Abstract
This paper introduces deep gradient network (DGNet), a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD). It decouples the task into two connected branches, i.e., a context and a texture encoder. The essential connection is the gradient-induced transition, representing a soft grouping between context and texture features. Benefiting from the simple but efficient framework, DGNet outperforms existing state-of-the-art COD models by a large margin. Notably, our efficient version, DGNet-S, runs in real-time (80 fps) and achieves comparable results to the cutting-edge model JCSOD-CVPR21 with only 6.82% parameters. The application results also show that the proposed DGNet performs well in the polyp segmentation, defect detection, and transparent object segmentation tasks. The code will be made available at https://github.com/GewelsJI/DGNet.
References
D. P. Fan, G. P. Ji, G. L. Sun, M. M. Cheng, J. B. Shen, L. Shao. Camouflaged object detection. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Seattle, USA, pp. 2774–2784, 2020. DOI: https://doi.org/10.1109/CVPR42600.2020.00285.
D. P. Fan, G. P. Ji, M. M. Cheng, L. Shao. Concealed object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, to be published. DOI: https://doi.org/10.1109/TPAMI.2021.3085766.
D. P. Fan, G. P. Ji, T. Zhou, G. Chen, H. Fu, J. Shen, L. Shao. PraNet: Parallel reverse attention network for polyp segmentation. In Proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Lima, Peru, pp. 263–273, 2020. DOI: https://doi.org/10.1007/978-3-030-59725-2_26.
G. P. Ji, Y. C. Chou, D. P. Fan, G. Chen, H. Z. Fu, D. Jha, L. Shao. Progressively normalized self-attention network for video polyp segmentation. In Proceedings of the 24th International Conference on Medical Image Computing and Computer-assisted Intervention, Springer, Strasbourg, France, pp. 142–152, 2021. DOI: https://doi.org/10.1007/978-3-030-87193-2_14.
G. P. Ji, G. Xiao, Y. C. Chou, D. P. Fan, K. Zhao, G. Chen, L. Van Gool. Video polyp segmentation: A deep learning perspective. Machine Intelligence Research, to be published. DOI: https://doi.org/10.1007/s11633-022-1371-y.
D. P. Fan, T. Zhou, G. P. Ji, Y. Zhou, G. Chen, H. Z. Fu, J. B. Shen, L. Shao. Inf-Net: Automatic COVID-19 lung infection segmentation from CT images. IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2626–2637, 2020. DOI: https://doi.org/10.1109/TMI.2020.2996645.
Y. H. Wu, S. H. Gao, J. Mei, J. Xu, D. P. Fan, R. G. Zhang, M. M. Cheng. JCS: An explainable COVID-19 diagnosis system by joint classification and segmentation. IEEE Transactions on Image Processing, vol. 30, pp. 3113–3126, 2021. DOI: https://doi.org/10.1109/TIP.2021.3058783.
J. N. Liu, B. Dong, S. Wang, H. Cui, D. P. Fan, J. Q. Ma, G. Chen. COVID-19 lung infection segmentation with a novel two-stage cross-domain transfer learning framework. Medical Image Analysis, vol. 74, Article number 102205, 2021. DOI: https://doi.org/10.1016/j.media.2021.102205.
G. P. Ji, K. R. Fu, Z. Wu, D. P. Fan, J. B. Shen, L. Shao. Full-duplex strategy for video object segmentation. In Proceedings of IEEE/CVF International Conference on Computer Vision, IEEE, Montreal, Canada, pp. 4902–4913, 2021. DOI: https://doi.org/10.1109/ICCV48922.2021.00488.
W. C. Chen, X. Y. Yu, L. L. Ou. Pedestrian attribute recognition in video surveillance scenarios based on view-attribute attention localization. Machine Intelligence Research, vol. 19, no. 2, pp. 153–168, 2022. DOI: https://doi.org/10.1007/s11633-022-1321-8.
J. R. Xue, J. W. Fang, P. Zhang. A survey of scene understanding by event reasoning in autonomous driving. International Journal of Automation and Computing, vol. 15, no. 3, pp. 249–266, 2018. DOI: https://doi.org/10.1007/s11633-018-1126-y.
R. R. Feng, B. Prabhakaran. Facilitating fashion camouflage art. In Proceedings of the 21st ACM International Conference on Multimedia, ACM, Barcelona, Spain, pp. 793–802, 2013. DOI: https://doi.org/10.1145/2502081.2502121.
M. Dean, R. Harwood, C. Kasari. The art of camouflage: Gender differences in the social behaviors of girls and boys with autism spectrum disorder. Autism, vol. 21, no. 6, pp. 678–689, 2017. DOI: https://doi.org/10.1177/1362361316671845.
H. Y. Mei, G. P. Ji, Z. Q. Wei, X. Yang, X. P. Wei, D. P. Fan. Camouflaged object segmentation with distraction mining. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, pp. 8768–8777, 2021. DOI: https://doi.org/10.1109/CVPR46437.2021.00866.
Y. Q. Lv, J. Zhang, Y. C. Dai, A. X. Li, B. W. Liu, N. Barnes, D. P. Fan. Simultaneously localize, segment and rank the camouflaged objects. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Nashville, USA, pp.11586–11596, 2021. DOI: https://doi.org/10.1109/CVPR46437.2021.01142.
Q. Jia, S. L. Yao, Y. Liu, X. Fan, R. S. Liu, Z. X. Luo. Segment, magnify and reiterate: Detecting camouflaged objects the hard way. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, New Orleans, USA, pp. 4713–1722, 2022.
Y. J. Zhong, B. Li, L. Tang, S. Y. Kuang, S. Wu, S. H. Ding. Detecting camouflaged object in frequency domain. In Proceedings of Conference on Computer Vision and Pattern Recognition, IEEE, New Orleans, USA, pp. 4504–4513, 2022.
Q. Zhai, X. Li, F. Yang, C. Chen, H. Cheng, D. P. Fan. Mutual graph learning for camouflaged object detection. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Nashville, USA, pp. 12992–13002, 2021. DOI: https://doi.org/10.1109/CVPR46437.2021.01280.
G. P. Ji, L. Zhu, M. C. Zhuge, K. R. Fu. Fast camouflaged object detection via edge-based reversible re-calibration network. Pattern Recognition, vol. 123, Article number 108414, 2022. DOI: https://doi.org/10.1016/j.patcog.2021.108414.
H. W. Zhu, P. Li, H. R. Xie, X. F. Yan, D. Liang, D. P. Chen, M. Q. Wei, J. Qin. I can find you! Boundary-guided separated attention network for camouflaged object detection. In Proceedings of the 36th AAAI Conference on Artificial Intelligence, online, pp. 3608–3616, 2022. DOI: https://doi.org/10.1609/aaai.v36i3.20273.
F. Yang, Q. Zhai, X. Li, R. Huang, A. Luo, H. Cheng, D. P. Fan. Uncertainty-guided transformer reasoning for camouflaged object detection. In Proceedings of IEEE/CVF International Conference on Computer Vision, IEEE, Montreal, Canada, pp. 4126–4135, 2021. DOI: https://doi.org/10.1109/IC-CV48922.2021.00411.
A. X. Li, J. Zhang, Y. Q. Lv, B. W. Liu, T. Zhang, Y. C. Dai. Uncertainty-aware joint salient object and camouflaged object detection. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Nashville, USA, pp. 10066–10076, 2021. DOI: https://doi.org/10.1109/CVPR46437.2021.00994.
Y. X. Pan, Y. W. Chen, Q. Fu, P. Zhang, X. Xu. Study on the camouflaged target detection method based on 3D convexity. Modern Applied Science, vol. 5, no. 4, pp. 152–157, 2011. DOI: https://doi.org/10.5539/mas.v5n4p152.
P. Sengottuvelan, A. Wahi, A. Shanmugam. Performance of decamouflaging through exploratory image analysis. In Proceedings of the 1st International Conference on Emerging Trends in Engineering and Technology, IEEE, Nagpur, India, pp. 6–10, 2008. DOI: https://doi.org/10.1109/ICETET.2008.232.
Z. Liu, K. Q. Huang, T. N. Tan. Foreground object detection using top-down information based on em framework. IEEE Transactions on Image Processing, vol. 21, no. 9, pp. 4204–4217, 2012. DOI: https://doi.org/10.1109/TIP.2012.2200492.
J. Q. Yin, Y. B. Han, W. D. Hou, J. P. Li. Detection of the mobile object with camouflage color under dynamic background based on optical flow. Procedia Engineering, vol. 15, pp. 2201–2205, 2011. DOI: https://doi.org/10.1016/j.proeng.2011.08.412.
J. Gallego, P. Bertolino. Foreground object segmentation for moving camera sequences based on foreground-background probabilistic models and prior probability maps. In Proceedings of IEEE International Conference on Image Processing, Paris, France, pp. 3312–3316, 2014. DOI: https://doi.org/10.1109/ICIP.2014.7025670.
Y. J. Sun, G. Chen, T. Zhou, Y. Zhang, N. Liu. Context-aware cross-level fusion network for camouflaged object detection. In Proceedings of the 30th International Joint Conference on Artificial Intelligence, Montreal, Canada, pp. 1025–1031, 2021. DOI: https://doi.org/10.24963/ijcai.2021/142.
G. Chen, S. J. Liu, Y. J. Sun, G. P. Ji, Y. F. Wu, T. Zhou. Camouflaged object detection via context-aware cross-level fusion. IEEE Transactions on Circuits and Systems for Video Technology, to be published. DOI: https://doi.org/10.1109/TC-SVT.2022.3178173.
J. J. Ren, X. W. Hu, L. Zhu, X. M. Xu, Y. Y. Xu, W. M. Wang, Z. J. Deng, P. A. Heng. Deep texture-aware features for camouflaged object detection. IEEE Transactions on Circuits and Systems for Video Technology, to be published. DOI: https://doi.org/10.1109/TCSVT.2021.3126591.
N. Kajiura, H. Liu, S. Satoh. Improving camouflaged object detection with the uncertainty of pseudo-edge labels. In Proceedings of ACM Multimedia Asia, Gold Coast, Australia, pp. 7, 2021. DOI: https://doi.org/10.1145/3469877.3490587.
M. C. Zhuge, X. K. Lu, Y. Y. Guo, Z. H. Cai, S. H. Chen. CubeNet: X-shape connection for camouflaged object detection. Pattern Recognition, vol. 127, Article number 108644, 2022. DOI: https://doi.org/10.1016/j.patcog.2022.108644.
T. N. Le, T. V. Nguyen, Z. L. Nie, M. T. Tran, A. Sugimoto. Anabranch network for camouflaged object segmentation. Computer Vision and Image Understanding, vol. 184, pp. 45–56, 2019. DOI: https://doi.org/10.1016/j.cviu.2019.04.006.
Y. Pang, X. Zhao, T. Z. Xiang, L. Zhang, H. Lu. Zoom in and out: A mixed-scale triplet network for camouflaged object detection. In Proceedings of Conference on Computer Vision and Pattern Recognition, IEEE, New Orleans, USA, pp. 2160–2170, 2022.
Y. X. Mao, J. Zhang, Z. X. Wan, Y. C. Dai, A. X. Li, Y. Q. Lv, X. Y. Tian, D. P. Fan, N. Barnes. Transformer transforms salient object detection and camouflaged object detection, [Online], Available: https://arxiv.org/abs/2104.10127v1, 2021.
X. L. Cheng, H. Xiong, D. P. Fan, Y. R. Zhong, M. Harandi, T. Drummond, Z. Y. Ge. Implicit motion handling for video camouflaged object detection. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, New Orleans, USA, pp. 13864–13873, 2022.
J. C. Zhu, X. Y. Zhang, S. Zhang, J. N. Liu. Inferring camouflaged objects by texture-aware interactive guidance network. Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 4, pp. 3599–3607, 2021.
T. Y. Lin, P. Dollàr, R. Girshick, K. M. He, B. Hariharan, S. Belongie. Feature pyramid networks for object detection. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, pp. 936–944, 2017. DOI: https://doi.org/10.1109/CVPR.2017.106.
Z. H. Ke, J. Y. Sun, K. C. Li, Q. Yan, R. W. H. Lau. MODNet: Real-time trimap-free portrait matting via objective decomposition. Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 1, pp. 1140–1147, 2022. DOI: https://doi.org/10.1609/aaai.v36i1.19999.
M. X. Tan, Q. V. Le. EfficientNet: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning, Long Beach, USA, pp. 6105–6114, 2019.
S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the 32nd International Conference on Machine Learning, Lille, France, pp. 448–456, 2015.
X. Glorot, A. Bordes, Y. Bengio. Deep sparse rectifier neural networks. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, USA, pp. 315–323, 2011.
Z. Su, W. Z. Liu, Z. T. Yu, D. W. Hu, Q. Liao, Q. Tian, M. Pietikäinen, L. Liu. Pixel difference networks for efficient edge detection. In Proceedings of IEEE/CVF International Conference on Computer Vision, IEEE, Montreal, Canada, pp. 5097–5107, 2021. DOI: https://doi.org/10.1109/ICCV48922.2021.00507.
C. Ma, Y. M. Rao, Y. A. Cheng, C. Chen, J. W. Lu, J. Zhou. Structure-preserving super resolution with gradient guidance. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Seattle, USA, pp.7766–7775, 2020. DOI: https://doi.org/10.1109/CVPR42600.2020.00779.
J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-8, no. 6, pp. 679–698, 1986. DOI: https://doi.org/10.1109/TPAMI.1986.4767851.
L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A. L. Yuille. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 4, pp. 834–848, 2018. DOI: https://doi.org/10.1109/TPAMI.2017.2699184.
K. M. He, X. Y. Zhang, S. Q. Ren, J. Sun. Deep residual learning for image recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, pp. 770–778, 2016. DOI: https://doi.org/10.1109/CVPR.2016.90.
J. Wei, S. H. Wang, Q. M. Huang. F.3Net: Fusion, feedback and focus for salient object detection. Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 7, pp. 12321–12328, 2020. DOI: https://doi.org/10.1609/aaai.v34i07.6916.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. M. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. J. Bai, S. Chintala. PyTorch: An imperative style, high-performance deep learning library. In Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, Canada, pp. 721, 2019.
S. M. Hu, D. Liang, G. Y. Yang, G. W. Yang, W. Y. Zhou. Jittor: A novel deep learning framework with meta-operators and unified graph execution. Science China Information Sciences, vol. 63, no. 12, Article number 222103, 2020. DOI: https://doi.org/10.1007/s11432-020-3097-4.
K. M. He, X. Y. Zhang, S. Q. Ren, J. Sun. Delving deep into rectifiers: Surpassing human-level performance on imageNet classification. In Proceedings of IEEE International Conference on Computer Vision, Santiago, Chile, pp. 1026–1034, 2015. DOI: https://doi.org/10.1109/ICCV.2015.123.
A. Krizhevsky, I. Sutskever, G. E. Hinton. ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, USA, pp. 1097–1105, 2012.
D. P. Kingma, J. Ba. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations, San Diego, USA, 2015.
I. Loshchilov, F. Hutter. SGDR: Stochastic gradient descent with warm restarts. In Proceedings of the 5th International Conference on Learning Representations, Toulon, France, 2017.
P. Krähenbühl, V. Koltun. Efficient inference in fully connected CRFs with Gaussian edge potentials. In Proceedings of the 24th International Conference on Neural Information Processing Systems, Granada, Spain, pp. 109–117, 2011.
J. X. Zhao, J. J. Liu, D. P. Fan, Y. Cao, J. F. Yang, M. M. Cheng. EGNet: Edge guidance network for salient object detection. In Proceedings of IEEE/CVF International Conference on Computer Vision, IEEE, Seoul, Republic of Korea, pp.8778–8787, 2019. DOI: https://doi.org/10.1109/ICCV.2019.00887.
Z. Wu, L. Su, Q. M. Huang. Stacked cross refinement network for edge-aware salient object detection. In Proceedings of IEEE/CVF International Conference on Computer Vision, IEEE, Seoul, Republic of Korea, pp. 7263–7272, 2019. DOI: https://doi.org/10.1109/ICCV.2019.00736.
Z. Wu, L. Su, Q. M. Huang. Cascaded partial decoder for fast and accurate salient object detection. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Long Beach, USA, pp. 3902–3911, 2019. DOI: https://doi.org/10.1109/CVPR.2019.00403.
S. H. Gao, Y. Q. Tan, M. M. Cheng, C. Z. Lu, Y. P. Chen, S. C. Yan. Highly efficient salient object detection with 100K parameters. In Proceedings of the 16th European Conference on Computer Vision, Springer, Glasgow, UK, pp. 702–721, 2020. DOI: https://doi.org/10.1007/978-3-030-58539-6_42.
J. Zhang, D. P. Fan, Y. C. Dai, S. Anwar, F. S. Saleh, T. Zhang, N. Barnes. UC-Net: Uncertainty inspired RGB-D saliency detection via conditional variational autoencoders. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Seattle, USA, pp. 8579–8588, 2020. DOI: https://doi.org/10.1109/CVPR42600.2020.00861.
H. J. Zhou, X. H. Xie, J. H. Lai, Z. X. Chen, L. X. Yang. Interactive two-stream decoder for accurate and fast saliency detection. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Seattle, USA, pp. 9138–9147, 2020. DOI: https://doi.org/10.1109/CVPR42600.2020.00916.
Y. W. Pang, X. Q. Zhao, L. H. Zhang, H. C. Lu. Multi-scale interactive network for salient object detection. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Seattle, USA, pp. 9410–9419, 2020. DOI: https://doi.org/10.1109/CVPR42600.2020.00943.
X. B. Qin, D. P. Fan, C. Y. Huang, C. Diagne, Z. C. Zhang, A. C. Sant’Anna, A. Suàrez, M. Jagersand, L. Shao. Boundary-aware segmentation network for mobile and web applications, [Online], Available: https://arxiv.org/abs/2101.04704, 2021.
D. P. Fan, M. M. Cheng, Y. Liu, T. Li, A. Borji. Structure-measure: A new way to evaluate foreground maps. In Proceedings of IEEE International Conference on Computer Vision, Venice, Italy, pp. 4558–4567, 2017. DOI: https://doi.org/10.1109/ICCV.2017.487.
D. P. Fan, C. Gong, Y. Cao, B. Ren, M. M. Cheng, A. Borji. Enhanced-alignment measure for binary foreground map evaluation. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, pp. 698–704, 2018. DOI: https://doi.org/10.24963/ijcai.2018/97.
D. P. Fan, G. P. Ji, X. B. Qin, M. M. Cheng. Cognitive vision inspired object segmentation metric and loss function. SCIENTIA SINICA Informationis, vol. 51, no. 9, Article number 1475, 2021. DOI: https://doi.org/10.1360/SSI-2020-0370.
A. Borji, M. M. Cheng, H. Z. Jiang, J. Li. Salient object detection: A benchmark. IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 5706–5722, 2015. DOI: https://doi.org/10.1109/TIP.2015.2487833.
M. C. Zhuge, D. P. Fan, N. Liu, D. W. Zhang, D. Xu, L. Shao. Salient object detection via integrity learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, to be published. DOI: https://doi.org/10.1109/TPAMI.2022.3179526.
R. Margolin, L. Zelnik-Manor, A. Tal. How to evaluate foreground maps. In IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, pp. 248–255, 2014. DOI: https://doi.org/10.1109/CVPR.2014.39.
A. Howard, M. Sandler, B. Chen, W. J. Wang, L. C. Chen, M. X. Tan, G. Chu, V. Vasudevan, Y. K. Zhu, R. M. Pang, H. Adam, Q. Le. Searching for MobileNetV3. In Proceedings of IEEE/CVF International conference on computer vision, IEEE, Seoul, Republic of Korea, pp. 1314–1324, 2019. DOI: https://doi.org/10.1109/ICCV.2019.00140.
D. Jha, P. H. Smedsrud, M. A. Riegler, P. Halvorsen, T. De Lange, D. Johansen, H. D. Johansen. Kvasir-SEG: A segmented polyp dataset. In Proceedings of the 26th International Conference on Multimedia Modeling, Springer, Daejeon, Republic of Korea, pp. 451–462, 2020. DOI: https://doi.org/10.1007/978-3-030-37734-2.
J. Bernal, F. J. Sànchez, G. Fernàndez-Esparrach, D. Gil, C. Rodríguez, F. Vilariño. WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. Saliency maps from physicians. Computerized Medical Imaging and Graphics, vol. 43, pp. 99–111, 2015. DOI: https://doi.org/10.1016/j.compmedimag.2015.02.007.
J. Bernal, J. Sànchez, F. Vilariño. Towards automatic polyp detection with a polyp appearance model. Pattern Recognition, vol. 45, no. 9, pp. 3166–3182, 2012. DOI: https://doi.org/10.1016/j.patcog.2012.03.002.
J. Silva, A. Histace, O. Romain, X. Dray, B. Granado. Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. International Journal of Computer Assisted Radiology and Surgery, vol. 9, no. 2, pp. 283–293, 2014. DOI: https://doi.org/10.1007/s11548-013-0926-3.
O. Ronneberger, P. Fischer, T. Brox. U-Net: Convolutional networks for biomedical image segmentation. In Proceedings of the 18th International Conference on Medical Image Computing and Computer-assisted Intervention, Springer, Munich, Germany, pp. 234–241, 2015. DOI: https://doi.org/10.1007/978-3-319-24574-4_28.
Z. W. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, J. M. Liang. UNet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transactions on Medical Imaging, vol. 39, no. 6, pp. 1856–1867, 2020. DOI: https://doi.org/10.1109/TMI.2019.2959609.
X. Q. Zhao, L. H. Zhang, H. C. Lu. Automatic polyp segmentation via multi-scale subtraction network. In Proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Strasbourg, France, pp. 120–130, 2021. DOI: https://doi.org/10.1007/978-3-030-87193-2_12.
Y. Shi, L. M. Cui, Z. Q. Qi, F. Meng, Z. S. Chen. Automatic road crack detection using random structured forests. IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 12, pp. 3434–3445, 2016. DOI: https://doi.org/10.1109/TITS.2016.2552248.
E. Z. Xie, W. J. Wang, W. H. Wang, M. Y. Ding, C. H. Shen, P. Luo. Segmenting transparent objects in the wild. In Proceedings of the 16th European Conference on Computer Vision, Springer, Glasgow, UK, pp. 696–711, 2020. DOI: https://doi.org/10.1007/978-3-030-58601-0_41.
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The authors would like to thank the anonymous reviewers and editor for their helpful comments on this manuscript.
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The major part of this work was done while Ge-Peng Ji was an intern mentored by Deng-Ping Fan.
Conflicts of Interests
The authors declared that they have no conflicts of interest in this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
Ge-Peng Ji received the M. Sc. degree in communication and information systems from Wuhan University, China in 2021. He is a Ph.D. student at Australian National University, supervised by Professor Nick Barnes, majoring in engineering and computer science. He has published about 10 peer-reviewed journal and conference papers. In 2021, he received the Student Travel Award from Medical Image Computing and Computer-Assisted Intervention Society.
His research interests lie in computer vision, especially in a variety of dense prediction tasks, such as video analysis, medical image segmentation, camouflaged object segmentation, and saliency detection.
Deng-Ping Fan received the Ph. D. degree from Nankai University, China in 2019. He joined the Inception Institute of Artificial Intelligence (IIAI), UAE in 2019. He is a Postdoctoral Researcher, working with Prof. Luc Van Gool in Computer Vision Laboratory, ETH Zürich, Switzerland. He has published approximately 50 top journal and conference papers such as TPAMI, CVPR, ICCV, ECCV, etc. He won the Best Paper Finalist Award at IEEE CVPR 2019, and the Best Paper Award Nominee at IEEE CVPR 2020. He was recognized as the CVPR 2019 outstanding reviewer with a special mention award, the CVPR 2020 outstanding reviewer, the ECCV 2020 high-quality reviewer, and the CVPR 2021 outstanding reviewer. He served as a program committee board (PCB) member of IJCAI 2022–2024, a senior program committee (SPC) member of IJCAI 2021, a committee member of China Society of Image and Graphics (CSIG), area chair in NeurIPS 2021 Datasets and Benchmarks Track, area chair in MICCAI2020 Workshop (OMIA7), editorial board member of Computer Vision and Machine Learning.
His research interests include computer vision, deep learning, and visual attention, especially the human vision on co-salient object detection, RGB salient object detection, RGB-D salient object detection, and video salient object detection.
Yu-Cheng Chou received the B. Sc. degree in software engineering from School of Computer Science, Wuhan University, China in 2022. He is currently a visiting student at Johns Hopkins University, supervised by Zongwei Zhou and Prof. Alan Yuille.
His research interests include medical imaging, causality, and computer vision, especially developing novel methodologies to detect lesions accurately and exploring explainability through causality for computer-aided diagnosis and surgery.
Dengxin Dai received the Ph. D. degree in computer vision from ETH Zürich, Switzerland in 2016. He is a senior research group leader at the MPI for Informatics, heading the research group vision for autonomous systems. He has been area chair of multiple major computer vision conferences (e.g., CVPR21, CVPR22, ECCV22), has organized multiple international workshops, is on the editorial board of IJCV, and is an ELLIS member. His team has won multiple awards including the 1st Place at Waymo Open Dataset Challenge 2022 and the 2nd Place at NuScenes Tracking Challenge 2021. He has received the Golden Owl Award with ETH Zürich in 2021 for his exceptional teaching.
His research interests lie in autonomous driving, robust perception in adverse weather and illumination conditions, domain adaptation, sensor fusion, multi-task learning, and object recognition under limited supervision.
Alexander Liniger received the B. Sc. and M. Sc. degrees in mechanical engineering from Department of Mechanical and Process Engineering, ETH Zürich, Switzerland in 2010 and 2013, respectively, and received the Ph. D. degree at Automatic Control Laboratory, ETH Zürich, Switzerland in 2018. Currently, he is a postdoctoral researcher in Computer Vision Laboratory, ETH Zürich, Switzerland, where he is part of Luc van Gool’s group working on the Toyota TRACE project.
During his Ph. D., his main research interests include model predictive control, viability theory as well as game theory and their application to autonomous driving and racing. Currently, he is investigating how control theory and computer vision can be combined to achieve end-to-end learning approaches with formal guarantees.
Luc Van Gool received the Ph. D. degree in electromechanical engineering from Katholieke Universiteit Leuven, Belgium in 1981. Currently, he is a professor at the Katholieke Universiteit Leuven in Belgium and the ETH Zürich, Switzerland. He leads computer vision research at both places and also teaches at both. He has been a program committee member of several major computer vision conferences. He received several Best Paper awards, won a David Marr Prize and a Koenderink Award, and was nominated Distinguished Researcher by the IEEE Computer Science Committee. He is a co-founder of 10 spin-off companies.
His interests include 3D reconstruction and modeling, object recognition, tracking, gesture analysis, and a combination of those.
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Ji, GP., Fan, DP., Chou, YC. et al. Deep Gradient Learning for Efficient Camouflaged Object Detection. Mach. Intell. Res. 20, 92–108 (2023). https://doi.org/10.1007/s11633-022-1365-9
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DOI: https://doi.org/10.1007/s11633-022-1365-9
Keywords
- Camouflaged object detection (COD)
- object gradient
- soft grouping
- efficient model
- image segmentation