research-article

Open access

Frequency Perception Network for Camouflaged Object Detection

Authors:

Yao ZhaoAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 1179 - 1189

https://doi.org/10.1145/3581783.3612083

Published: 27 October 2023 Publication History

Abstract

Camouflaged object detection (COD) aims to accurately detect objects hidden in the surrounding environment. However,the existing COD methods mainly locate camouflaged objects in the RGB domain, their performance has not been fully exploited in many challenging scenarios. Considering that the features of the camouflaged object and the background are more discriminative in the frequency domain, we propose a novel learnable and separable frequency perception mechanism driven by the semantic hierarchy in the frequency domain. Our entire network adopts a two-stage model, including a frequency-guided coarse localization stage and a detail-preserving fine localization stage.With the multi-level features extracted by the backbone, we design a flexible frequency perception module based on octave convolution for coarse positioning. Then, we design the correction fusion module to step-by-step integrate the high-level features through the prior-guided correction and cross-layer feature channel association, and finally combine them with the shallow features to achieve the detailed correction of the camouflaged objects. Compared with the currently existing models, our proposed method achieves competitive performance in three popular benchmark datasets both qualitatively and quantitatively. The code will be released at https://github.com/rmcong/FPNet_ACMMM23.

References

[1]

Nagappa U Bhajantri and P Nagabhushan. 2006. Camouflage defect identification: A novel approach. In 9th International Conference on Information Technology (ICIT). 145--148.

Digital Library

[2]

Geng Chen, Si-Jie Liu, Yu-Jia Sun, Ge-Peng Ji, Ya-Feng Wu, and Tao Zhou. 2022. Camouflaged object detection via context-aware cross-level fusion. IEEE Transactions on Circuits and Systems for Video Technology, Vol. 32, 10 (2022), 6981--6993.

[3]

Rui Chen, Fei Li, Ying Tong, Minghu Wu, and Yang Jiao. 2023. A weighted block cooperative sparse representation algorithm based on visual saliency dictionary. CAAI Transactions on Intelligence Technology, Vol. 8, 1 (2023), 235--246.

Digital Library

[4]

Yunpeng Chen, Haoqi Fan, Bing Xu, Zhicheng Yan, Yannis Kalantidis, Marcus Rohrbach, Shuicheng Yan, and Jiashi Feng. 2019. Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution. In IEEE/CVF International Conference on Computer Vision (ICCV). 3435--3444.

[5]

Zuyao Chen, Qianqian Xu, Runmin Cong, and Qingming Huang. 2020. Global context-aware progressive aggregation network for salient object detection. In Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI). 10599--10606.

[6]

Runmin Cong, Ke Huang, Jianjun Lei, Yao Zhao, Qingming Huang, and Sam Kwong. early access. Multi-projection fusion and refinement network for salient object detection in 360° omnidirectional image. IEEE Transactions on Neural Networks and Learning Systems ( early access.

[7]

Runmin Cong, Jianjun Lei, Huazhu Fu, Ming-Ming Cheng, Weisi Lin, and Qingming Huang. 2019. Review of visual saliency detection with comprehensive information. IEEE Transactions on Circuits and Systems for Video Technology, Vol. 29, 10 (2019), 2941--2959.

[8]

Runmin Cong, Qinwei Lin, Chen Zhang, Chongyi Li, Xiaochun Cao, Qingming Huang, and Yao Zhao. 2022a. CIR-Net: Cross-modality interaction and refinement for RGB-D salient object detection. IEEE Transactions on Image Processing, Vol. 31 (2022), 6800--6815.

Digital Library

[9]

Runmin Cong, Qi Qin, Chen Zhang, Qiuping Jiang, Shiqi Wang, Yao Zhao, and Sam Kwong. 2023 a. A weakly supervised learning framework for salient object detection via hybrid labels. IEEE Transactions on Circuits and Systems for Video Technology, Vol. 33, 2 (2023), 534--548.

[10]

Runmin Cong, Weiyu Song, Jianjun Lei, Guanghui Yue, Yao Zhao, and Sam Kwong. 2023 b. PSNet: Parallel symmetric network for video salient object detection. IEEE Trans. Emerg. Topics Comput. Intell., Vol. 7, 2 (2023), 402--414.

[11]

Runmin Cong, Haowei Yang, Qiuping Jiang, Wei Gao, Hai-Sheng Li, Cong Wang, Yao Zhao, and Sam Kwong. 2022b. BCS-Net: Boundary, context, and semantic for automatic COVID-19 lung infection segmentation from CT images. IEEE Transactions on Instrumentation and Measurement, Vol. 71 (2022), 1--11.

[12]

Runmin Cong, Ning Yang, Chongyi Li, Huazhu Fu, Yao Zhao, Qingming Huang, and Sam Kwong. 2023 c. Global-and-local collaborative learning for co-Salient object detection. IEEE Transactions on Cybernetics, Vol. 53, 3 (2023), 1920--1931.

[13]

Runmin Cong, Kepu Zhang, Chen Zhang, Feng Zheng, Yao Zhao, Qingming Huang, and Sam Kwong. early access. Does Thermal really always matter for RGB-T salient object detection? IEEE Transactions on Multimedia (early access.

Digital Library

[14]

Runmin Cong, Yumo Zhang, Ning Yang, Haisheng Li, Xueqi Zhang, Ruochen Li, Zewen Chen, Yao Zhao, and Sam Kwong. 2022c. Boundary guided semantic larning for real-time COVID-19 lung infection segmentation system. IEEE Transactions on Consumer Electronics, Vol. 68, 4 (2022), 376--386.

Digital Library

[15]

Michelle Dean, Robin Harwood, and Connie Kasari. 2017. The art of camouflage: Gender differences in the social behaviors of girls and boys with autism spectrum disorder. Autism, Vol. 21, 6 (2017), 678--689.

[16]

Fan Deng-Ping, J Ge-Pen, Cheng Ming-Ming, and Shao Ling. 2022. Concealed object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, 10 (2022), 6024--6042.

Digital Library

[17]

Max Ehrlich and Larry S Davis. 2019. Deep residual learning in the JPEG transform domain. In IEEE/CVF International Conference on Computer Vision (ICCV). 3484--3493.

[18]

Deng-Ping Fan, Ming-Ming Cheng, Yun Liu, Tao Li, and Ali Borji. 2017. Structure-measure: A new way to evaluate foreground maps. In IEEE International Conference on Computer Vision (ICCV). 4548--4557.

[19]

Deng-Ping Fan, Cheng Gong, Yang Cao, Bo Ren, Ming-Ming Cheng, and Ali Borji. 2018. Enhanced-alignment measure for binary foreground map evaluation. arXiv preprint arXiv:1805.10421 (2018).

[20]

Deng-Ping Fan, Ge-Peng Ji, Guolei Sun, Ming-Ming Cheng, Jianbing Shen, and Ling Shao. 2020a. Camouflaged object detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2777--2787.

[21]

Deng-Ping Fan, Ge-Peng Ji, Tao Zhou, Geng Chen, Huazhu Fu, Jianbing Shen, and Ling Shao. 2020b. PraNet: Parallel reverse attention network for polyp segmentation. In Medical Image Computing and Computer Assisted Intervention (MICCAI). 263--273.

[22]

Deng-Ping Fan, Tao Zhou, Ge-Peng Ji, Yi Zhou, Geng Chen, Huazhu Fu, Jianbing Shen, and Ling Shao. 2020c. Inf-Net: Automatic COVID-19 lung infection segmentation from CT images. IEEE Transactions on Medical Imaging, Vol. 39, 8 (2020), 2626--2637.

[23]

Ranran Feng and Balakrishnan Prabhakaran. 2013. Facilitating fashion camouflage art. In ACM International Conference on Multimedia (ACM MM). 793--802.

Digital Library

[24]

Lionel Gueguen, Alex Sergeev, Ben Kadlec, Rosanne Liu, and Jason Yosinski. 2018. Faster neural networks straight from JPEG. Advances in Neural Information Processing Systems (NIPS), Vol. 31 (2018).

[25]

Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick. 2017. Mask R-CNN. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 42 (2017), 386--397.

[26]

Jianqin Yin Yanbin Han Wendi Hou and Jinping Li. 2011. Detection of the mobile object with camouflage color under dynamic background based on optical flow. Procedia Engineering, Vol. 15 (2011), 2201--2205.

[27]

Iván Huerta, Daniel Rowe, Mikhail Mozerov, and Jordi Gonzàlez. 2007. Improving background subtraction based on a casuistry of colour-motion segmentation problems. In Third Iberian Conference on Pattern Recognition and Image Analysis. 475--482.

Digital Library

[28]

Dong Jing, Shuo Zhang, Runmin Cong, and Youfang Lin. 2021. Occlusion-aware bi-directional guided network for light field salient object detection. In ACM International Conference on Multimedia (ACM MM). 1692--1701.

Digital Library

[29]

Ch Kavitha, B Prabhakara Rao, and A Govardhan. 2011. An efficient content based image retrieval using color and texture of image sub blocks. International Journal of Engineering Science and Technology, Vol. 3, 2 (2011), 1060--1068.

[30]

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2017. Imagenet classification with deep convolutional neural networks. Commun. ACM, Vol. 60, 6 (2017), 84--90.

Digital Library

[31]

Trung-Nghia Le, Tam V Nguyen, Zhongliang Nie, Minh-Triet Tran, and Akihiro Sugimoto. 2019. Anabranch network for camouflaged object segmentation. Computer Vision and Image Understanding, Vol. 184 (2019), 45--56.

Digital Library

[32]

Chongyi Li, Runmin Cong, Sam Kwong, Junhui Hou, Huazhu Fu, Guopu Zhu, Dingwen Zhang, and Qingming Huang. 2021. ASIF-Net: Attention steered interweave fusion network for RGB-D salient object detection. IEEE Transactions on Cybernetics, Vol. 50, 1 (2021), 88--100.

[33]

Chongyi Li, Runmin Cong, Yongri Piao, Qianqian Xu, and Chen Change Loy. 2020. RGB-D salient object detection with cross-modality modulation and selection. In European Conference on Computer Vision (ECCV). 225--241.

Digital Library

[34]

Tsung-Yi Lin, Piotr Dollár, Ross B. Girshick, Kaiming He, Bharath Hariharan, and Serge J. Belongie. 2016. Feature pyramid networks for object detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016), 936--944.

[35]

Jiawei Liu, Jing Zhang, and Nick Barnes. 2021b. Confidence-aware learning for camouflaged object detection. arXiv preprint arXiv:2106.11641 (2021).

[36]

Songtao Liu, Di Huang, et al. 2018. Receptive field block net for accurate and fast object detection. In European Conference on Computer Vision (ECCV). 385--400.

Digital Library

[37]

Zhou Liu, Kaiqi Huang, and Tieniu Tan. 2012. Foreground object detection using top-down information based on EM framework. IEEE Transactions on Image Processing, Vol. 21, 9 (2012), 4204--4217.

Digital Library

[38]

Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. 2021a. Swin Transformer: Hierarchical vision transformer using shifted windows. In IEEE/CVF International Conference on Computer Vision (ICCV). 10012--10022.

[39]

Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Nick Barnes, and Deng-Ping Fan. 2023. Towards Deeper Understanding of Camouflaged Object Detection. IEEE Transactions on Circuits and Systems for Video Technology (2023).

Digital Library

[40]

Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Bowen Liu, Nick Barnes, and Deng-Ping Fan. 2021. Simultaneously localize, segment and rank the camouflaged objects. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 11591--11601.

[41]

Yuxin Mao, Jing Zhang, Zhexiong Wan, Yuchao Dai, Aixuan Li, Yun-Qiu Lv, Xinyu Tian, Deng-Ping Fan, and Nick Barnes. 2021. Transformer transforms salient object detection and camouflaged object detection. ArXiv, Vol. abs/2104.10127 (2021).

[42]

Ran Margolin, Lihi Zelnik-Manor, and Ayellet Tal. 2014. How to evaluate foreground maps?. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 248--255.

Digital Library

[43]

Haiyang Mei, Ge-Peng Ji, Ziqi Wei, Xin Yang, Xiaopeng Wei, and Deng-Ping Fan. 2021. Camouflaged object segmentation with distraction mining. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 8772--8781.

[44]

Andrew Owens, Connelly Barnes, Alex Flint, Hanumant Singh, and William Freeman. 2014. Camouflaging an object from many viewpoints. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2782--2789.

Digital Library

[45]

Youwei Pang, Xiaoqi Zhao, Tian-Zhu Xiang, Lihe Zhang, and Huchuan Lu. 2022. Zoom in and out: A mixed-scale triplet network for camouflaged object detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2160--2170.

[46]

Przemysław Skurowski, Hassan Abdulameer, J Błaszczyk, Tomasz Depta, Adam Kornacki, and P Kozieł. 2018. Animal camouflage analysis: Chameleon database. (2018). https://www.polsl.pl/rau6/chameleon-database-animal-camouflage-analysis/

[47]

Yujia Sun, Geng Chen, Tao Zhou, Yi Zhang, and Nian Liu. 2021. Context-aware cross-level fusion network for camouflaged object detection. In Thirtieth International Joint Conference on Artificial Intelligence (IJCAI). 1025--1031.

[48]

Yujia Sun, Shuo Wang, Chenglizhao Chen, and Tian-Zhu Xiang. 2022. Boundary-guided camouflaged object detection. arXiv preprint arXiv:2207.00794 (2022).

[49]

Ariel Tankus and Yehezkel Yeshurun. 2001. Convexity-based visual camouflage breaking. Computer Vision and Image Understanding, Vol. 82, 3 (2001), 208--237.

Digital Library

[50]

Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao. 2021. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. In IEEE/CVF International Conference on Computer Vision (ICCV). 568--578.

[51]

Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao. 2022. PVT v2: Improved baselines with Pyramid Vision Transformer. Computational Visual Media, Vol. 8, 3 (2022), 415--424.

[52]

Jun Wei, Shuhui Wang, and Qingming Huang. 2020. F3Net: Fusion, feedback and focus for salient object detection. In AAAI conference on artificial intelligence, Vol. 34. 12321--12328.

[53]

Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon. 2018. CBAM: Convolutional block attention module. In European Conference on Computer Vision (ECCV). 3--19.

Digital Library

[54]

Zhe Wu, Li Su, and Qingming Huang. 2019. Cascaded partial decoder for fast and accurate salient object detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 3907--3916.

[55]

Fan Yang, Qiang Zhai, Xin Li, Rui Huang, Ao Luo, Hong Cheng, and Deng-Ping Fan. 2021. Uncertainty-guided transformer reasoning for camouflaged object detection. In IEEE/CVF International Conference on Computer Vision (ICCV). 4146--4155.

[56]

Zhiyong Yang, Qianqian Xu, Shilong Bao, Xiaochun Cao, and Qingming Huang. 2022. Learning with multiclass AUC: Theory and algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, 11 (2022), 7747--7763.

[57]

Zhiyong Yang, Qianqian Xu, Shilong Bao, Yuan He, Xiaochun Cao, and Qingming Huang. 2023. Optimizing two-way partial AUC with an end-to-end framework. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, 8 (2023), 10228--10246.

Digital Library

[58]

Guanghui Yue, Wanwan Han, Bin Jiang, Tianwei Zhou, Runmin Cong, and Tianfu Wang. 2022. Boundary constraint network with cross layer feature integration for polyp segmentation. IEEE Journal of Biomedical and Health Informatics, Vol. 26, 8 (2022), 4090--4099.

[59]

Chen Zhang, Runmin Cong, Qinwei Lin, Lin Ma, Feng Li, Yao Zhao, and Sam Kwong. 2021. Cross-modality discrepant interaction network for RGB-D salient object detection. In ACM International Conference on Multimedia (ACM MM). 2094--2102.

Digital Library

[60]

Qijian Zhang, Runmin Cong, Junhui Hou, Chongyi Li, and Yao Zhao. 2020. CoADNet: Collaborative aggregation-and-distribution networks for co-salient object detection. In Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS). 6959--6970.

[61]

Yijie Zhong, Bo Li, Lv Tang, Senyun Kuang, Shuang Wu, and Shouhong Ding. 2022. Detecting camouflaged object in frequency domain. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 4504--4513.

Cited By

He ZXia CQiao SLi JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Text-prompt Camouflaged Instance Segmentation with Graduated Camouflage LearningProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681132(5584-5593)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681132
Niu YYang LXu RLi YChen YCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)MiNet: Weakly-Supervised Camouflaged Object Detection through Mutual Interaction between Region and Edge CuesProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680891(6316-6325)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680891
Liang WWu JWu YMu XXu J(2024)FINet: Frequency Injection Network for Lightweight Camouflaged Object DetectionIEEE Signal Processing Letters10.1109/LSP.2024.335641631(526-530)Online publication date: 2024
https://doi.org/10.1109/LSP.2024.3356416
Show More Cited By

Index Terms

Frequency Perception Network for Camouflaged Object Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene understanding

Recommendations

Frequency Representation Integration for Camouflaged Object Detection
MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Recent camouflaged object detection (COD) approaches have been proposed to accurately segment objects blended into surroundings. The most challenging and critical issue in COD is to find out the lines of demarcation between objects and background in the ...
Frequency-aware Camouflaged Object Detection
Camouflaged object detection (COD) is important as it has various potential applications. Unlike salient object detection (SOD), which tries to identify visually salient objects, COD tries to detect objects that are visually very similar to the ...
Key Object Detection: Unifying Salient and Camouflaged Object Detection Into One Task
Pattern Recognition and Computer Vision
Abstract
Visual salient object detection (SOD) aims to discover eye-catching salient objects, while camouflaged object detection (COD) seeks to segment objects that are visually hidden in their surrounding environment. In this paper, considering the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Taishan Scholar Project of Shandong Province
National Natural Science Foundation of China
CAAI-Huawei MindSpore Open Fund
National Key R&D Program of China
Project for Self-Developed Innovation Team of Jinan City
Young Elite Scientist Sponsorship Program by the China Association for Science and Technology
China Postdoctoral Science Foundation

Conference

MM '23

Sponsor:

SIGMM

MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

Ottawa ON, Canada

Acceptance Rates

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
984
Total Downloads

Downloads (Last 12 months)968
Downloads (Last 6 weeks)104

Reflects downloads up to 22 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

He ZXia CQiao SLi JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Text-prompt Camouflaged Instance Segmentation with Graduated Camouflage LearningProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681132(5584-5593)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681132
Niu YYang LXu RLi YChen YCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)MiNet: Weakly-Supervised Camouflaged Object Detection through Mutual Interaction between Region and Edge CuesProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680891(6316-6325)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680891
Liang WWu JWu YMu XXu J(2024)FINet: Frequency Injection Network for Lightweight Camouflaged Object DetectionIEEE Signal Processing Letters10.1109/LSP.2024.335641631(526-530)Online publication date: 2024
https://doi.org/10.1109/LSP.2024.3356416
Luo ZWang YChen LYang W(2024)Frequency Spectrum Features Modeling for Real-Time Tiny Object Detection in Remote Sensing ImageIEEE Geoscience and Remote Sensing Letters10.1109/LGRS.2024.341282421(1-5)Online publication date: 2024
https://doi.org/10.1109/LGRS.2024.3412824
Yang JZhang QZhao YLi YLiu Z(2024)Bi-directional Boundary-object interaction and refinement network for Camouflaged Object Detection2024 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME57554.2024.10687766(1-6)Online publication date: 15-Jul-2024
https://doi.org/10.1109/ICME57554.2024.10687766
Hui WZhu ZZheng SZhao Y(2024)Endow SAM with Keen Eyes: Temporal-Spatial Prompt Learning for Video Camouflaged Object Detection2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.01803(19058-19067)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.01803
Ge YZhong YRen JHe MBi HZhang Q(2024)Camouflaged Object Detection via location-awareness and feature fusionImage and Vision Computing10.1016/j.imavis.2024.105339152(105339)Online publication date: Dec-2024
https://doi.org/10.1016/j.imavis.2024.105339
Gao SLi YLuo H(2024)Detecting thyroid nodules along with surrounding tissues and tracking nodules using motion prior in ultrasound videosComputerized Medical Imaging and Graphics10.1016/j.compmedimag.2024.102439117(102439)Online publication date: Oct-2024
https://doi.org/10.1016/j.compmedimag.2024.102439
Zhao XPang YZhang LLu HZhang L(2024)Towards Diverse Binary Segmentation via a Simple yet General Gated NetworkInternational Journal of Computer Vision10.1007/s11263-024-02058-y132:10(4157-4234)Online publication date: 7-May-2024
https://doi.org/10.1007/s11263-024-02058-y
Du HZhang MZhang WQiao K(2024)Mscnet: Mask stepwise calibration network for camouflaged object detectionThe Journal of Supercomputing10.1007/s11227-024-06376-380:16(24718-24737)Online publication date: 27-Jul-2024
https://dl.acm.org/doi/10.1007/s11227-024-06376-3
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents