Ge-Peng Ji, Deng-Ping Fan, Yu-Cheng Chou, Dengxin Dai, Alexander Liniger, Luc Van Gool. Deep Gradient Learning for Efficient Camouflaged Object Detection. Machine Intelligence Research, vol. 20, no. 1, pp.92-108, 2023. https://doi.org/10.1007/s11633-022-1365-9
Citation: Ge-Peng Ji, Deng-Ping Fan, Yu-Cheng Chou, Dengxin Dai, Alexander Liniger, Luc Van Gool. Deep Gradient Learning for Efficient Camouflaged Object Detection. Machine Intelligence Research, vol. 20, no. 1, pp.92-108, 2023. https://doi.org/10.1007/s11633-022-1365-9

Deep Gradient Learning for Efficient Camouflaged Object Detection

doi: 10.1007/s11633-022-1365-9
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  • Author Bio:

    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. E-mail: gepengai.ji@gmail.comORCID iD: 0000-0001-7092-2877

    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. E-mail: dengpfan@gmail.com (Corresponding author)ORCID iD: 0000-0002-5245-7518

    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. E-mail: johnson111788@gmail.comORCID iD: 0000-0002-9334-2899

    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.E-mail: dai@vision.ee.ethz.chORCID iD: 0000-0001-5440-9678

    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.E-mail: alex.liniger@vision.ee.ethz.chORCID iD: 0000-0002-7858-7900

    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. E-mail: vangool@vision.ee.ethz.chORCID iD: 0000-0002-3445-5711

  • Received Date: 2022-05-25
  • Accepted Date: 2022-08-06
  • 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.

     

  • 1 In this paper, ${\rm{ConvBR}}$ denotes the standard convolutional layer followed by a BN[41] layer and a ReLU[42] layer.
    2 The model′s parameter and MACs are measured by the toolbox: https://github.com/sovrasov/flops-counter.pytorch.
    *Corresponding author (dengpfan@gmail.com). The major part of this work was done while Ge-Peng Ji was an intern mentored by Deng-Ping Fan.
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