Citation: | ZHU Shiping, XIE Wentao, ZHAO Congyang, LI Qinghai. Salient Object Detection via Feature Permutation and Space Activation[J]. Journal of Electronics & Information Technology, 2022, 44(3): 1093-1101. doi: 10.11999/JEIT210133 |
[1] |
GIBSON K B, VO D T, and NGUYEN T Q. An investigation of dehazing effects on image and video coding[J]. IEEE Transactions on Image Processing, 2012, 21(2): 662–673. doi: 10.1109/TIP.2011.2166968
|
[2] |
SULLIVAN G J, OHM J R, HAN W J, et al. Overview of the high efficiency video coding (HEVC) standard[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2012, 22(12): 1649–1668. doi: 10.1109/TCSVT.2012.2221191
|
[3] |
OHM J R, SULLIVAN G J, SCHWARZ H, et al. Comparison of the coding efficiency of video coding standards—including high efficiency video coding (HEVC)[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2012, 22(12): 1669–1684. doi: 10.1109/TCSVT.2012.2221192
|
[4] |
TREISMAN A M and GELADE G. A feature-integration theory of attention[J]. Cognitive Psychology, 1980, 12(1): 97–136. doi: 10.1016/0010-0285(80)90005-5
|
[5] |
KOCH C and ULLMAN S. Shifts in selective visual attention: Towards the underlying neural circuitry[J]. Human Neurobiology, 1985, 4(4): 219–227.
|
[6] |
ITTI L, KOCH C, and NIEBUR E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254–1259. doi: 10.1109/34.730558
|
[7] |
LIU Nian, HAN Junwei, and YANG M H. PiCANet: Learning pixel-wise contextual attention for saliency detection[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 3089–3098.
|
[8] |
CHEN Shuhan, TAN Xiuli, WANG Ben, et al. Reverse attention for salient object detection[C]. The 15th European Conference on Computer Vision, Munich, Germany, Springer, 2018: 236–252.
|
[9] |
LI Xin, YANG Fan, CHENG Hong, et al. Contour knowledge transfer for salient object detection[C]. The 15th European Conference on Computer Vision, Munich, Germany, 2018: 370–385.
|
[10] |
QIN Xuebin, ZHANG Zichen, HUANG Chenyang, et al. BASNet: Boundary-aware salient object detection[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 7471–7481.
|
[11] |
WU Zhe, SU Li, and HUANG Qingming. Cascaded partial decoder for fast and accurate salient object detection[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 3902–3911.
|
[12] |
LIU Jiangjiang, HOU Qibin, CHENG Mingming, et al. A simple pooling-based design for real-time salient object detection[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 3912–3921.
|
[13] |
MA Guangxiao, CHEN Chenglizhao, LI Shuai, et al. Salient object detection via multiple instance joint re-learning[J]. IEEE Transactions on Multimedia, 2020, 22(2): 324–336. doi: 10.1109/TMM.2019.2929943
|
[14] |
WEI Jun, WANG Shuhui, WU Zhe, et al. Label decoupling framework for salient object detection[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 13025–13034.
|
[15] |
PANG Youwei, ZHAO Xiaoqi, ZHANG Lihe, et al. Multi-scale interactive network for salient object detection[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 9413–9422.
|
[16] |
LI Haofeng, LI Guanbin, and YU Yizhou. ROSA: Robust salient object detection against adversarial attacks[J]. IEEE Transactions on Cybernetics, 2020, 50(11): 4835–4847. doi: 10.1109/TCYB.2019.2914099
|
[17] |
CHEN Shuhan, TAN Xiuli, WANG Ben, et al. Reverse attention-based residual network for salient object detection[J]. IEEE Transactions on Image Processing, 2020, 29: 3763–3776. doi: 10.1109/TIP.2020.2965989
|
[18] |
HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770–778.
|
[19] |
ZHANG Xianyu, ZHOU Xinyu, LIN Mengxiao, et al. ShuffleNet: An extremely efficient convolutional neural network for mobile devices[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 6848–6856.
|
[20] |
LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 936–944.
|
[21] |
MA Ningning, ZHANG Xiangyu, and SUN Jian. Funnel activation for visual recognition[C]. The 16th European Conference on Computer Vision, Glasgow, UK, 2020: 1–17.
|
[22] |
RAHMAN M A and WANG Yang. Optimizing intersection-over-union in deep neural networks for image segmentation[C]. The 12th International Symposium on Visual Computing, Las Vegas, USA, 2016: 234–244.
|
[23] |
MARGOLIN R, ZELNIK-MANOR L, and TAL A. How to evaluate foreground maps[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 248–255.
|
[24] |
FAN Dengping, CHENG Mingming, LIU Yun, et al. Structure-measure: A new way to evaluate foreground maps[C]. 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 4558–4567.
|
[25] |
PERAZZI F, KRÄHENBÜHL P, PRITCH Y, et al. Saliency filters: Contrast based filtering for salient region detection[C]. 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012: 733–740.
|