Camouflaged Object Segmentation with Distraction Mining

Camouflaged object segmentation (COS) aims to identify objects that are "perfectly" assimilate into their surroundings, which has a wide range of valuable applications. The key challenge of COS is that there exist high intrinsic similarities between the candidate objects and noise background. In this paper, we strive to embrace challenges towards effective and efficient COS. To this end, we develop a bio-inspired framework, termed Positioning and Focus Network (PFNet), which mimics the process of predation in nature. Specifically, our PFNet contains two key modules, i.e., the positioning module (PM) and the focus module (FM). The PM is designed to mimic the detection process in predation for positioning the potential target objects from a global perspective and the FM is then used to perform the identification process in predation for progressively refining the coarse prediction via focusing on the ambiguous regions. Notably, in the FM, we develop a novel distraction mining strategy for the distraction discovery and removal, to benefit the performance of estimation. Extensive experiments demonstrate that our PFNet runs in real-time (72 FPS) and significantly outperforms 18 cutting-edge models on three challenging datasets under four standard metrics.

[1]  Kai Chen,et al.  Hybrid Task Cascade for Instance Segmentation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Lihi Zelnik-Manor,et al.  How to Evaluate Foreground Maps , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Yongchao Gong,et al.  Mask Scoring R-CNN , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Nima Tajbakhsh,et al.  UNet++: A Nested U-Net Architecture for Medical Image Segmentation , 2018, DLMIA/ML-CDS@MICCAI.

[5]  Xin Yang,et al.  DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution With Large Factors , 2019, IEEE Transactions on Multimedia.

[6]  Gang Wang,et al.  Progressive Attention Guided Recurrent Network for Salient Object Detection , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[7]  Xiaogang Wang,et al.  Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Yunchao Wei,et al.  Deep Salient Object Detection With Dense Connections and Distraction Diagnosis , 2018, IEEE Transactions on Multimedia.

[9]  Ling Shao,et al.  An Iterative and Cooperative Top-Down and Bottom-Up Inference Network for Salient Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Jiangjiang Liu,et al.  Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground , 2018, ECCV.

[11]  Chi-Wing Fu,et al.  Bidirectional Feature Pyramid Network with Recurrent Attention Residual Modules for Shadow Detection , 2018, ECCV.

[12]  Huchuan Lu,et al.  Multi-Scale Interactive Network for Salient Object Detection , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Shaodi You,et al.  Single Image Water Hazard Detection Using FCN with Reflection Attention Units , 2018, ECCV.

[14]  Carsten Rother,et al.  Panoptic Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Zygmunt Pizlo,et al.  Camouflage and visual perception , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[16]  Changqun Xia,et al.  Selectivity or Invariance: Boundary-Aware Salient Object Detection , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[17]  Bo Dong,et al.  Depth-Aware Mirror Segmentation , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Ben Wang,et al.  Reverse Attention for Salient Object Detection , 2018, ECCV.

[20]  Innes C. Cuthill,et al.  Camouflage, detection and identification of moving targets , 2013, Proceedings of the Royal Society B: Biological Sciences.

[21]  Robert Green,et al.  My Mirror , 1998, Annals of Internal Medicine.

[22]  A. Thayer,et al.  Concealing-coloration in the animal kingdom : an exposition of the laws of disguise through color and pattern being a summary of Abbott H. Thayer's discoveries , 1909 .

[23]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[24]  Qingming Huang,et al.  F3Net: Fusion, Feedback and Focus for Salient Object Detection , 2019, AAAI.

[25]  Vladlen Koltun,et al.  Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials , 2011, NIPS.

[26]  Matti Pietikäinen,et al.  Deep Learning for Generic Object Detection: A Survey , 2018, International Journal of Computer Vision.

[27]  Ye Wang,et al.  Semantic Segmentation with Reverse Attention , 2017, BMVC.

[28]  Ling Shao,et al.  Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images , 2020, IEEE Transactions on Medical Imaging.

[29]  Dongsheng Zhou,et al.  Exploring Dense Context for Salient Object Detection , 2021, IEEE Transactions on Circuits and Systems for Video Technology.

[30]  Shi-Min Hu,et al.  Global contrast based salient region detection , 2011, CVPR 2011.

[31]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Ming-Ming Cheng,et al.  EGNet: Edge Guidance Network for Salient Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[33]  Li Fei-Fei,et al.  Towards total scene understanding: Classification, annotation and segmentation in an automatic framework , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[34]  Zhuowen Tu,et al.  Deeply Supervised Salient Object Detection with Short Connections , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  P. Sengottuvelan,et al.  Performance of Decamouflaging Through Exploratory Image Analysis , 2008, 2008 First International Conference on Emerging Trends in Engineering and Technology.

[36]  Ting Zhao,et al.  Pyramid Feature Attention Network for Saliency Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[37]  Huchuan Lu,et al.  Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[38]  明明 程,et al.  Cognitive vision inspired object segmentation metric and loss function , 2021, SCIENTIA SINICA Informationis.

[39]  E. Poulton Adaptive Coloration in Animals , 1940, Nature.

[40]  Trung-Nghia Le,et al.  MirrorNet: Bio-Inspired Adversarial Attack for Camouflaged Object Segmentation , 2020, ArXiv.

[41]  In-So Kweon,et al.  CBAM: Convolutional Block Attention Module , 2018, ECCV.

[42]  Qiang Zhang,et al.  Don’t Hit Me! Glass Detection in Real-World Scenes , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[43]  Wei Liu,et al.  ParseNet: Looking Wider to See Better , 2015, ArXiv.

[44]  Dimitris Samaras,et al.  A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuation , 2017, ECCV.

[45]  Chunhua Shen,et al.  Segmenting Transparent Objects in the Wild , 2020, ECCV.

[46]  Chi-Wing Fu,et al.  Direction-Aware Spatial Context Features for Shadow Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[47]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[48]  M. Shah,et al.  Object tracking: A survey , 2006, CSUR.

[49]  Gayoung Lee,et al.  Deep Saliency with Encoded Low Level Distance Map and High Level Features , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[50]  Ping Zhang,et al.  Study on the Camouflaged Target Detection Method Based on 3D Convexity , 2011 .

[51]  Rynson W. H. Lau,et al.  Distraction-Aware Shadow Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[52]  Sabine Süsstrunk,et al.  Frequency-tuned salient region detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[53]  Ming-Hsuan Yang,et al.  PiCANet: Learning Pixel-Wise Contextual Attention for Saliency Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[54]  Tao Li,et al.  Structure-Measure: A New Way to Evaluate Foreground Maps , 2017, International Journal of Computer Vision.

[55]  Yuxin Wang,et al.  Multi-Context And Enhanced Reconstruction Network For Single Image Super Resolution , 2020, 2020 IEEE International Conference on Multimedia and Expo (ICME).

[56]  Qingming Huang,et al.  Global Context-Aware Progressive Aggregation Network for Salient Object Detection , 2020, AAAI.

[57]  Ling Shao,et al.  Camouflaged Object Detection , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[58]  S. Merilaita,et al.  Animal camouflage: current issues and new perspectives , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[59]  Wei Wu,et al.  Distractor-aware Siamese Networks for Visual Object Tracking , 2018, ECCV.

[60]  Jianqin Yin Yanbin Han Wendi Hou Jinping Li,et al.  Detection of the Mobile Object with Camouflage Color Under Dynamic Background Based on Optical Flow , 2011 .

[61]  Xiangyu Zhang,et al.  Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[62]  Ling Shao,et al.  PraNet: Parallel Reverse Attention Network for Polyp Segmentation , 2020, MICCAI.

[63]  Zhe Wu,et al.  Cascaded Partial Decoder for Fast and Accurate Salient Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[64]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[65]  Trung-Nghia Le,et al.  Anabranch network for camouflaged object segmentation , 2019, Comput. Vis. Image Underst..

[66]  Gang Wang,et al.  Context Contrasted Feature and Gated Multi-scale Aggregation for Scene Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.