A Cross-Level Iterative Subtraction Network for Camouflaged Object Detection
Abstract
:1. Introduction
- We propose a cross-level iterative subtraction network for camouflaged object detection, which focuses on difference complementation between cross-level features to enhance the segmentation performance;
- We present a cross-level iterative structure and multi-scale strip convolution subtraction module for the efficient interaction and integration of features across multiple levels, which captures difference information between cross-level features and guides the network to focus on the details of object;
- We present the enhanced guidance attention module to deeply mine multi-scale local context information and capture the relationships between different features, which aims to improve the segmentation accuracy of camouflaged objects.
2. Related Work
2.1. Camouflaged Object Detection
2.2. Multi-Level Feature Integration
2.3. Transformers in Computer Vision
3. CISNet Method
3.1. Cross-Level Iterative Structure
Multi-Scale Strip Convolution Subtraction
3.2. Enhanced Guidance Attention
3.3. Loss Function
4. Experiments
4.1. Datasets
4.2. Evaluation Metrics
4.3. Implementation Details
4.4. Performance Comparison
4.4.1. Quantitative Comparisons
4.4.2. Visual Comparisons
4.5. Ablation Studies
4.5.1. Effectiveness of Cross-Level Integration Mechanism and Strip Convolutions in MSCSub
4.5.2. Effectiveness of EGA
4.5.3. Effectiveness of Iterative Strategy
4.5.4. Influence of LossNet
4.5.5. Influence of Channels in MSCSub
5. Use Cases
5.1. Camouflage Pattern People Detection
5.2. Polyp Segmentation
5.3. Transparent Object Detection
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Dong, B.; Wang, W.; Fan, D.P.; Li, J.; Fu, H.; Shao, L. Polyp-pvt: Polyp segmentation with pyramid vision transformers. arXiv 2021, arXiv:2108.06932. [Google Scholar] [CrossRef]
- Fan, D.P.; Zhou, T.; Ji, G.P.; Zhou, Y.; Chen, G.; Fu, H.; Shen, J.; Shao, L. Inf-net: Automatic covid-19 lung infection segmentation from ct images. IEEE Trans. Med. Imaging 2020, 39, 2626–2637. [Google Scholar] [CrossRef] [PubMed]
- Fan, D.P.; Ji, G.P.; Zhou, T.; Chen, G.; Fu, H.; Shen, J.; Shao, L. Pranet: Parallel reverse attention network for polyp segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, Peru, 4–8 October 2020; Springer: Berlin/Heidelberg, Germany, 2020; pp. 263–273. [Google Scholar]
- Deng, J.; Gong, H.; Ming, L. Medical Image Segmentation Based on Object Detection. J. Univ. Electron. Sci. Technol. China 2023, 52, 254. [Google Scholar]
- Xie, E.; Wang, W.; Wang, W.; Ding, M.; Shen, C.; Luo, P. Segmenting transparent objects in the wild. In Proceedings of the Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, 23–28 August 2020; Proceedings, Part XIII 16. Springer: Berlin/Heidelberg, Germany, 2020; pp. 696–711. [Google Scholar]
- Pérez-de la Fuente, R.; Delclòs, X.; Peñalver, E.; Speranza, M.; Wierzchos, J.; Ascaso, C.; Engel, M.S. Early evolution and ecology of camouflage in insects. Proc. Natl. Acad. Sci. USA 2012, 109, 21414–21419. [Google Scholar] [CrossRef] [PubMed]
- Zheng, Y.; Zhang, X.; Wang, F.; Cao, T.; Sun, M.; Wang, X. Detection of people with camouflage pattern via dense deconvolution network. IEEE Signal Process. Lett. 2018, 26, 29–33. [Google Scholar] [CrossRef]
- Dai, X.; Gong, H.; Wu, S.; Yuan, X.; Ma, Y. Fully convolutional line parsing. Neurocomputing 2022, 506, 1–11. [Google Scholar] [CrossRef]
- Fan, D.P.; Ji, G.P.; Sun, G.; Cheng, M.M.; Shen, J.; Shao, L. Camouflaged object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 14–19 June 2020; pp. 2777–2787. [Google Scholar]
- Lv, Y.; Zhang, J.; Dai, Y.; Li, A.; Liu, B.; Barnes, N.; Fan, D.P. Simultaneously localize, segment and rank the camouflaged objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 20–25 June 2021; pp. 11591–11601. [Google Scholar]
- He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 770–778. [Google Scholar]
- Gao, S.H.; Cheng, M.M.; Zhao, K.; Zhang, X.Y.; Yang, M.H.; Torr, P. Res2net: A new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell. 2019, 43, 652–662. [Google Scholar] [CrossRef]
- Wang, W.; Xie, E.; Li, X.; Fan, D.P.; Song, K.; Liang, D.; Lu, T.; Luo, P.; Shao, L. Pvt v2: Improved baselines with pyramid vision transformer. Comput. Vis. Media 2022, 8, 415–424. [Google Scholar] [CrossRef]
- Liu, Z.; Lin, Y.; Cao, Y.; Hu, H.; Wei, Y.; Zhang, Z.; Lin, S.; Guo, B. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, BC, Canada, 11–17 October 2021; pp. 10012–10022. [Google Scholar]
- Chen, G.; Liu, S.J.; Sun, Y.J.; Ji, G.P.; Wu, Y.F.; Zhou, T. Camouflaged object detection via context-aware cross-level fusion. IEEE Trans. Circuits Syst. Video Technol. 2022, 32, 6981–6993. [Google Scholar] [CrossRef]
- Xing, H.; Wang, Y.; Wei, X.; Tang, H.; Gao, S.; Zhang, W. Go Closer To See Better: Camouflaged Object Detection via Object Area Amplification and Figure-ground Conversion. IEEE Trans. Circuits Syst. Video Technol. 2023, 33, 5444–5457. [Google Scholar] [CrossRef]
- Mei, H.; Ji, G.P.; Wei, Z.; Yang, X.; Wei, X.; Fan, D.P. Camouflaged object segmentation with distraction mining. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 20–25 June 2021; pp. 8772–8781. [Google Scholar]
- Zhu, H.; Li, P.; Xie, H.; Yan, X.; Liang, D.; Chen, D.; Wei, M.; Qin, J. I can find you! boundary-guided separated attention network for camouflaged object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, Online, 22 February–1 March 2022; Volume 36, pp. 3608–3616. [Google Scholar]
- Ji, G.P.; Zhu, L.; Zhuge, M.; Fu, K. Fast camouflaged object detection via edge-based reversible re-calibration network. Pattern Recognit. 2022, 123, 108414. [Google Scholar] [CrossRef]
- Yin, B.; Zhang, X.; Hou, Q.; Sun, B.Y.; Fan, D.P.; Van Gool, L. Camoformer: Masked separable attention for camouflaged object detection. arXiv 2022, arXiv:2212.06570. [Google Scholar] [CrossRef]
- Li, X.; Xu, F.; Yong, X.; Chen, D.; Xia, R.; Ye, B.; Gao, H.; Chen, Z.; Lyu, X. SSCNet: A Spectrum-Space Collaborative Network for Semantic Segmentation of Remote Sensing Images. Remote Sens. 2023, 15, 5610. [Google Scholar] [CrossRef]
- Li, X.; Xu, F.; Liu, F.; Tong, Y.; Lyu, X.; Zhou, J. Semantic Segmentation of Remote Sensing Images by Interactive Representation Refinement and Geometric Prior-Guided Inference. IEEE Trans. Geosci. Remote. Sens. 2024, 62, 5400318. [Google Scholar] [CrossRef]
- Galun; Sharon; Basri; Brandt. Texture segmentation by multiscale aggregation of filter responses and shape elements. In Proceedings of the Ninth IEEE International Conference on Computer Vision, Washington, DC, USA, 13–16 October 2003; pp. 716–723. [Google Scholar]
- Guo, H.; Dou, Y.; Tian, T.; Zhou, J.; Yu, S. A robust foreground segmentation method by temporal averaging multiple video frames. In Proceedings of the 2008 International Conference on Audio, Language and Image Processing, Shanghai, China, 7–9 July 2008; pp. 878–882. [Google Scholar]
- Hall, J.R.; Cuthill, I.C.; Baddeley, R.; Shohet, A.J.; Scott-Samuel, N.E. Camouflage, detection and identification of moving targets. Proc. R. Soc. B Biol. Sci. 2013, 280, 20130064. [Google Scholar] [CrossRef] [PubMed]
- Pang, Y.; Zhao, X.; Xiang, T.Z.; Zhang, L.; Lu, H. Zoom in and out: A mixed-scale triplet network for camouflaged object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 18–24 June 2022; pp. 2160–2170. [Google Scholar]
- Yan, X.; Sun, M.; Han, Y.; Wang, Z. Camouflaged Object Segmentation Based on Matching–Recognition–Refinement Network. IEEE Trans. Neural Netw. Learn. Syst.
- Jia, Q.; Yao, S.; Liu, Y.; Fan, X.; Liu, R.; Luo, Z. Segment, magnify and reiterate: Detecting camouflaged objects the hard way. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 18–24 June 2022; pp. 4713–4722. [Google Scholar]
- Li, A.; Zhang, J.; Lv, Y.; Liu, B.; Zhang, T.; Dai, Y. Uncertainty-aware joint salient object and camouflaged object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 20–25 June 2021; pp. 10071–10081. [Google Scholar]
- Yang, F.; Zhai, Q.; Li, X.; Huang, R.; Luo, A.; Cheng, H.; Fan, D.P. Uncertainty-guided transformer reasoning for camouflaged object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Nashville, TN, USA, 20–25 June 2021; pp. 4146–4155. [Google Scholar]
- 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, Seattle, WA, USA, 14–19 June 2020; pp. 9413–9422. [Google Scholar]
- Song, Z.; Kang, X.; Wei, X.; Liu, H.; Dian, R.; Li, S. FSNet: Focus Scanning Network for Camouflaged Object Detection. IEEE Trans. Image Process. 2023, 32, 2267–2278. [Google Scholar] [CrossRef] [PubMed]
- Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.N.; Kaiser, Ł.; Polosukhin, I. Attention is all you need. Adv. Neural Inf. Process. Syst. 2017, 30, 6000–6010. [Google Scholar]
- Devlin, J.; Chang, M.W.; Lee, K.; Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv 2018, arXiv:1810.04805. [Google Scholar]
- Chu, X.; Tian, Z.; Wang, Y.; Zhang, B.; Ren, H.; Wei, X.; Xia, H.; Shen, C. Twins: Revisiting the design of spatial attention in vision transformers. Adv. Neural Inf. Process. Syst. 2021, 34, 9355–9366. [Google Scholar]
- Han, K.; Xiao, A.; Wu, E.; Guo, J.; Xu, C.; Wang, Y. Transformer in transformer. Adv. Neural Inf. Process. Syst. 2021, 34, 15908–15919. [Google Scholar]
- Wu, H.; Xiao, B.; Codella, N.; Liu, M.; Dai, X.; Yuan, L.; Zhang, L. Cvt: Introducing convolutions to vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada, 10–17 October 2021; pp. 22–31. [Google Scholar]
- Li, X.; Xu, F.; Gao, H.; Liu, F.; Lyu, X. A Frequency Domain Feature-Guided Network for Semantic Segmentation of Remote Sensing Images. IEEE Signal Process. Lett. 2024, 31, 1369–1373. [Google Scholar] [CrossRef]
- Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. An image is worth 16 × 16 words: Transformers for image recognition at scale. arXiv 2020, arXiv:2010.11929. [Google Scholar]
- Touvron, H.; Cord, M.; Douze, M.; Massa, F.; Sablayrolles, A.; Jégou, H. Training data-efficient image transformers & distillation through attention. In Proceedings of the International Conference on Machine Learning. PMLR, Virtual Event, 18–24 July 2021; pp. 10347–10357. [Google Scholar]
- Wang, W.; Xie, E.; Li, X.; Fan, D.P.; Song, K.; Liang, D.; Lu, T.; Luo, P.; Shao, L. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada, 10–17 October 2021; pp. 568–578. [Google Scholar]
- Carion, N.; Massa, F.; Synnaeve, G.; Usunier, N.; Kirillov, A.; Zagoruyko, S. End-to-end object detection with transformers. In Proceedings of the European Conference on Computer Vision, Glasgow, UK, 23–28 August 2020; Springer: Berlin/Heidelberg, Germany, 2020; pp. 213–229. [Google Scholar]
- Liu, N.; Zhang, N.; Wan, K.; Shao, L.; Han, J. Visual saliency transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada, 10–17 October 2021; pp. 4722–4732. [Google Scholar]
- Zhuge, M.; Fan, D.P.; Liu, N.; Zhang, D.; Xu, D.; Shao, L. Salient object detection via integrity learning. IEEE Trans. Pattern Anal. Mach. Intell. 2022, 45, 3738–3752. [Google Scholar] [CrossRef]
- Jiang, Z.H.; Hou, Q.; Yuan, L.; Zhou, D.; Shi, Y.; Jin, X.; Wang, A.; Feng, J. All tokens matter: Token labeling for training better vision transformers. Adv. Neural Inf. Process. Syst. 2021, 34, 18590–18602. [Google Scholar]
- Xie, E.; Wang, W.; Yu, Z.; Anandkumar, A.; Alvarez, J.M.; Luo, P. SegFormer: Simple and efficient design for semantic segmentation with transformers. Adv. Neural Inf. Process. Syst. 2021, 34, 12077–12090. [Google Scholar]
- Zheng, S.; Lu, J.; Zhao, H.; Zhu, X.; Luo, Z.; Wang, Y.; Fu, Y.; Feng, J.; Xiang, T.; Torr, P.H.; et al. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 20–25 June 2021; pp. 6881–6890. [Google Scholar]
- Li, X.; Xu, F.; Li, L.; Xu, N.; Liu, F.; Yuan, C.; Chen, Z.; Lyu, X. AAFormer: Attention-Attended Transformer for Semantic Segmentation of Remote Sensing Images. IEEE Geosci. Remote Sens. Lett. 2024, 21, 5002805. [Google Scholar] [CrossRef]
- Li, X.; Xu, F.; Lyu, X.; Gao, H.; Tong, Y.; Cai, S.; Li, S.; Liu, D. Dual attention deep fusion semantic segmentation networks of large-scale satellite remote-sensing images. Int. J. Remote Sens. 2021, 42, 3583–3610. [Google Scholar] [CrossRef]
- Hu, J.; Shen, L.; Sun, G. Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–22 June 2018; pp. 7132–7141. [Google Scholar]
- Li, X.; Xu, F.; Liu, F.; Lyu, X.; Tong, Y.; Xu, Z.; Zhou, J. A Synergistical Attention Model for Semantic Segmentation of Remote Sensing Images. IEEE Trans. Geosci. Remote Sens. 2023, 61, 5400916. [Google Scholar] [CrossRef]
- Hu, X.; Wang, S.; Qin, X.; Dai, H.; Ren, W.; Luo, D.; Tai, Y.; Shao, L. High-resolution iterative feedback network for camouflaged object detection. Proc. AAAI Conf. Artif. Intell. 2023, 37, 881–889. [Google Scholar] [CrossRef]
- Huang, Z.; Dai, H.; Xiang, T.Z.; Wang, S.; Chen, H.X.; Qin, J.; Xiong, H. Feature shrinkage pyramid for camouflaged object detection with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 17–24 June 2023; pp. 5557–5566. [Google Scholar]
- Zamir, S.W.; Arora, A.; Khan, S.; Hayat, M.; Khan, F.S.; Yang, M.H. Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 18–24 June 2022; pp. 5728–5739. [Google Scholar]
- Wei, J.; Wang, S.; Huang, Q. F3Net: Fusion, feedback and focus for salient object detection. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, New York, NY, USA, 7–12 February 2020; Volume 34, pp. 12321–12328. [Google Scholar]
- Zhao, X.; Zhang, L.; Lu, H. Automatic polyp segmentation via multi-scale subtraction network. In Proceedings, Part I 24, Proceedings of the Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, 27 September–1 October 2021; Springer: Berlin/Heidelberg, Germany, 2021; pp. 120–130. [Google Scholar]
- Skurowski, P.; Abdulameer, H.; Błaszczyk, J.; Depta, T.; Kornacki, A.; Kozieł, P. Animal camouflage analysis: Chameleon database. Unpubl. Manuscr. 2018, 2, 7. [Google Scholar]
- Le, T.N.; Nguyen, T.V.; Nie, Z.; Tran, M.T.; Sugimoto, A. Anabranch network for camouflaged object segmentation. Comput. Vis. Image Underst. 2019, 184, 45–56. [Google Scholar] [CrossRef]
- 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 Computer Vision, Venice, Italy, 22–29 October 2017; pp. 4548–4557. [Google Scholar]
- Fan, D.P.; Gong, C.; Cao, Y.; Ren, B.; Cheng, M.M.; Borji, A. Enhanced-alignment measure for binary foreground map evaluation. arXiv 2018, arXiv:1805.10421. [Google Scholar]
- Margolin, R.; Zelnik-Manor, L.; Tal, A. How to evaluate foreground maps? In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 23–28 June 2014; pp. 248–255. [Google Scholar]
- Perazzi, F.; Krähenbühl, P.; Pritch, Y.; Hornung, A. Saliency filters: Contrast based filtering for salient region detection. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 16–21 June 2012; pp. 733–740. [Google Scholar]
- Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; et al. Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 2019, 32, 8026–8037. [Google Scholar]
- Krizhevsky, A.; Sutskever, I.; Hinton, G.E. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 2012, 25, 1097–1105. [Google Scholar] [CrossRef]
- Loshchilov, I.; Hutter, F. Decoupled weight decay regularization. arXiv 2017, arXiv:1711.05101. [Google Scholar]
- Fan, D.P.; Ji, G.P.; Cheng, M.M.; Shao, L. Concealed object detection. IEEE Trans. Pattern Anal. Mach. Intell. 2021, 44, 6024–6042. [Google Scholar] [CrossRef]
- Pei, J.; Cheng, T.; Fan, D.P.; Tang, H.; Chen, C.; Van Gool, L. Osformer: One-stage camouflaged instance segmentation with transformers. In Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel, 23–27 October 2022; Springer: Berlin/Heidelberg, Germany, 2022; pp. 19–37. [Google Scholar]
- Zhang, Q.; Ge, Y.; Zhang, C.; Bi, H. TPRNet: Camouflaged object detection via transformer-induced progressive refinement network. Vis. Comput. 2022, 39, 4593–4607. [Google Scholar] [CrossRef]
- Ji, G.P.; Fan, D.P.; Chou, Y.C.; Dai, D.; Liniger, A.; Van Gool, L. Deep gradient learning for efficient camouflaged object detection. Mach. Intell. Res. 2023, 20, 92–108. [Google Scholar] [CrossRef]
- Yu, J.; Chen, S.; Lu, L.; Chen, Z.; Xu, X.; Hu, X.; Zhu, J. Alternate guidance network for boundary-aware camouflaged object detection. Mach. Vis. Appl. 2023, 34, 69. [Google Scholar] [CrossRef]
- Sun, Y.; Wang, S.; Chen, C.; Xiang, T.Z. Boundary-guided camouflaged object detection. arXiv 2022, arXiv:2207.00794. [Google Scholar]
- Li, X.; Li, H.; Zhou, H.; Yu, M.; Chen, D.; Li, S.; Zhang, J. Camouflaged object detection with counterfactual intervention. Neurocomputing 2023, 553, 126530. [Google Scholar] [CrossRef]
- Sun, D.; Jiang, S.; Qi, L. Edge-Aware Mirror Network for Camouflaged Object Detection. In Proceedings of the 2023 IEEE International Conference on Multimedia and Expo (ICME), Brisbane, Australia, 10–14 July 2023; pp. 2465–2470. [Google Scholar]
- Zheng, D.; Zheng, X.; Yang, L.T.; Gao, Y.; Zhu, C.; Ruan, Y. Mffn: Multi-view feature fusion network for camouflaged object detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA, 2–7 January 2023; pp. 6232–6242. [Google Scholar]
- Song, Y.; Li, X.; Qi, L. Camouflaged Object Detection with Feature Grafting and Distractor Aware. In Proceedings of the 2023 IEEE International Conference on Multimedia and Expo (ICME), Brisbane, Australia, 10–14 July 2023; pp. 2459–2464. [Google Scholar]
- He, C.; Li, K.; Zhang, Y.; Tang, L.; Zhang, Y.; Guo, Z.; Li, X. Camouflaged object detection with feature decomposition and edge reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 17–24 June 2023; pp. 22046–22055. [Google Scholar]
- Jha, D.; Smedsrud, P.H.; Riegler, M.A.; Halvorsen, P.; De Lange, T.; Johansen, D.; Johansen, H.D. Kvasir-seg: A segmented polyp dataset. In Proceedings, Part II 26, Proceedings of the Multi Media Modeling: 26th International Conference, MMM 2020, Daejeon, Republic of Korea, 5–8 January 2020; Springer: Berlin/Heidelberg, Germany, 2020; pp. 451–462. [Google Scholar]
- Bernal, J.; Sánchez, F.J.; Fernández-Esparrach, G.; Gil, D.; Rodríguez, C.; Vilariño, F. WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Comput. Med. Imaging Graph. 2015, 43, 99–111. [Google Scholar] [CrossRef] [PubMed]
Method | Year | CHAMELEON | CAMO-Test | COD10K-Test | NC4K | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
↑ | ↑ | ↑ | M↓ | ↑ | ↑ | ↑ | M↓ | ↑ | ↑ | ↑ | M↓ | ↑ | ↑ | ↑ | M↓ | ||
SINet [9] | 2020 | 0.869 | 0.891 | 0.740 | 0.044 | 0.751 | 0.771 | 0.606 | 0.100 | 0.771 | 0.806 | 0.551 | 0.051 | 0.810 | 0.873 | 0.772 | 0.057 |
SINetV2 [66] | 2021 | 0.888 | 0.942 | 0.816 | 0.030 | 0.820 | 0.882 | 0.743 | 0.070 | 0.815 | 0.887 | 0.680 | 0.037 | 0.847 | 0.903 | 0.769 | 0.048 |
PFNet [17] | 2021 | 0.882 | 0.942 | 0.810 | 0.033 | 0.782 | 0.852 | 0.695 | 0.085 | 0.800 | 0.868 | 0.660 | 0.036 | 0.829 | 0.892 | 0.745 | 0.053 |
C2FNet [15] | 2021 | 0.888 | 0.932 | 0.828 | 0.032 | 0.796 | 0.864 | 0.719 | 0.080 | 0.813 | 0.890 | 0.686 | 0.036 | 0.838 | 0.898 | 0.762 | 0.049 |
LSR [10] | 2021 | 0.893 | 0.938 | 0.839 | 0.033 | 0.793 | 0.826 | 0.725 | 0.085 | 0.847 | 0.924 | 0.775 | 0.028 | 0.839 | 0.883 | 0.779 | 0.053 |
OSFormer [67] | 2022 | 0.897 | 0.931 | 0.839 | 0.028 | 0.801 | 0.859 | 0.769 | 0.071 | 0.811 | 0.881 | 0.701 | 0.034 | 0.833 | 0.887 | 0.792 | 0.047 |
TPRNet [68] | 2022 | 0.814 | 0.870 | 0.781 | 0.076 | 0.814 | 0.870 | 0.781 | 0.076 | 0.829 | 0.892 | 0.725 | 0.034 | 0.854 | 0.903 | 0.790 | 0.047 |
SegMaR [28] | 2022 | 0.897 | 0.950 | 0.835 | 0.027 | 0.815 | 0.872 | 0.742 | 0.071 | 0.833 | 0.899 | 0.724 | 0.034 | 0.841 | 0.905 | 0.781 | 0.046 |
BSANet [18] | 2022 | 0.895 | 0.946 | 0.841 | 0.027 | 0.796 | 0.851 | 0.717 | 0.079 | 0.818 | 0.891 | 0.699 | 0.034 | 0.841 | 0.897 | 0.771 | 0.048 |
DGNet [69] | 2022 | 0.890 | 0.934 | 0.816 | 0.029 | 0.839 | 0.901 | 0.769 | 0.057 | 0.822 | 0.911 | 0.693 | 0.033 | 0.857 | 0.907 | 0.784 | 0.042 |
BGNet [71] | 2022 | 0.885 | 0.942 | 0.815 | 0.032 | 0.812 | 0.870 | 0.749 | 0.073 | 0.831 | 0.901 | 0.722 | 0.033 | 0.843 | 0.901 | 0.764 | 0.048 |
ZoomNet [26] | 2022 | 0.902 | 0.952 | 0.845 | 0.023 | 0.820 | 0.883 | 0.752 | 0.066 | 0.838 | 0.893 | 0.729 | 0.029 | 0.853 | 0.907 | 0.784 | 0.043 |
AGNet [70] | 2023 | 0.900 | 0.952 | 0.864 | 0.027 | 0.808 | 0.859 | 0.783 | 0.075 | 0.835 | 0.896 | 0.760 | 0.033 | 0.852 | 0.900 | 0.816 | 0.046 |
CINet [72] | 2023 | 0.905 | 0.947 | 0.843 | 0.028 | 0.827 | 0.888 | 0.763 | 0.066 | 0.830 | 0.904 | 0.710 | 0.033 | 0.855 | 0.910 | 0.789 | 0.043 |
EAMNet [73] | 2023 | 0.899 | 0.942 | 0.855 | 0.023 | 0.831 | 0.890 | 0.763 | 0.064 | 0.839 | 0.907 | 0.733 | 0.029 | 0.862 | 0.916 | 0.801 | 0.040 |
MFFN [74] | 2023 | 0.905 | 0.963 | 0.852 | 0.021 | 0.829 | 0.881 | 0.793 | 0.062 | 0.846 | 0.917 | 0.745 | 0.028 | 0.856 | 0.915 | 0.791 | 0.042 |
FDNet [75] | 2023 | 0.909 | 0.947 | 0.856 | 0.025 | 0.836 | 0.886 | 0.777 | 0.066 | 0.857 | 0.918 | 0.763 | 0.028 | 0.865 | 0.911 | 0.803 | 0.042 |
FEDER [76] | 2023 | 0.903 | 0.947 | 0.856 | 0.026 | 0.836 | 0.897 | 0.807 | 0.066 | 0.844 | 0.911 | 0.748 | 0.029 | 0.862 | 0.913 | 0.824 | 0.042 |
HitNet [52] | 2023 | 0.915 | 0.962 | 0.875 | 0.020 | 0.844 | 0.902 | 0.801 | 0.057 | 0.868 | 0.932 | 0.798 | 0.024 | 0.870 | 0.921 | 0.825 | 0.039 |
CISNet | - | 0.916 | 0.970 | 0.877 | 0.019 | 0.846 | 0.908 | 0.808 | 0.056 | 0.879 | 0.938 | 0.812 | 0.021 | 0.880 | 0.931 | 0.837 | 0.034 |
No. | cross_level | adjacent_level | strip_conv | ordinary_conv | COD10K-Test | NC4K | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
↑ | ↑ | ↑ | M↓ | ↑ | ↑ | ↑ | M↓ | |||||
#1 | ✓ | 0.878 | 0.930 | 0.810 | 0.022 | 0.879 | 0.927 | 0.834 | 0.036 | |||
#2 | ✓ | 0.876 | 0.932 | 0.809 | 0.023 | 0.880 | 0.928 | 0.834 | 0.036 | |||
#3 | ✓ | ✓ | 0.873 | 0.930 | 0.804 | 0.023 | 0.878 | 0.927 | 0.831 | 0.036 | ||
#4 | ✓ | ✓ | 0.876 | 0.933 | 0.809 | 0.022 | 0.880 | 0.928 | 0.832 | 0.036 | ||
#5 | ✓ | ✓ | 0.878 | 0.931 | 0.811 | 0.022 | 0.880 | 0.930 | 0.836 | 0.034 | ||
#OUR | ✓ | ✓ | 0.879 | 0.938 | 0.812 | 0.021 | 0.880 | 0.931 | 0.837 | 0.034 |
No. | EGA | LossNet | Iteration | COD10K-Test | NC4K | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
↑ | ↑ | ↑ | M↓ | ↑ | ↑ | ↑ | M↓ | ||||
#6 | ✓ | ✓ | 0.873 | 0.884 | 0.781 | 0.023 | 0.878 | 0.914 | 0.821 | 0.037 | |
#7 | ✓ | ✓ | 0.871 | 0.911 | 0.787 | 0.023 | 0.878 | 0.919 | 0.821 | 0.037 | |
#8 | ✓ | ✓ | 0.876 | 0.932 | 0.807 | 0.022 | 0.880 | 0.929 | 0.832 | 0.036 | |
#OUR | ✓ | ✓ | ✓ | 0.879 | 0.938 | 0.812 | 0.021 | 0.880 | 0.931 | 0.837 | 0.034 |
No. | Channels in MSCSub | COD10K-Test | NC4K | ||||||
---|---|---|---|---|---|---|---|---|---|
↑ | ↑ | ↑ | M↓ | ↑ | ↑ | ↑ | M↓ | ||
#9 | 32 | 0.878 | 0.934 | 0.811 | 0.022 | 0.880 | 0.927 | 0.834 | 0.036 |
#10 | 128 | 0.879 | 0.935 | 0.813 | 0.022 | 0.881 | 0.931 | 0.838 | 0.034 |
#11 | 256 | 0.883 | 0.939 | 0.820 | 0.020 | 0.884 | 0.932 | 0.840 | 0.033 |
#OUR | 64 | 0.879 | 0.938 | 0.812 | 0.021 | 0.880 | 0.931 | 0.837 | 0.034 |
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Hu, T.; Zhang, C.; Lyu, X.; Sun, X.; Chen, S.; Zeng, T.; Chen, J. A Cross-Level Iterative Subtraction Network for Camouflaged Object Detection. Appl. Sci. 2024, 14, 8063. https://doi.org/10.3390/app14178063
Hu T, Zhang C, Lyu X, Sun X, Chen S, Zeng T, Chen J. A Cross-Level Iterative Subtraction Network for Camouflaged Object Detection. Applied Sciences. 2024; 14(17):8063. https://doi.org/10.3390/app14178063
Chicago/Turabian StyleHu, Tongtong, Chao Zhang, Xin Lyu, Xiaowen Sun, Shangjing Chen, Tao Zeng, and Jiale Chen. 2024. "A Cross-Level Iterative Subtraction Network for Camouflaged Object Detection" Applied Sciences 14, no. 17: 8063. https://doi.org/10.3390/app14178063