STEVENS M , MERILAITA S . Animal camouflage: current issues and new perspectives [J ] . Philosophical Transactions of the Royal Society B Biological Sciences , 2009 , 364 ( 1516 ): 423 - 427 . DOI: 10.1098/rstb.2008.0217 http://doi.org/10.1098/rstb.2008.0217 https://royalsocietypublishing.org/doi/10.1098/rstb.2008.0217 https://royalsocietypublishing.org/doi/10.1098/rstb.2008.0217

STUART-FOX D , MOUSSALLI A , WHITING M J . Predator-specific camouflage in chameleons [J ] . Biology Letters , 2008 , 4 ( 4 ): 326 - 329 . DOI: 10.1098/rsbl.2008.0173 http://doi.org/10.1098/rsbl.2008.0173 https://royalsocietypublishing.org/doi/10.1098/rsbl.2008.0173 https://royalsocietypublishing.org/doi/10.1098/rsbl.2008.0173 \n A crucial problem for most animals is how to deal with multiple types of predator, which differ in their sensory capabilities and methods of prey detection. For animals capable of rapid colour change, one potential strategy is to change their appearance in relation to the threat posed by different predators. Here, we show that the dwarf chameleon,\n Bradypodion taeniabronchum\n, exhibits different colour responses to two predators that differ in their visual capabilities. Using a model of animal colour perception to gain a ‘predator's eye view’, we show that chameleons showed better background colour matching in response to birds than snakes, yet they appear significantly more camouflaged to the snake visual system because snakes have poorer colour discrimination.\n

PUZIKOVA N P , UVAROVA E V , FILYAEV I M , et al. Principles of an approach for coloring military camouflage [J ] . Fibre Chemistry , 2008 , 40 ( 2 ): 155 - 159 . DOI: 10.1007/s10692-008-9030-9 http://doi.org/10.1007/s10692-008-9030-9 http://link.springer.com/10.1007/s10692-008-9030-9 http://link.springer.com/10.1007/s10692-008-9030-9

DING Y , ZHAO X F , ZHANG Z L , et al. Semi-supervised locality preserving dense graph neural network with ARMA filters and context-aware learning for hyperspectral image classification [J/OL ] . IEEE Transactions on Geoscience and Remote Sensing .(2021-08-04)[2021-11-06 ] . https:∥doi.org/ 10.1109/TGRS.2021.3100578 https://dx.doi.org/10.1109/TGRS.2021.3100578 . DOI: 10.1109/TGRS.2021.3100578 http://doi.org/10.1109/TGRS.2021.3100578

ZHANG J J , ZHANG X , LI T , et al. Visible light polarization image desmogging via cycle convolutional neural network [J/OL ] . Multimedia Systems .(2021-04-30)[2021-11-06 ] . https:∥doi.org/ 10.1007/s00530-021-00802-9 https://dx.doi.org/10.1007/s00530-021-00802-9 . DOI: 10.1007/s00530-021-00802-9 http://doi.org/10.1007/s00530-021-00802-9

JIANG X H , CAI W , YANG Z Y , et al. IARet: a lightweight multiscale infrared aerocraft recognition algorithm [J/OL ] . Arabian Journal for Science and Engineerin .(2021-09-24)[2021-11-06 ] . https:∥doi.org/10.1007/s13369-021-06181-7 https:∥doi.org/10.1007/s13369-021-06181-7 .

周静 , 窦一民 , 李金屏 . 基于光流场分割的伪装色运动目标检测 [J ] . 济南大学学报(自然科学版) , 2020 , 34 ( 4 ): 328 - 334 .

ZHOU J , DOU Y M , LI J P . Camouflage color moving object detection based on optical flow field segmentation [J ] . Journal of University of Jinan (Science and Technology) , 2020 , 34 ( 4 ): 328 - 334 . (in Chinese)

FAN D P , JI G P , SUN G L , et al. Camouflaged object detection [C ] ∥Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, US:IEEE , 2020 : 2774 - 2784 .

MEI H Y , JI G P , WEI Z Q , et al. Camouflaged object segmentation with distraction mining [C ] ∥Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, US:IEEE , 2021 : 8768 - 8777 .

HOU J , GRAHAM B , NIESNERM , et al. Exploring data-efficient 3D scene understanding with contrastive scene contexts [C ] ∥Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Reco gnition.Piscataway, NJ, US:IEEE , 2021 : 15582 - 15592 .

HUANG J H , WANG H Q , BIRDAL T , et al. MultiBodySync: multi-body segmentation and motion estimation via 3D scan synchronization [C ] ∥Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, US:IEEE , 2021 : 7104 - 7114 .

LIU Z Z , QI X J , FU C W . One thing one click: a self-training approach for weakly supervised 3D semantic segmentation [C ] ∥Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, US:IEEE , 2021 : 1726 - 1736 .

CHEN X K , YUAN Y H , ZENG G , et al. Semi-supervised semantic segmentation with cross pseudo supervision [C ] ∥Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, US:IEEE , 2021 : 2613 - 2622 .

YAO Y Z , CHEN T , XIE G S , et al. Non-salient region object mining for weakly supervised semantic segmentation [C ] ∥Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, US:IEEE , 2021 : 2623 - 2632 .

FU Y , YANG L J , LIU D , et al. CompFeat: comprehensive feature aggregation for video instance segmentation [C ] ∥Proceedings of the AAAI Conference on Artificial Intelligence. Menlo Park, NJ, US:AAAI , 2019 , 35 ( 2 ): 1361 - 1369 .

WEI J , HU Y W , ZHANG R M , et al. Shallow attention network for polyp segmentation [EB/OL ] . (2021-09-24)[2021-11-06 ] . https:∥arxiv.org/abs/2108.00882 https:∥arxiv.org/abs/2108.00882 .

WANG J F , SONG L , LI Z M , et al. End-to-end object detection with fully convolutional network [C ] ∥Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway, NJ, US:IEEE , 2021 : 15844 - 15853 .

PATEL K , BUR A M , WANG G H , et al. Enhanced U-Net:a feature enhancement network for polyp segmentation [C ] ∥Proceedings of the 2021 18th Conference on Robots and Vision.Piscataway, NJ, US:IEEE , 2021 : 181 - 188 .

FAN H , MEI X , PROKHOROV D , et al. RGB-D scene labeling with multimodal recurrent neural networks [C ] ∥Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).Piscataway, NJ, US:IEEE , 2017 : 203 - 211 .

LIU K H , YE Z H , GUO H Y , et al. FISS GAN: a generative adversarial network for foggy image semantic segmentation [J ] . IEEE/CAA Journal of Automatica Sinica , 2021 , 8 ( 8 ): 1428 - 1439 . DOI: 10.1109/JAS.2021.1004057 http://doi.org/10.1109/JAS.2021.1004057 https://ieeexplore.ieee.org/document/9459610/ https://ieeexplore.ieee.org/document/9459610/

LE T N , CAO Y , NGUYEN T C , et al. Camouflaged instance segmentation in-the-wild: dataset, method, and benchmark suite [J ] . IEEE Transactions on Image Processing , 2022 , 31 : 287 - 300 . DOI: 10.1109/TIP.2021.3130490 http://doi.org/10.1109/TIP.2021.3130490 https://ieeexplore.ieee.org/document/9633224/ https://ieeexplore.ieee.org/document/9633224/

梁新宇 , 林浩坤 , 杨辉 , 等 . 军用伪装目标图像语义分割数据集的构建 [J ] . 激光与光电子学进展 , 2021 , 58 ( 4 ): 0410015 .

LIANG X Y , LIN H K , YANG H , et al. Construction of semantic segmentation dataset of camouflage target image [J ] . Laser & Optoelectronics Progress , 2021 , 58 ( 4 ): 0410015 . (in Chinese)

HALL J R , CUTHILL I C , BADDELEY R J , et al. Camouflage, detection and identification of moving targets [J ] . Proceedings of the Royal Society B: Biological Sciences , 2013 , 280 ( 1758 ): 20130064 . DOI: 10.1098/rspb.2013.0064 http://doi.org/10.1098/rspb.2013.0064 https://royalsocietypublishing.org/doi/10.1098/rspb.2013.0064 https://royalsocietypublishing.org/doi/10.1098/rspb.2013.0064 Nearly all research on camouflage has investigated its effectiveness for concealing stationary objects. However, animals have to move, and patterns that only work when the subject is static will heavily constrain behaviour. We investigated the effects of different camouflages on the three stages of predation—detection, identification and capture—in a computer-based task with humans. An initial experiment tested seven camouflage strategies on static stimuli. In line with previous literature, background-matching and disruptive patterns were found to be most successful. Experiment 2 showed that if stimuli move, an isolated moving object on a stationary background cannot avoid detection or capture regardless of the type of camouflage. Experiment 3 used an identification task and showed that while camouflage is unable to slow detection or capture, camouflaged targets are harder to identify than uncamouflaged targets when similar background objects are present. The specific details of the camouflage patterns have little impact on this effect. If one has to move, camouflage cannot impede detection; but if one is surrounded by similar targets (e.g. other animals in a herd, or moving background distractors), then camouflage can slow identification. Despite previous assumptions, motion does not entirely ‘break’ camouflage.

GAO S , CHENG M M , ZHAO K , et al. Res2net: a new multi-scale backbone architecture [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2019 , 43 ( 2 ): 652 - 662 . DOI: 10.1109/TPAMI.34 http://doi.org/10.1109/TPAMI.34 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34

WEI J H , WANG S H , HUANG Q M . F 3 Net: fusion, feedback and focus for salient object detection [C ] ∥Proceedings of the AAAI Conference on Artificial Intelligence. Menlo Park, NJ, US:AAAI , 2021 , 34 ( 7 ): 12321 - 12328 .

PERAZZI F , KRÄHENBÜHL P , PRITCH Y , et al. Saliency filters: contrast based filtering for salient region detection [C ] ∥Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, US:IEEE , 2012 : 733 - 740 .

FAN D P , CHENG M M , LIU Y , et al. Structure-measure: a new way to evaluate foreground maps [C ] ∥Proceedings of 2017 IEEE International Conference on Computer Vision. Piscataway, NJ, US:IEEE , 2017 : 4558 - 4567 .

FAN D P , GONG C , CAO Y , et al. Enhanced-alignment measure for binary foreground map evaluation [C ] ∥Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. San Francisco,CA , US : Margan Kaufmann , 2018 : 698 - 704 .

MARGOLIN R , ZELNIK-MANOR L , TAL A . How to evaluate foreground maps [C ] ∥Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, US:IEEE , 2014 : 248 - 255 .

MILLETARI F , NASSIR N , SEYED-AHMAD A . V-Net: fully convolutional neural networks for volumetric medical image segmentation [C ] ∥Proceedings of the 2016 4th International Conference on 3D Vision (3DV). CA, US: Computer Society’s Conference Publishing Services , 2016 : 565 - 571 .

ZHOU Z , IDDIQUEE M M , TAJBAKHSH N , et al. UNet++: redesigning skip connections to exploit multiscale features in image segmentation [J ] . IEEE Transactions on Medical Imaging , 2020 , 39 : 1856 - 1867 . DOI: 10.1109/TMI.2019.2959609 http://doi.org/10.1109/TMI.2019.2959609 The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations: (1) their optimal depth is apriori unknown, requiring extensive architecture search or inefficient ensemble of models of varying depths; and (2) their skip connections impose an unnecessarily restrictive fusion scheme, forcing aggregation only at the same-scale feature maps of the encoder and decoder sub-networks. To overcome these two limitations, we propose UNet++, a new neural architecture for semantic and instance segmentation, by (1) alleviating the unknown network depth with an efficient ensemble of U-Nets of varying depths, which partially share an encoder and co-learn simultaneously using deep supervision; (2) redesigning skip connections to aggregate features of varying semantic scales at the decoder sub-networks, leading to a highly flexible feature fusion scheme; and (3) devising a pruning scheme to accelerate the inference speed of UNet++. We have evaluated UNet++ using six different medical image segmentation datasets, covering multiple imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and electron microscopy (EM), and demonstrating that (1) UNet++ consistently outperforms the baseline models for the task of semantic segmentation across different datasets and backbone architectures; (2) UNet++ enhances segmentation quality of varying-size objects-an improvement over the fixed-depth U-Net; (3) Mask RCNN++ (Mask R-CNN with UNet++ design) outperforms the original Mask R-CNN for the task of instance segmentation; and (4) pruned UNet++ models achieve significant speedup while showing only modest performance degradation. Our implementation and pre-trained models are available at https://github.com/MrGiovanni/UNetPlusPlus.

FAN D P , JI G P , ZHOU T , et al. Pranet: parallel reverse attention network for polyp segmentation [C ] ∥Proceedings of International Conference on Medical Image Computing and Computer-Assisted Inter-vention.Berlin, Germany:Springer , 2020 : 263 - 273 .

KIM T , LEE H , KIM D . UACANet: uncertainty augmented context attention for polyp segmentation [C ] ∥Proceedings of the 29th ACM International Conference on Multimedia. New York, NY, US:ACM , 2021 : 2167 - 2175 .

QIN X B , ZHANG Z V , HUANG C Y , et al. BASNet: Boundary-Aware Salient Object Detection [C ] ∥Proceedings o f 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway,NJ, US:IEEE , 2019 : 7471 - 7481 .

WU Z , SU L , HUANG Q M . Stacked cross refinement network for edge-aware salient object detection [C ] ∥Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Piscataway, NJ, US:IEEE , 2019 : 7263 - 7272 .