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REVIEW 4 major objections 8 minor 49 references

Lightweight Multi-Scale Feature Extraction with Fully Connected LMF Layer for Salient Object Detection

T0 review · 4 major / 8 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read The paper proposes LMFNet, a 0.81M-parameter network built on a fully connected multi-scale layer of depthwise separable dilated convolutions, and claims competitive salient object detection on five benchmarks.

desk verdict The LMF layer's defining cross-input connectivity is not implemented in the forward pass, so the experiments test a different architecture than the one proposed. read the letter →

arxiv 2508.07170 v1 pith:T6Y6X4TU submitted 2025-08-10 cs.CV cs.AI

Yunpeng Shi , Lei Chen , Xiaolu Shen , Yanju Guo This is my paper · ORCID
classification cs.CVcs.AI
keywords lightweightnetworksmulti-scalefeaturelearningsalientobjectdetectiondepthwiseseparabledilatedconvolutionLMFlayerreceptivefieldmodelefficiencyCIFAR
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 tries to establish that multi-scale feature extraction for salient object detection can be done with an extremely small network by stacking a new layer, the LMF layer, built from depthwise separable dilated convolutions with varying dilation rates. If true, this would give edge devices a practical saliency detector with 0.81M parameters and 3.8G FLOPs, and the same encoder would transfer to image classification. The authors claim competitive or state-of-the-art results, though the reported tables show performance slightly below some lightweight peers like HVPNet and SAMNet.

What carries the argument

The LMF layer: a set of depthwise separable dilated convolutions K = [K0,...,Kn] with dilation factors d, where each Ki is applied to input feature maps and the outputs are concatenated and fused by a 1x1 convolution. It provides multiple receptive fields in one layer at low parameter cost, with the paper's stated connectivity rule (Eq. 8) as its defining feature.

What would settle it

Inspect the released code and check the forward pass of an LMF layer: if every LMF layer receives only one feature map (as in Eq. 11) and never a vector of maps (as in Eq. 8), then the claimed fully connected structure is not what was tested; alternatively, re-run the DUTS-TE evaluation with the released weights to verify the reported MAE and F-measure numbers.

Watch

Extended reading notes

Core claim

The central discovery claimed is that a fully connected arrangement of depthwise separable dilated convolutions with dilation rates such as [1,4,12,36,108] yields diverse receptive fields that capture multi-scale information at very low parameter cost. Each dilation branch processes input feature maps, and a 1x1 convolution fuses the results. The authors also introduce a design rule: adjacent dilation rates should keep a ratio smaller than the kernel size to avoid information loss. On DUTS-TR training and five test sets, LMFNet reaches the reported metrics, and the encoder achieves competitive CIFAR accuracy.

Load-bearing premise

Each LMF layer is assumed to be truly fully connected, with every dilated branch processing every input feature map; in the implemented forward pass, each layer receives a single feature map, so the fully connected connectivity described in Eq. 8 is not present in the tested model.

Editorial extensions

If this is right

  • LMFNet achieves competitive salient object detection with 0.81M parameters and 3.8G FLOPs, making deployment on resource-constrained devices plausible.
  • The rule that the ratio of adjacent dilation rates must stay below the kernel size offers a practical design principle for layered dilated convolutions.
  • The same encoder reaches competitive CIFAR-10/100 accuracy with fewer than 0.66M parameters, suggesting the LMF layer is transferable beyond SOD.
  • Ablation results indicate that increasing parameter count does not significantly improve performance, pointing to redundancy in the design.
  • The hybrid SSIM+BCE+IoU loss measurably improves performance over each component alone.

Reading between the lines

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

  • The paper's Eq. 8 defines a fully connected layer where each dilation branch processes every input map, but the forward pass in Eq. 11 feeds each LMF layer a single feature map; a direct consequence is that the implemented model differs from the described architecture, so the claimed benefit of full connectivity is untested.
  • The abstract's 'state-of-the-art' claim is stronger than the evidence in Table 1, where LMFNet trails HVPNet and SAMNet on most metrics; a more defensible claim is a competitive efficiency-performance trade-off.
  • The dilation-ratio rule could generalize as a heuristic for any dilated-convolution stack, and testing it on other lightweight backbones would clarify its scope.
  • The CIFAR results suggest the LMF encoder is a reusable lightweight backbone; probing it on ImageNet or dense prediction tasks would illuminate its generality.
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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

4 major / 8 minor

Summary. The paper proposes a Lightweight Multi-scale Feature (LMF) layer based on depthwise separable dilated convolutions arranged in a 'fully connected' structure, and an SOD network LMFNet with 0.81M parameters. The paper claims state-of-the-art or comparable results on five benchmark datasets and also reports image-classification experiments. The central idea is that each dilated branch K_i processes a vector of input feature maps I=[I_0,...,I_m] and fuses the results. However, the forward equations in the actual encoder/decoder only ever pass a single tensor to each LMF layer, so the defining multi-input connectivity is not implemented. In addition, the reported configuration is selected by tuning hyperparameters on the test sets, and the abstract's performance claim is contradicted by the paper's own Table 1.

Significance. If the proposed fully connected LMF layer were actually realized and validated, it would be a useful contribution to lightweight multi-scale feature extraction. The paper's highly compact model (0.81M parameters) and the attempt to ablate dilation rates and loss variants are strengths, and the code link is provided. However, the mismatch between Eq. (8) and the implemented forward pass means that the experiments, as reported, validate only a depthwise separable dilated convolution stack with 1x1 fusion, not the fully connected LMF mechanism. The performance claims are also not supported by the tables: the reported F-measures are below several compared methods, and the text itself concedes lower performance than HVPNet and SAMNet. Because the defining mechanism is absent from the tested model, the central claim cannot be accepted on the present evidence.

major comments (4)
  1. [§3.3 Eq. (8); §4.1 Eqs. (11), (13); Fig. 1] The proposed LMF layer is defined with a vector input I=[I_0,...,I_m], and each branch K_i is applied to every input feature map: F_i = Concat(K_i(I_0),...,K_i(I_m)). In the encoder, however, Eq. (11) sets F_1 = f_LMF1(I) and F_i = f_LMFP_i(F_{i-1}), so every LMF layer receives exactly one tensor. The decoder Eq. (13) likewise passes a single tensor (possibly after an outer Concat) into each f_LMF layer. Consequently, m=0 in every executed LMF layer and Eq. (8) degenerates to F_i = K_i(I_0). The 'fully connected' cross-input connectivity that motivates the title and abstract is never tested. This is an internal inconsistency, not a matter of interpretation.
  2. [Abstract; Table 1; §5.2] The abstract states that LMFNet achieves 'state-of-the-art or comparable results' and outperforms several models, but Table 1 shows, for example, ECSSD Fβ=0.897 for LMFNet versus 0.941 for U2Net, and DUTS-TE Fβ=0.782 versus 0.876 for CDMNET. The text itself concedes that LMFNet is slightly worse than HVPNet and SAMNet and reports an average F-measure of 0.820 versus 0.877 for CDMNET. The stated claim is not supported by the paper's own quantitative results and should be corrected to 'competitive among lightweight models' at most.
  3. [Table 2; §5.3] The dilation-rate vector and the loss function are selected by comparing variants on the same five test datasets used for the final evaluation (ECSSD, DUTS-TE, HKU-IS, PASCAL-S, DUT-OMRON), with no held-out validation split and no error bars or significance tests. Table 2 evaluates all rows on the test sets, and §5.3 reports tuning d[1] on the same five datasets. The reported configuration is therefore the result of fitting hyperparameters to the test data, and the resulting numbers do not provide an unbiased estimate of generalization performance.
  4. [§2.1 (last paragraph) vs. §§3–4] The related-work section states, 'Additionally, we incorporate Deformable Modules to further improve the network's adaptability to object deformations.' The methodology and architecture description in Sections 3–4 and Figure 3 do not mention or implement deformable convolutions, and no experiment or ablation involving them is reported. As written, this introduces an unsupported methodological component that is not part of the actual model.
minor comments (8)
  1. [§2 heading] The heading reads 'Relate Work'; it should be 'Related Work'.
  2. [Eq. (14)] 'Sigmod' should be 'Sigmoid'.
  3. [§5.1.3] The text says the F-measure curve is 'as shown in formula (15)', but Eq. (15) is the loss function; the reference should be to Eq. (17).
  4. [Eq. (1)] Eq. (1) defines d_i as 'stride', while the rest of the paper uses d as the dilation rate. The notation should be reconciled.
  5. [§5.3] The section contains an unresolved cross-reference: 'As shown in Table ??'. Also, 'In Table3-10, we removed all dilated convolutions...' is ambiguous and should be clarified.
  6. [Table 1 caption and §5.2] The Table 1 caption says gray marks 'Results inferior to our model' for traditional networks, while §5.2 says 'we highlight the second-best values in gray and the best values in red'. These conventions should be made consistent.
  7. [§5.2] The comparison with lightweight models refers to 'Table IV', but the paper only has Tables 1–5; this should be corrected to the appropriate table.
  8. [Table 2] Row 10 of Table 2 uses '*' without explaining what configuration it represents.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the paper is an empirical architecture study with no derivation chain that reduces to its inputs; the noted Eq. 8/Eq. 11 inconsistency and test-set hyperparameter selection are validity concerns, not circularity.

full rationale

This paper does not claim a first-principles derivation or a predictive theory; it proposes an architectural layer (LMF) and reports empirical benchmark results. The core definitional equations (Eq. 8: Fi = Concat(Ki(I0),...,Ki(Im)) and Eq. 10: Oi = Kc(Fi)) define the layer, but the network forward pass in Eq. 11 feeds each LMF layer a single tensor, so the 'fully connected' multi-input topology is not actually exercised. This is an internal inconsistency that undermines the claim that the experiments validate the proposed mechanism, but it is not circularity: the reported numbers are measurements of a concrete network, not quantities forced by construction to equal their inputs. Likewise, the ablation in Table 2 selects dilation rates and loss terms by evaluating on the same five test datasets later used in Table 1; this is a selection-bias / multiple-testing concern, not a case where a fitted parameter is renamed as a prediction. Finally, there are no load-bearing self-citations: the cited lightweight SOD works ([17], [18], [9], etc.) are external baselines, and no uniqueness theorem or ansatz is imported from the authors' own prior work. The paper is self-contained against external benchmarks, so the appropriate circularity score is 0.

Assumptions & free parameters 4 free parameters · 4 assumptions · 1 invented entities

The central claim rests on hand-chosen architecture hyperparameters (dilation rates, loss, channel widths) and on the untested assertion that the fully connected topology contributes to performance. The only invented construct, the LMF layer, is an architecture module with independent testability through public benchmarks and promised code.

free parameters (4)
  • dilation factor vector d = [1,4,1] for the first LMF layer; [1,4,12,36,108] for others
    Hand-selected; Table 2 shows varying d changes results, with d[1]=4 chosen as best on test datasets.
  • loss function weights = Unweighted sum of SSIM, BCE, IoU (Eq. 15)
    Compared in Table 2 rows 5-7; hybrid loss chosen based on higher F-measure on the five test sets.
  • channel widths of LMF layers = Not stated; param counts 0.5M, 0.81M, 1.31M in Table 2 correspond to different widths
    The paper does not list the exact channel counts, preventing exact reproduction; the widths are tuned to hit parameter targets.
  • kernel size of first convolution = 5 (first layer), 3 elsewhere
    Mentioned in Section 5.3 ablation; the first layer's kernel size interacts with the dilation ratio rule.
assumptions (4)
  • standard math Receptive field can be computed as RFi = k_size_i + (RF_{i-1} - 1) * d_i (Eq. 1).
    Known CNN receptive field recurrence; used to justify dilation-based expansion.
  • ad hoc to paper The dilation ratio between adjacent dilated layers must be smaller than the kernel size of the preceding layer to prevent information loss.
    Proposed in Section 4.1; supported only by one ablation row (d[1]=6 causing a drop). Not a general theorem.
  • standard math Depthwise separable dilated convolution preserves accuracy while reducing parameters.
    Borrowed from MobileNet and DeepLab; the paper relies on this without new justification.
  • domain assumption The fully connected multi-input processing defined by Eq. 8 is a beneficial design.
    The paper asserts the fully connected topology is central, yet the implemented forward pass (Eq. 11) does not use it; the assumption is untested.
invented entities (1)
  • LMF layer independent evidence
    purpose: Multi-scale feature extraction layer combining depthwise separable dilated convolutions with multiple dilation rates in a fully connected arrangement.
    It is an architectural component, not a physical entity. Its effect is testable on public SOD benchmarks; code is promised at the GitHub URL, though no artifacts are in the preprint.

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Cite this review

Pith. "Pith review of Lightweight Multi-Scale Feature Extraction with Fully Connected LMF Layer for Salient Object Detection." pith.science (2026). https://pith.science/paper/T6Y6X4TU

@misc{pith2026250807170,
  author       = {Pith},
  title        = {Pith review of: Lightweight Multi-Scale Feature Extraction with Fully Connected LMF Layer for Salient Object Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T6Y6X4TU}},
  note         = {Machine review of arXiv:2508.07170}
}
read the original abstract

In the domain of computer vision, multi-scale feature extraction is vital for tasks such as salient object detection. However, achieving this capability in lightweight networks remains challenging due to the trade-off between efficiency and performance. This paper proposes a novel lightweight multi-scale feature extraction layer, termed the LMF layer, which employs depthwise separable dilated convolutions in a fully connected structure. By integrating multiple LMF layers, we develop LMFNet, a lightweight network tailored for salient object detection. Our approach significantly reduces the number of parameters while maintaining competitive performance. Here, we show that LMFNet achieves state-of-the-art or comparable results on five benchmark datasets with only 0.81M parameters, outperforming several traditional and lightweight models in terms of both efficiency and accuracy. Our work not only addresses the challenge of multi-scale learning in lightweight networks but also demonstrates the potential for broader applications in image processing tasks. The related code files are available at https://github.com/Shi-Yun-peng/LMFNet

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Works this paper leans on

49 extracted references · 41 canonical work pages

  1. [1]

    Advances in neural information processing systems 25 (2012)

    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolu- tional neural networks. Advances in neural information processing systems 25 (2012)

    2012

  2. [2]

    arXiv preprint arXiv:1409.1556 (2014)

    Simonyan, K., Zisserman, A.: Very deep convo- lutional networks for large-scale image recog- nition. arXiv preprint arXiv:1409.1556 (2014)

    arXiv 2014

  3. [3]

    In: Proceedings of the IEEE Conference on Com- puter Vision and Pattern Recognition, pp

    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Com- puter Vision and Pattern Recognition, pp. 770–778 (2016)

    2016

  4. [4]

    In: Proceedings of the IEEE Confer- ence on Computer Vision and Pattern Recog- nition, pp

    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convo- lutions. In: Proceedings of the IEEE Confer- ence on Computer Vision and Pattern Recog- nition, pp. 1–9 (2015)

    work page 2015

  5. [5]

    In: Proceed- ings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

    Lin, T.-Y., Doll´ ar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceed- ings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117– 2125 (2017)

    work page 2017

  6. [6]

    IEEE Transactions on Image Pro- cessing 32, 3027–3039 (2023)

    Zhou, W., Sun, F., Jiang, Q., Cong, R., Hwang, J.-N.: Wavenet: Wavelet network with knowledge distillation for rgb-t salient object detection. IEEE Transactions on Image Pro- cessing 32, 3027–3039 (2023)

    work page 2023

  7. [7]

    In: Pro- ceedings of the IEEE International Conference on Computer Vision, pp

    Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., Zhang, C.: Learning efficient convolutional networks through network slimming. In: Pro- ceedings of the IEEE International Conference on Computer Vision, pp. 2736–2744 (2017)

    work page 2017

  8. [8]

    IEEE Transactions on Image Process- ing 32, 1329–1340 (2023)

    Zhou, W., Zhu, Y., Lei, J., Yang, R., Yu, L.: Lsnet: Lightweight spatial boosting network for detecting salient objects in rgb-thermal 10 images. IEEE Transactions on Image Process- ing 32, 1329–1340 (2023)

    work page 2023

Show all 49 references
  1. [9]

    IEEE transactions on cybernetics 51(9), 4439–4449 (2020)

    Liu, Y., Gu, Y.-C., Zhang, X.-Y., Wang, W., Cheng, M.-M.: Lightweight salient object detection via hierarchical visual perception learning. IEEE transactions on cybernetics 51(9), 4439–4449 (2020)

    2020

  2. [10]

    In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

    Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., Li, S.: Salient object detection: A discrim- inative regional feature integration approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2083–2090 (2013)

  3. [11]

    In: Proceedings of the IEEE International Conference on Computer Vision, pp

    Li, X., Lu, H., Zhang, L., Ruan, X., Yang, M.-H.: Saliency detection via dense and sparse reconstruction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2976–2983 (2013)

    2013

  4. [12]

    IEEE transactions on multimedia 23, 2902–2916 (2020)

    Nawaz, M., Yan, H.: Saliency detection using deep features and affinity-based robust back- ground subtraction. IEEE transactions on multimedia 23, 2902–2916 (2020)

    2020

  5. [13]

    In: Proceedings of the IEEE Confer- ence on Computer Vision and Pattern Recog- nition, pp

    Liu, N., Han, J., Yang, M.-H.: Picanet: Learn- ing pixel-wise contextual attention for saliency detection. In: Proceedings of the IEEE Confer- ence on Computer Vision and Pattern Recog- nition, pp. 3089–3098 (2018)

    2018

  6. [14]

    Pattern recognition 106, 107404 (2020)

    Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O.R., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection. Pattern recognition 106, 107404 (2020)

    2020

  7. [15]

    In: Proceedings of the IEEE/CVF Winter Conference on Applica- tions of Computer Vision, pp

    Ke, Y.Y., Tsubono, T.: Recursive contour- saliency blending network for accurate salient object detection. In: Proceedings of the IEEE/CVF Winter Conference on Applica- tions of Computer Vision, pp. 2940–2950 (2022)

    2022

  8. [16]

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

    Pang, Y., Zhao, X., Zhang, L., Lu, H.: Multi- scale interactive network for salient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9413–9422 (2020)

    2020

  9. [17]

    IEEE Transactions on Image Processing 30, 3804–3814 (2021)

    Liu, Y., Zhang, X.-Y., Bian, J.-W., Zhang, L., Cheng, M.-M.: Samnet: Stereoscopically atten- tive multi-scale network for lightweight salient object detection. IEEE Transactions on Image Processing 30, 3804–3814 (2021)

    2021

  10. [18]

    IEEE Transactions on Multimedia (2024)

    Zhou, X., Shen, K., Liu, Z.: Admnet: Attention-guided densely multi-scale network for lightweight salient object detection. IEEE Transactions on Multimedia (2024)

    2024

  11. [19]

    IEEE Transactions on Pattern analysis and machine intelligence 33(2), 353– 367 (2010)

    Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., Shum, H.-Y.: Learning to detect a salient object. IEEE Transactions on Pattern analysis and machine intelligence 33(2), 353– 367 (2010)

    2010

  12. [20]

    In: 2009 IEEE Conference on Com- puter Vision and Pattern Recognition, pp

    Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: 2009 IEEE Conference on Com- puter Vision and Pattern Recognition, pp. 1597–1604 (2009). IEEE

    2009

  13. [21]

    Journal of Visual Communication and Image Representation 41, 406–413 (2016)

    Kuo, C.-C.J.: Understanding convolutional neural networks with a mathematical model. Journal of Visual Communication and Image Representation 41, 406–413 (2016)

    2016

  14. [22]

    IEEE Transactions on Multimedia 25, 1991–2006 (2022)

    Qiu, Y., Liu, Y., Chen, Y., Zhang, J., Zhu, J., Xu, J.: A2sppnet: Attentive atrous spa- tial pyramid pooling network for salient object detection. IEEE Transactions on Multimedia 25, 1991–2006 (2022)

    1991

  15. [23]

    arXiv preprint arXiv:1704.04861 (2017)

    Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Effi- cient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)

    2017 arXiv

  16. [24]

    IEEE transactions on pattern analy- sis and machine intelligence 40(4), 834–848 (2017)

    Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analy- sis and machine intelligence 40(4), 834–848 (2017)

    2017

  17. [25]

    In: The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, vol

    Wang, Z., Simoncelli, E.P., Bovik, A.C.: Mul- tiscale structural similarity for image quality assessment. In: The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, vol. 2, pp. 1398–1402 (2003). Ieee

    2003

  18. [26]

    Annals of operations research 134, 19–67 (2005)

    De Boer, P.-T., Kroese, D.P., Mannor, S., Rubinstein, R.Y.: A tutorial on the cross- entropy method. Annals of operations research 134, 19–67 (2005)

    2005

  19. [27]

    In: Proceedings of the IEEE International Conference on Computer Vision, pp

    M´ attyus, G., Luo, W., Urtasun, R.: Deep- roadmapper: Extracting road topology from aerial images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3438–3446 (2017)

    2017

  20. [28]

    In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

    Wang, L., Lu, H., Wang, Y., Feng, M., Wang, D., Yin, B., Ruan, X.: Learning to detect salient objects with image-level supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 136–145 (2017)

    2017

  21. [29]

    In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

    Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical 11 saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1155–1162 (2013)

    2013

  22. [30]

    In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

    Li, G., Yu, Y.: Visual saliency based on mul- tiscale deep features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5455–5463 (2015)

    2015

  23. [31]

    In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

    Li, Y., Hou, X., Koch, C., Rehg, J.M., Yuille, A.L.: The secrets of salient object segmenta- tion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 280–287 (2014)

    2014

  24. [32]

    In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

    Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.-H.: Saliency detection via graph-based manifold ranking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3166–3173 (2013)

    2013

  25. [33]

    arXiv preprint arXiv:1805.10421 (2018)

    Fan, D.-P., Gong, C., Cao, Y., Ren, B., Cheng, M.-M., Borji, A.: Enhanced-alignment mea- sure for binary foreground map evaluation. arXiv preprint arXiv:1805.10421 (2018)

    2018 arXiv

  26. [34]

    In: Proceedings of the IEEE International Conference on Com- puter Vision, pp

    Fan, D.-P., Cheng, M.-M., Liu, Y., Li, T., Borji, A.: Structure-measure: A new way to evaluate foreground maps. In: Proceedings of the IEEE International Conference on Com- puter Vision, pp. 4548–4557 (2017)

    2017

  27. [35]

    In: Proceedings of the IEEE International Confer- ence on Computer Vision, pp

    Zhang, P., Wang, D., Lu, H., Wang, H., Ruan, X.: Amulet: Aggregating multi-level convolu- tional features for salient object detection. In: Proceedings of the IEEE International Confer- ence on Computer Vision, pp. 202–211 (2017)

    2017

  28. [36]

    In: Proceed- ings of the IEEE International Conference on Computer Vision, pp

    Zhang, P., Wang, D., Lu, H., Wang, H., Yin, B.: Learning uncertain convolutional features for accurate saliency detection. In: Proceed- ings of the IEEE International Conference on Computer Vision, pp. 212–221 (2017)

    2017

  29. [37]

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

    Wu, Z., Su, L., Huang, Q.: Cascaded partial decoder for fast and accurate salient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3907–3916 (2019)

    2019

  30. [38]

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

    Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M., Jagersand, M.: Basnet: Boundary-aware salient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7479–7489 (2019)

    2019

  31. [39]

    In: Com- puter vision–ECCV 2020: 16th European Con- ference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16, pp

    Zhao, X., Pang, Y., Zhang, L., Lu, H., Zhang, L.: Suppress and balance: A simple gated network for salient object detection. In: Com- puter vision–ECCV 2020: 16th European Con- ference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16, pp. 35–51 (2020). Springer

    2020

  32. [40]

    In: Pro- ceedings of the European Conference on Com- puter Vision (ECCV), pp

    Chen, S., Tan, X., Wang, B., Hu, X.: Reverse attention for salient object detection. In: Pro- ceedings of the European Conference on Com- puter Vision (ECCV), pp. 234–250 (2018)

    2018

  33. [41]

    ACM Transactions on Multimedia Computing, Communications and Applications 19(1), 1–15 (2023)

    Song, Y., Tang, H., Sebe, N., Wang, W.: Disen- tangle saliency detection into cascaded detail modeling and body filling. ACM Transactions on Multimedia Computing, Communications and Applications 19(1), 1–15 (2023)

    2023

  34. [42]

    In: European Conference on Computer Vision, pp

    Gao, S.-H., Tan, Y.-Q., Cheng, M.-M., Lu, C., Chen, Y., Yan, S.: Highly efficient salient object detection with 100k parameters. In: European Conference on Computer Vision, pp. 702–721 (2020). Springer

    2020

  35. [43]

    IEEE Geoscience and Remote Sensing Letters 19, 1–5 (2022)

    Shen, K., Zhou, X., Wan, B., Shi, R., Zhang, J.: Fully squeezed multiscale inference network for fast and accurate saliency detection in opti- cal remote-sensing images. IEEE Geoscience and Remote Sensing Letters 19, 1–5 (2022)

    2022

  36. [44]

    IEEE Transactions on Geoscience and Remote Sensing 60, 1–13 (2021)

    Li, G., Liu, Z., Lin, W., Ling, H.: Multi-content complementation network for salient object detection in optical remote sensing images. IEEE Transactions on Geoscience and Remote Sensing 60, 1–13 (2021)

    2021

  37. [45]

    Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)

    2009

  38. [46]

    In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

    Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: Mobilenetv2: Inverted resid- uals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)

    2018

  39. [47]

    In: Pro- ceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

    Zhang, X., Zhou, X., Lin, M., Sun, J.: Shuf- flenet: An extremely efficient convolutional neural network for mobile devices. In: Pro- ceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848– 6856 (2018)

    2018

  40. [48]

    In: Proceedings of the European Conference on Computer Vision (ECCV), pp

    Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: Shufflenet v2: Practical guidelines for efficient cnn architecture design. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 116–131 (2018)

    2018

  41. [49]

    arXiv preprint arXiv:1602.07360 (2016) 12

    Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size. arXiv preprint arXiv:1602.07360 (2016) 12

    2016 arXiv

Pith tools

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