zhang, L. Peng, T. Zhang, S. Cao, and Z. Peng, “Infrared small target detection via non-convex rank approximation minimization joint ℓ2,1 norm,” Remote Sensing, vol. 10, no. 11, p. 1821, 2018

    2018

  30. [38]

    Infrared small target detection based on non-convex optimization with lp-norm constraint,

    T. Zhang, H. Wu, Y . Liu, L. Peng, C. Yang, and Z. Peng, “Infrared small target detection based on non-convex optimization with lp-norm constraint,” Remote Sensing, vol. 11, no. 5, p. 559, 2019

    2019

  31. [39]

    Infrared small target detection based on partial sum of the tensor nuclear norm,

    L. Zhang and Z. Peng, “Infrared small target detection based on partial sum of the tensor nuclear norm,” Remote Sensing, vol. 11, no. 4, p. 382, 2019

    2019

  32. [40]

    Attention-guided pyramid context networks for detecting infrared small target under complex background,

    T. Zhang, L. Li, S. Cao, T. Pu, and Z. Peng, “Attention-guided pyramid context networks for detecting infrared small target under complex background,” IEEE Transactions on Aerospace and Electronic Systems , vol. 59, no. 4, pp. 4250–4261, 2023

    2023

  33. [41]

    Ilnet: Low-level matters for salient infrared small target detection,

    H. Li, J. Yang, R. Wang, and Y . Xu, “Ilnet: Low-level matters for salient infrared small target detection,” arXiv preprint arXiv:2309.13646 , 2023

    2023 arXiv

  34. [42]

    Are large kernels better teachers than transformers for convnets?

    T. Huang, L. Yin, Z. Zhang, L. Shen, M. Fang, M. Pechenizkiy, Z. Wang, and S. Liu, “Are large kernels better teachers than transformers for convnets?” in International Conference on Machine Learning . PMLR, 2023, pp. 14 023–14 038

    2023

  35. [43]

    Rpcanet: Deep unfolding rpca based infrared small target detection,

    F. Wu, T. Zhang, L. Li, Y . Huang, and Z. Peng, “Rpcanet: Deep unfolding rpca based infrared small target detection,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), January 2024, pp. 4809–4818

    2024

  36. [44]

    Scaling up your kernels to 31x31: Revisiting large kernel design in cnns,

    X. Ding, X. Zhang, J. Han, and G. Ding, “Scaling up your kernels to 31x31: Revisiting large kernel design in cnns,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 11 963–11 975

    2022

  37. [45]

    Unireplknet: A universal perception large-kernel convnet for audio video point cloud time-series and image recognition,

    X. Ding, Y . Zhang, Y . Ge, S. Zhao, L. Song, X. Yue, and Y . Shan, “Unireplknet: A universal perception large-kernel convnet for audio video point cloud time-series and image recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , ...

    2024

  38. [46]

    Conv2former: A simple transformer-style convnet for visual recognition,

    Q. Hou, C.-Z. Lu, M.-M. Cheng, and J. Feng, “Conv2former: A simple transformer-style convnet for visual recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024

    2024

  39. [47]

    Moganet: Multi-order gated aggregation network,

    S. Li, Z. Wang, Z. Liu, C. Tan, H. Lin, D. Wu, Z. Chen, J. Zheng, and S. Z. Li, “Moganet: Multi-order gated aggregation network,” in The Twelfth International Conference on Learning Representations , 2023

    2023

  40. [48]

    Wavelet convolutions for large receptive fields,

    S. E. Finder, R. Amoyal, E. Treister, and O. Freifeld, “Wavelet convolutions for large receptive fields,” in European Conference on Computer Vision. Springer, 2025, pp. 363–380

    2025

  41. [49]

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

    K. W. Lau, L.-M. Po, and Y . A. U. Rehman, “Large separable kernel attention: Rethinking the large kernel attention design in cnn,” Expert Systems with Applications , vol. 236, p. 121352, 2024

    2024

  42. [50]

    Pelk: Parameter- efficient large kernel convnets with peripheral convolution,

    H. Chen, X. Chu, Y . Ren, X. Zhao, and K. Huang, “Pelk: Parameter- efficient large kernel convnets with peripheral convolution,” in Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 5557–5567

    2024

  43. [51]

    Segnext: Rethinking convolutional attention design for semantic segmentation,

    M.-H. Guo, C.-Z. Lu, Q. Hou, Z. Liu, M.-M. Cheng, and S.-M. Hu, “Segnext: Rethinking convolutional attention design for semantic segmentation,” Advances in Neural Information Processing Systems , vol. 35, pp. 1140–1156, 2022

    2022

  44. [52]

    Lsknet: A foundation lightweight backbone for remote sensing,

    Y . Li, X. Li, Y . Dai, Q. Hou, L. Liu, Y . Liu, M.-M. Cheng, and J. Yang, “Lsknet: A foundation lightweight backbone for remote sensing,” International Journal of Computer Vision , pp. 1–22, 2024

    2024

  45. [53]

    Large selective kernel network for remote sensing object detection,

    Y . Li, Q. Hou, Z. Zheng, M.-M. Cheng, J. Yang, and X. Li, “Large selective kernel network for remote sensing object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 16 794–16 805

    2023

  46. [54]

    Beyond self-attention: Deformable large kernel attention for medical image segmentation,

    R. Azad, L. Niggemeier, M. Hüttemann, A. Kazerouni, E. K. Aghdam, Y . Velichko, U. Bagci, and D. Merhof, “Beyond self-attention: Deformable large kernel attention for medical image segmentation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Visi...

    2024

  47. [55]

    Hornet: Efficient high-order spatial interactions with recursive gated convolutions,

    Y . Rao, W. Zhao, Y . Tang, J. Zhou, S. N. Lim, and J. Lu, “Hornet: Efficient high-order spatial interactions with recursive gated convolutions,” Advances in Neural Information Processing Systems , vol. 35, pp. 10 353– 10 366, 2022

    2022

  48. [56]

    Denoising diffusion probabilistic models,

    J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840– 6851, 2020

    2020

  49. [57]

    Improved denoising diffusion probabilistic models,

    A. Q. Nichol and P. Dhariwal, “Improved denoising diffusion probabilistic models,” in International conference on machine learning. PMLR, 2021, pp. 8162–8171

    2021

  50. [58]

    Repaint: Inpainting using denoising diffusion probabilistic models,

    A. Lugmayr, M. Danelljan, A. Romero, F. Yu, R. Timofte, and L. Van Gool, “Repaint: Inpainting using denoising diffusion probabilistic models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 11 461–11 471

    2022

  51. [59]

    Metaformer is actually what you need for vision,

    W. Yu, M. Luo, P. Zhou, C. Si, Y . Zhou, X. Wang, J. Feng, and S. Yan, “Metaformer is actually what you need for vision,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 819–10 829

    2022

  52. [60]

    Metaformer baselines for vision,

    W. Yu, C. Si, P. Zhou, M. Luo, Y . Zhou, J. Feng, S. Yan, and X. Wang, “Metaformer baselines for vision,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023

    2023

  53. [61]

    Transnext: Robust foveal visual perception for vision transform- ers,

    D. Shi, “Transnext: Robust foveal visual perception for vision transform- ers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 17 773–17 783

    2024

  54. [62]

    Cspn: A category- specific processing network for low-light image enhancement,

    H. Wu, C. Wang, L. Tu, C. Patsch, and Z. Jin, “Cspn: A category- specific processing network for low-light image enhancement,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 34, no. 11, pp. 11 929–11 941, 2024

    2024

  55. [63]

    Revisiting residual networks for adversarial robustness,

    S. Huang, Z. Lu, K. Deb, and V . N. Boddeti, “Revisiting residual networks for adversarial robustness,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 8202–8211

    2023

  56. [64]

    Mgnet: A unified framework of multigrid and convolutional neural network,

    J. He and J. Xu, “Mgnet: A unified framework of multigrid and convolutional neural network,” Science china mathematics , vol. 62, pp. 1331–1354, 2019

    2019

  57. [65]

    Group normalization,

    Y . Wu and K. He, “Group normalization,” inProceedings of the European conference on computer vision (ECCV) , 2018, pp. 3–19

    2018

  58. [66]

    Going deeper with image transformers,

    H. Touvron, M. Cord, A. Sablayrolles, G. Synnaeve, and H. Jégou, “Going deeper with image transformers,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 32–42

    2021

  59. [67]

    Segformer: Simple and efficient design for semantic segmentation with transformers,

    E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, “Segformer: Simple and efficient design for semantic segmentation with transformers,” Advances in neural information processing systems, vol. 34, pp. 12 077–12 090, 2021

    2021

  60. [68]

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

    W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, and L. Shao, “Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 568–578

    2021

  61. [69]

    Deep residual learning for image recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778

    2016

  62. [70]

    Resnest: Split-attention networks,

    H. Zhang, C. Wu, Z. Zhang, Y . Zhu, H. Lin, Z. Zhang, Y . Sun, T. He, J. Mueller, R. Manmatha et al., “Resnest: Split-attention networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 2736–2746

    2022

  63. [71]

    Gaussian error linear units (gelus),

    D. Hendrycks and K. Gimpel, “Gaussian error linear units (gelus),” arXiv preprint arXiv:1606.08415, 2016

    2016 arXiv

  64. [72]

    Run, don’t walk: chasing higher flops for faster neural networks,

    J. Chen, S.-h. Kao, H. He, W. Zhuo, S. Wen, C.-H. Lee, and S.-H. G. Chan, “Run, don’t walk: chasing higher flops for faster neural networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2023, pp. 12 021–12 031

    2023

  65. [73]

    Selective kernel networks,

    X. Li, W. Wang, X. Hu, and J. Yang, “Selective kernel networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 510–519

    2019

  66. [74]

    Distilling the knowledge in a neural network,

    G. Hinton, “Distilling the knowledge in a neural network,” arXiv preprint arXiv:1503.02531, 2015

    2015 arXiv

  67. [75]

    Eviprompt: A training-free evidential prompt generation method for adapting segment anything model in medical images,

    Y . Xu, J. Tang, A. Men, and Q. Chen, “Eviprompt: A training-free evidential prompt generation method for adapting segment anything model in medical images,” IEEE Transactions on Image Processing , 2024

    2024

  68. [76]

    Rewrite the stars,

    X. Ma, X. Dai, Y . Bai, Y . Wang, and Y . Fu, “Rewrite the stars,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 5694–5703

    2024

  69. [77]

    Multi-scale attention network for single image super-resolution,

    Y . Wang, Y . Li, G. Wang, and X. Liu, “Multi-scale attention network for single image super-resolution,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 5950–5960

    2024

  70. [78]

    Mrf3net: An infrared small target detection network using multireceptive field perception and effective feature fusion,

    X. Zhang, X. Zhang, S.-Y . Cao, B. Yu, C. Zhang, and H.-L. Shen, “Mrf3net: An infrared small target detection network using multireceptive field perception and effective feature fusion,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–14, 2024

    2024

  71. [79]

    Revealing the dark secrets of masked image modeling,

    Z. Xie, Z. Geng, J. Hu, Z. Zhang, H. Hu, and Y . Cao, “Revealing the dark secrets of masked image modeling,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 14 475–14 485

    2023

  72. [80]

    Batching soft iou for training semantic segmentation networks,

    Y . Huang, Z. Tang, D. Chen, K. Su, and C. Chen, “Batching soft iou for training semantic segmentation networks,” IEEE Signal Processing Letters, vol. 27, pp. 66–70, 2020

    2020

  73. [81]

    Boundary iou: Improving object-centric image segmentation evaluation,

    B. Cheng, R. Girshick, P. Dollar, A. C. Berg, and A. Kirillov, “Boundary iou: Improving object-centric image segmentation evaluation,” in Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2021, pp. 15 334–15 342

    2021

  74. [82]

    Adan: Adaptive nesterov momentum algorithm for faster optimizing deep models,

    X. Xie, P. Zhou, H. Li, Z. Lin, and S. Yan, “Adan: Adaptive nesterov momentum algorithm for faster optimizing deep models,” IEEE Transac- tions on Pattern Analysis and Machine Intelligence , vol. 46, no. 12, pp. 9508–9520, 2024

    2024

  75. [83]

    Miss detection vs. false alarm: Adversarial learning for small object segmentation in infrared images,

    H. Wang, L. Zhou, and L. Wang, “Miss detection vs. false alarm: Adversarial learning for small object segmentation in infrared images,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 8509–8518

    2019

  76. [84]

    Exploring feature compensation and cross-level correlation for infrared small target detection,

    M. Zhang, K. Yue, J. Zhang, Y . Li, and X. Gao, “Exploring feature compensation and cross-level correlation for infrared small target detection,” in Proceedings of the 30th ACM International Conference on Multimedia, ser. MM ’22. New York, NY , USA: Association for Computing M...

    2022

  77. [85]

    A convnet for the 2020s,

    Z. Liu, H. Mao, C.-Y . Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A convnet for the 2020s,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 11 976–11 986

    2022

  78. [86]

    Normformer: Improved transformer pretraining with extra normalization,

    S. Shleifer, J. Weston, and M. Ott, “Normformer: Improved transformer pretraining with extra normalization,” arXiv preprint arXiv:2110.09456 , 2021

    2021 arXiv

  79. [87]

    Shufflenet v2: Practical guidelines for efficient cnn architecture design,

    N. Ma, X. Zhang, H.-T. Zheng, and J. Sun, “Shufflenet v2: Practical guidelines for efficient cnn architecture design,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 116–131

    2018

Pith tools

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