Concealed Object Detection

We present the first systematic study on concealed object detection (COD), which aims to identify objects that are ?perfectly? embedded in their background. The high intrinsic similarities between the concealed objects and their background make COD far more challenging than traditional object detection/segmentation. To better understand this task, we collect a large-scale dataset, called COD10K, which consists of 10,000 images covering concealed objects in diverse real-world scenarios from 78 object categories. Further, we provide rich annotations including object categories, object boundaries, challenging attributes, object-level labels, and instance-level annotations. Our COD10K enables comprehensive concealed object understanding and can even be used to help progress several other vision tasks, such as detection, segmentation, classification etc. We also design a simple but strong baseline for COD, termed the Search Identification Network (SINet). Without any bells and whistles, SINet outperform 12 cutting-edge baselines on all datasets tested, making them robust, general architectures that could serve as catalysts for future research in COD. Finally, we provide some interesting findings, and highlight several potential applications and future directions. To spark research in this new field, our code, dataset, and online demo are available at our project page: http://mmcheng.net/cod.

[1]  Leonidas J. Guibas,et al.  Taskonomy: Disentangling Task Transfer Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[2]  Ali Borji,et al.  Revisiting Video Saliency: A Large-Scale Benchmark and a New Model , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[3]  Trung-Nghia Le,et al.  CamouFinder: Finding Camouflaged Instances in Images , 2021, AAAI.

[4]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[5]  Nick Barnes,et al.  Uncertainty Inspired RGB-D Saliency Detection , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Joan Bruna,et al.  Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation , 2014, NIPS.

[7]  Luc Van Gool,et al.  The Pascal Visual Object Classes Challenge: A Retrospective , 2014, International Journal of Computer Vision.

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

[9]  T. Wong,et al.  Camouflage images , 2010, ACM Trans. Graph..

[10]  Hujun Bao,et al.  A Late Fusion CNN for Digital Matting , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Isabelle Guyon,et al.  Taking Human out of Learning Applications: A Survey on Automated Machine Learning , 2018, 1810.13306.

[12]  Yang Cao,et al.  Semantic Edge Detection with Diverse Deep Supervision , 2018, International Journal of Computer Vision.

[13]  Peter Kontschieder,et al.  The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[14]  Yang Cao,et al.  Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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

[16]  Antonio Torralba,et al.  SIFT Flow: Dense Correspondence across Scenes and Its Applications , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[18]  Ling Shao,et al.  Group Collaborative Learning for Co-Salient Object Detection , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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

[20]  Ling Shao,et al.  Boundary-Aware Segmentation Network for Mobile and Web Applications , 2021, ArXiv.

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

[22]  Yuchao Dai,et al.  Simultaneously Localize, Segment and Rank the Camouflaged Objects , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Kaiming He,et al.  Momentum Contrast for Unsupervised Visual Representation Learning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Ramesh Raskar,et al.  Deep Polarization Cues for Transparent Object Segmentation , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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

[26]  Ali Borji,et al.  Salient object detection: A survey , 2014, Computational Visual Media.

[27]  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.

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

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

[30]  Yao Zhao,et al.  Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Bolei Zhou,et al.  Places: A 10 Million Image Database for Scene Recognition , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Ting He,et al.  A Fully Convolutional Neural Network for Wood Defect Location and Identification , 2019, IEEE Access.

[33]  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).

[34]  Jungong Han,et al.  RGB-T Salient Object Detection via Fusing Multi-Level CNN Features , 2019, IEEE Transactions on Image Processing.

[35]  Ming-Ming Cheng,et al.  Representative Batch Normalization with Feature Calibration , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Bolei Zhou,et al.  Scene Parsing through ADE20K Dataset , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

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

[39]  Yael Pritch,et al.  Saliency filters: Contrast based filtering for salient region detection , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[40]  Chao Gao,et al.  BASNet: Boundary-Aware Salient Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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

[42]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

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

[44]  Shuhan Chen,et al.  Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection , 2020, ECCV.

[45]  Jure Skvarč,et al.  Segmentation-based deep-learning approach for surface-defect detection , 2019, Journal of Intelligent Manufacturing.

[46]  Antonio Criminisi,et al.  TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation , 2006, ECCV.

[47]  Andrew Owens,et al.  Self-Supervised Learning of Audio-Visual Objects from Video , 2020, ECCV.

[48]  Ning Xu,et al.  Deep Image Matting , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[50]  Sebastian Ramos,et al.  The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[51]  Andrew Zisserman,et al.  Speeding up Convolutional Neural Networks with Low Rank Expansions , 2014, BMVC.

[52]  Bo Ren,et al.  Enhanced-alignment Measure for Binary Foreground Map Evaluation , 2018, IJCAI.

[53]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

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

[55]  Ajoy Mondal,et al.  Camouflaged Object Detection and Tracking: A Survey , 2020, Int. J. Image Graph..

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

[57]  Trevor Darrell,et al.  Adapting Visual Category Models to New Domains , 2010, ECCV.

[58]  J. Wolfe,et al.  Guided Search 2.0 A revised model of visual search , 1994, Psychonomic bulletin & review.

[59]  Greg Mori,et al.  Guiding model search using segmentation , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

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

[61]  M. Huynen,et al.  Disruptive coloration and background pattern matching , 2005, Nature.

[62]  Luc Van Gool,et al.  A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

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

[65]  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 .

[66]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

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

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

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

[70]  Ling Shao,et al.  Salient Objects in Clutter , 2021, ArXiv.

[71]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[72]  Ming-Ming Cheng,et al.  Global2Local: Efficient Structure Search for Video Action Segmentation , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[73]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[74]  Dima Damen,et al.  Scaling Egocentric Vision: The EPIC-KITCHENS Dataset , 2018, ArXiv.

[75]  Yunhong Wang,et al.  Receptive Field Block Net for Accurate and Fast Object Detection , 2017, ECCV.

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

[77]  Xiaopeng Wei,et al.  Camouflaged Object Segmentation with Distraction Mining , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[78]  Atsushi Shimada,et al.  TransCut: Transparent Object Segmentation from a Light-Field Image , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[79]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[80]  Shuicheng Yan,et al.  Highly Efficient Salient Object Detection with 100K Parameters , 2020, ECCV.

[81]  Shi-Min Hu,et al.  Jittor: a novel deep learning framework with meta-operators and unified graph execution , 2020, Science China Information Sciences.

[82]  Ming-Ming Cheng,et al.  JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation , 2020, IEEE Transactions on Image Processing.

[83]  Yuan Xie,et al.  Instance-Level Salient Object Segmentation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[84]  Wenguan Wang,et al.  Shifting More Attention to Video Salient Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[85]  Hanumant Singh,et al.  Camouflaging an Object from Many Viewpoints , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[86]  Dinggang Shen,et al.  Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19 , 2020, IEEE Reviews in Biomedical Engineering.

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

[88]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

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

[90]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[91]  Jianmin Jiang,et al.  A Simple Pooling-Based Design for Real-Time Salient Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[92]  Chenglizhao Chen,et al.  Mutual Graph Learning for Camouflaged Object Detection , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[93]  Leonidas J. Guibas,et al.  PartNet: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level 3D Object Understanding , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[94]  Kai Zhao,et al.  Res2Net: A New Multi-Scale Backbone Architecture , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[95]  Neil D. B. Bruce,et al.  Relative Saliency and Ranking: Models, Metrics, Data and Benchmarks , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[96]  Alexei A. Efros,et al.  Unbiased look at dataset bias , 2011, CVPR 2011.

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

[98]  Tengpeng Li,et al.  Re-thinking Co-Salient Object Detection , 2021, IEEE transactions on pattern analysis and machine intelligence.

[99]  Qijun Zhao,et al.  Siamese Network for RGB-D Salient Object Detection and Beyond , 2020, ArXiv.

[100]  Andrew Zisserman,et al.  Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation , 2020, ACCV.

[101]  A. Treisman.  Features and Objects: The Fourteenth Bartlett Memorial Lecture , 1988, The Quarterly journal of experimental psychology. A, Human experimental psychology.

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

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

[104]  Zheng Lin,et al.  Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[105]  Xindong Wu,et al.  Object Detection With Deep Learning: A Review , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[106]  Nick Barnes,et al.  UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[107]  Harshit,et al.  Transparent Object Tracking Benchmark , 2020, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

[108]  James M. Rehg,et al.  The Secrets of Salient Object Segmentation , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[109]  Ali Borji,et al.  Salient Object Detection: A Benchmark , 2015, IEEE Transactions on Image Processing.

[110]  Qinggang Meng,et al.  An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features , 2020, IEEE Transactions on Instrumentation and Measurement.

[111]  Haibin Ling,et al.  Salient Object Detection in the Deep Learning Era: An In-Depth Survey , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.