WANG L , HUA G , SUKTHANKAR R , et al . Video object discovery and co-segmentation with extremely weak supervision [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017 , 39 ( 10 ): 2074 - 2088 . DOI: 10.1109/TPAMI.2016.2612187 http://doi.org/10.1109/TPAMI.2016.2612187 We present a spatio-temporal energy minimization formulation for simultaneous video object discovery and co-segmentation across multiple videos containing irrelevant frames. Our approach overcomes a limitation that most existing video co-segmentation methods possess, i.e., they perform poorly when dealing with practical videos in which the target objects are not present in many frames. Our formulation incorporates a spatio-temporal auto-context model, which is combined with appearance modeling for superpixel labeling. The superpixel-level labels are propagated to the frame level through a multiple instance boosting algorithm with spatial reasoning, based on which frames containing the target object are identified. Our method only needs to be bootstrapped with the frame-level labels for a few video frames (e.g., usually 1 to 3) to indicate if they contain the target objects or not. Extensive experiments on four datasets validate the efficacy of our proposed method: 1) object segmentation from a single video on the SegTrack dataset, 2) object co-segmentation from multiple videos on a video co-segmentation dataset, and 3) joint object discovery and co-segmentation from multiple videos containing irrelevant frames on the MOViCS dataset and XJTU-Stevens, a new dataset that we introduce in this paper. The proposed method compares favorably with the state-of-the-art in all of these experiments.

MENG F M , LI H L , LIU G , et al . Object co-segmentation based on shortest path algorithm and saliency model [J ] . IEEE Transactions on Multimedia , 2012 , 14 : 1429 - 1441 . DOI: 10.1109/TMM.2012.2197741 http://doi.org/10.1109/TMM.2012.2197741 http://ieeexplore.ieee.org/document/6194335/ http://ieeexplore.ieee.org/document/6194335/

HUNG T Y , VAIKUNDAM S , NATARAJAN V , et al . Phase fourier reconstruction for anomaly detection on metal surface using salient irregularity [C ] //Proceedings of Conference on Multimedia Modeling.Berlin,Germany:Springer , 2017 : 290 - 302 .

CAO Y , ZHANG J , TIAN Q , et al . Salient target detection in hyperspectral images using spectral saliency [C ] //Proceedings of IEEE China Summit & International Conference on Signal & Information Processing. Chengdu, China:IEEE , 2015 .

王成龙 , 赵倩 , 赵琰 . 基于深度可分离卷积的实时遥感目标检测算法 [J ] . 电光与控制 , 2022 , 29 ( 8 ): 45 - 49 .

WANG C L , ZHAO Q , ZHAO Y . Areal-time remote separable convolution [J ] . Electronics Optics & Control , 2022 , 29 ( 8 ): 45 - 49 . (in Chinese)

马梁 , 苟于涛 , 雷涛 , 等 . 基于多尺度特征融合的遥感图像小目标检测 [J ] . 光电工程 , 2022 , 49 ( 4 ): 210363 .

MA L , GOU Y T , LEI T , et al . Small object detection based on multi-scale feature fusion using remote sensing images [J ] . Opto-Electron Eng , 2022 , 49 ( 4 ): 210363 . (in Chinese)

于博文 , 吕明 . 改进的YOLOv3算法及其在军事目标检测中的应用 [J ] . 兵工学报 , 2022 , 43 ( 2 ): 345 - 354 . DOI: 10.3969/j.issn.1000-1093.2022.02.012 http://doi.org/10.3969/j.issn.1000-1093.2022.02.012 复杂环境下军事目标检测技术是提高战场态势生成、分析能力的基础和关键。针对军事目标检测任务在复杂环境下传统检测算法的检测性能较低问题，提出一种基于改进YOLOv3的军事目标检测算法，通过深度学习实现复杂环境下军事目标的自动检测。构建军事目标图像数据集，为各类目标检测算法提供测试环境；在网络结构上通过引入可形变卷积改进的ResNet50-D残差网络作为特征提取网络，提高网络对形变目标的检测精度和速度；在特征融合阶段引入双注意力机制和特征重构模块，增强目标特征的表征能力，抑制干扰，提升检测精度；利用DIOU损失函数和Focal损失函数重新设计目标检测器的损失函数，进一步提高其对军事目标的检测精度；在军事目标图像数据集中进行测试实验。实验结果表明，改进的YOLOv3算法相比于原YOLOv3算法，平均精度均值提高了2.98%，检测速度提高了8.6帧/s，具有较好的检测性能，可为战场态势生成、分析提供有效的辅助技术支持。

YU B W , LÜ M . Improved YOLOv3 algorithm and its application in military target detection [J ] . Acta Armamentarii , 2022 , 43 ( 2 ): 345 - 354 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2022.02.012 http://doi.org/10.3969/j.issn.1000-1093.2022.02.012 Military target detection in a complex environment is the basis and key to improving battlefield situation generation and analysis capability. For the military target detection tasks, the detection performance of traditional detection algorithms in complex environment is low. A military target detection algorithm based on improved YOLOv3 algorithm is proposed to automatically detect the military targets in complex environment through deep learning. A military target image dataset is constructed to provide a testing environment for various target detection algorithms. The detection accuracy and speed of deformable target are improved by introducing the deformable convolutional improved ResNet50-D residual network as feature extraction network. In the stage of feature fusion, a dual-attention mechanism and feature reconstruction module are introduced to enhance the characterization ability of target features, suppress the interference, and improve the detection accuracy. The loss function of target detector is redesigned by using DIOU Loss functions and Focal Loss to funther improve the detection accuracy of military targets. The experimental results show that the improved YOLOv3 algorithm improves the average detection accuracy by 2.98% and the detection speed by 8.6 frames/s compared with the original YOLOv3 algorithm. The improved YOLOv3 algorithm has better detection performance and can provide effective auxiliary technical support for battlefield situation generation and analysis.

WANG W G , ZHAO S Y , SHEN J B , et al . Salient object detection with pyramid attention and salient edges [C ] // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Long Beach, CA, US:IEEE , 2019 .

BEARMAN A L , RUSSAKOVSKY O , FERRARI V , et al . What's the point: semantic segmentation with point supervision:arXiv:1506.02106 [R ] . Ithaca,NY , US : Cornell University , 2021 :1506.02106.

ZHANG L , ZHANG Y N , YAN H Q , et al . Salient object detection in hyperspectral imagery using multi-scale spectral-spatial gradient [J ] . Neurocomputing , 2018 , 291 : 215 - 225 . DOI: 10.1016/j.neucom.2018.02.070 http://doi.org/10.1016/j.neucom.2018.02.070 https://linkinghub.elsevier.com/retrieve/pii/S0925231218302376 https://linkinghub.elsevier.com/retrieve/pii/S0925231218302376

ITTI L , KOCH C , NIEBUR E . A model of saliency-based visual attention for rapid scene analysis [J ] . IEEE Transactions on Pattern Analysis & Machine Intelligence , 1998 , 20 ( 11 ): 1254 - 1259 .

MOAN S L , MANSOURI A , HARDEBERG J Y , et al . Saliency in spectral images [J ] . Scandinavian Conference on Image Analysis , 2011 : 114 - 123 .

LIANG J , ZHOU J , BAI X , et al . Salient object detection in hyperspectral imagery [C ] //Proceedings of International Conference on Image Processing. Melbourne, VIC, Australia:IEEE , 2013 .

IMAMOGLU N , DING G Q , FANG Y M , et al . Salient object detection on hyperspectral images using features learned from unsupervised segmentation task [C ] //Proceedings of ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing. Brighton, UK:IEEE,2019.

HUANG C , XU T F , ZHANG Y H , et al . Salient object detection on hyperspectral images in wireless network using CNN and saliency optimization [J ] . Ad Hoc Networks , 2021 , 112 ( 8 ): 102369 . DOI: 10.1016/j.adhoc.2020.102369 http://doi.org/10.1016/j.adhoc.2020.102369 https://linkinghub.elsevier.com/retrieve/pii/S1570870520307125 https://linkinghub.elsevier.com/retrieve/pii/S1570870520307125

PAN C , LIU J F , YAN W Q , et al . Salient object detection based on visual perceptual saturation and two-stream hybrid networks [J ] . IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society , 2021 , 30 : 4773 - 4787 . DOI: 10.1109/TIP.2021.3074796 http://doi.org/10.1109/TIP.2021.3074796 https://ieeexplore.ieee.org/document/9420315/ https://ieeexplore.ieee.org/document/9420315/

ZHOU Y , HUO S W , XIANG W , et al . Semi-supervised salient object detection using a linear feedback control system model [J ] . IEEE Transactions on Cybernetics , 2019 , 49 ( 4 ): 1173 - 1185 . DOI: 10.1109/TCYB.2018.2793278 http://doi.org/10.1109/TCYB.2018.2793278 To overcome the challenging problems in saliency detection, we propose a novel semi-supervised classifier which makes good use of a linear feedback control system (LFCS) model by establishing a relationship between control states and salient object detection. First, we develop a boundary homogeneity model to estimate the initial saliency and background likelihoods, which are regarded as the labeled samples in our semi-supervised learning procedure. Then in order to allocate an optimized saliency value to each superpixel, we present an iterative semi-supervised learning framework which integrates multiple saliency cues and image features using an LFCS model. Via an innovative iteration method, the system gradually converges an optimized stable state, which is associating with an accurate saliency map. This paper also covers comprehensive simulation study based on public datasets, which demonstrates the superiority of the proposed approach.

ZHANG Q J , CONG R M , LI C Y , et al . Dense attention fluid network for salient object detection in optical remote sensing images [J ] . IEEE Transactions on Image Processing , 2021 , 30 : 1305 - 1317 . DOI: 10.1109/TIP.2020.3042084 http://doi.org/10.1109/TIP.2020.3042084 Despite the remarkable advances in visual saliency analysis for natural scene images (NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains an open and challenging problem. In this paper, we propose an end-to-end Dense Attention Fluid Network (DAFNet) for SOD in optical RSIs. A Global Context-aware Attention (GCA) module is proposed to adaptively capture long-range semantic context relationships, and is further embedded in a Dense Attention Fluid (DAF) structure that enables shallow attention cues flow into deep layers to guide the generation of high-level feature attention maps. Specifically, the GCA module is composed of two key components, where the global feature aggregation module achieves mutual reinforcement of salient feature embeddings from any two spatial locations, and the cascaded pyramid attention module tackles the scale variation issue by building up a cascaded pyramid framework to progressively refine the attention map in a coarse-to-fine manner. In addition, we construct a new and challenging optical RSI dataset for SOD that contains 2,000 images with pixel-wise saliency annotations, which is currently the largest publicly available benchmark. Extensive experiments demonstrate that our proposed DAFNet significantly outperforms the existing state-of-the-art SOD competitors. https://github.com/rmcong/DAFNet_TIP20.

LIN Z Y , LI P T , HUANG W J , et al . Fixed feature with attention based on recurrent criss-cross salient object detection network [C ] //Proceedings of the 2020 8th International Conference on Digital Home.Dalian, China:IEEE , 2020 .

QIN X B , ZHANG Z C , HUANG C Y , et al . U2-net: going deeper with nested U-structure for salient object detection [J ] . Pattern Recognition , 2020 , 106 : 107404 . DOI: 10.1016/j.patcog.2020.107404 http://doi.org/10.1016/j.patcog.2020.107404 https://linkinghub.elsevier.com/retrieve/pii/S0031320320302077 https://linkinghub.elsevier.com/retrieve/pii/S0031320320302077

WANG Y D , ZHANG J , KAN M N , et al . Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation [C ] //Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA, US:IEEE , 2020 .

JAMPANI V , SUN D Q , LIU M Y , et al . Superpixel Sampling Networks [C ] //Proceedings of European Conference on Computer Vision.Beilin,Germany:Springer , 2018 .

QIN X B , ZHANG Z C , HUANG C Y , et al . BASNet: boundary-aware salient object detection [C ] //Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA, US:IEEE , 2019 .

YAN H Q , ZHANG Y N , WEI W , et al . Salient object detection in hyperspectral imagery using spectral gradient contrast [C ] //Proceedings of IGARSS 2016 - 2016 IEEE International Geoscience and Remote Sensing Symposium. Beijing, China : IEEE , 2016.

TONG N , LU H C , RUAN X , et al . Salient object detection via bootstrap learning [C ] //Proceedings of IEEE Conference on Computer Vision & Pattern Recognition. Boston, MA, US:IEEE , 2015 : 1884 - 1892 .

TONG N , LU H C , ZHANG L H , et al . Saliency detection with multi-scale superpixels [J ] . IEEE Signal Processing Letters , 2014 , 21 ( 9 ): 1035 - 1039 . DOI: 10.1109/LSP.2014.2323407 http://doi.org/10.1109/LSP.2014.2323407 http://ieeexplore.ieee.org/document/6814822/ http://ieeexplore.ieee.org/document/6814822/

LI H Y , LU H C , LIN Z , et al . Inner and inter label propagation: salient object detection in the wild [J ] . IEEE Transactions on Image Processing , 2015 , 24 ( 10 ): 3176 - 3186 . DOI: 10.1109/TIP.2015.2440174 http://doi.org/10.1109/TIP.2015.2440174 In this paper, we propose a novel label propagation-based method for saliency detection. A key observation is that saliency in an image can be estimated by propagating the labels extracted from the most certain background and object regions. For most natural images, some boundary superpixels serve as the background labels and the saliency of other superpixels are determined by ranking their similarities to the boundary labels based on an inner propagation scheme. For images of complex scenes, we further deploy a threecue-center-biased objectness measure to pick out and propagate foreground labels. A co-transduction algorithm is devised to fuse both boundary and objectness labels based on an inter propagation scheme. The compactness criterion decides whether the incorporation of objectness labels is necessary, thus greatly enhancing computational efficiency. Results on five benchmark data sets with pixelwise accurate annotations show that the proposed method achieves superior performance compared with the newest state-of-the-arts in terms of different evaluation metrics.

YANG C , ZHANG L H , LU H C , et al . Saliency detection via graph-based manifold ranking [C ] //Proceedings of Computer Vision & Pattern Recognition.Portland, OR , US : IEEE , 2013 .

ZHU W J , LIANG S , WEI Y C , et al . Saliency optimization from robust background detection [C ] //Proceedings of IEEE Conference on Computer Vision & Pattern Recognition.Columbus, OH, US:IEEE , 2014 : 2814 - 2821 .

ZHANG J M , SCLAROFF S , LIN Z , et al . Minimum barrier salient object detection at 80 FPS [C ] //Proceedings of IEEE International Conference on Computer Vision. Washington,D.C. , US : IEEE , 2015 .

TU W C , HE S F , YANG Q X , et al . Real-time Salient object detection with a minimum spanning Tree [C ] //Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas, NV , US : IEEE , 2016 .

ZHOU Y , HUO S W , XIANG W , et al . Semi-supervised salient object detection using a linear feedback control system model [J ] . IEEE Transactions on Cybernetics , 2019 , 49 ( 4 ): 1173 - 1185 . DOI: 10.1109/TCYB.2018.2793278 http://doi.org/10.1109/TCYB.2018.2793278 To overcome the challenging problems in saliency detection, we propose a novel semi-supervised classifier which makes good use of a linear feedback control system (LFCS) model by establishing a relationship between control states and salient object detection. First, we develop a boundary homogeneity model to estimate the initial saliency and background likelihoods, which are regarded as the labeled samples in our semi-supervised learning procedure. Then in order to allocate an optimized saliency value to each superpixel, we present an iterative semi-supervised learning framework which integrates multiple saliency cues and image features using an LFCS model. Via an innovative iteration method, the system gradually converges an optimized stable state, which is associating with an accurate saliency map. This paper also covers comprehensive simulation study based on public datasets, which demonstrates the superiority of the proposed approach.