WRGPruner: A new model pruning solution for tiny salient object detection
Tài liệu tham khảo
Gong, 2014
Hanson, 1989, Comparing biases for minimal network construction with back-propagation, 177
Touretzky, 1996
Hassibi, 1992, Second order derivatives for network pruning: optimal brain surgeon, Adv. Neural Inf. Proces. Syst., 5, 164
Li, 2016
Molchanov, 2016
Lin, 2020, Hrank: Filter pruning using high-rank feature map
Singh, 2020, Falf convnets: Fatuous auxiliary loss based filter-pruning for efficient deep cnns, 103857
Krizhevsky, 2009
Russakovsky, 2015, ImageNet large scale visual recognition challenge, Int. J. Comput. Vis., 115, 211, 10.1007/s11263-015-0816-y
Shu, 2019, Co-evolutionary compression for unpaired image translation, 3235
Ghosh, 2019, Deep network pruning for object detection, 3915
Itti, 1998, A model of saliency-based visual attention for rapid scene analysis, IEEE Trans. Pattern Anal. Mach. Intell., 20, 1254, 10.1109/34.730558
Ignatov, 2020, Rendering natural camera bokeh effect with deep learning, 418
Yazdi, 2018, New trends on moving object detection in video images captured by a moving camera: a survey, Comput. Sci. Rev., 28, 157, 10.1016/j.cosrev.2018.03.001
Parra-Arnau, 2017, Pay-per-tracking: a collaborative masking model for web browsing, Inf. Sci., 385, 96, 10.1016/j.ins.2016.12.036
Kaveti, 2020
He, 2020, Visual recognition of traffic police gestures with convolutional pose machine and handcrafted features, Neurocomputing, 390, 248, 10.1016/j.neucom.2019.07.103
Tong, 2020, Recent advances in small object detection based on deep learning: a review, Image Vis. Comput., 103910, 10.1016/j.imavis.2020.103910
Keren, 2020, Jl-dcf: Joint learning and densely-cooperative fusion framework for rgb-d salient object detection, 3052
Zhang, 2020, Uc-net: uncertainty inspired rgb-d saliency detection via conditional variational autoencoders, 8582
Frankle, 2018
Liu, 2018
Malach, 2020
Zhou, 2019, Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask, 3592
Lin, 2020
Krizhevsky, 2017, Imagenet classification with deep convolutional neural networks, Commun. ACM, 60, 84, 10.1145/3065386
Simonyan, 2014
He, 2016, Deep residual learning for image recognition, 770
Szegedy, 2015, Going deeper with convolutions, 1
Lee, 2019
Zhang, 2019
Liebenwein, 2019
Liu, 2019
Renda, 2020
Lin, 2020
Yan, 2013, Hierarchical saliency detection, 1155
Zeiler, 2010, Deconvolutional networks, 2528
Einhäuser, 2003, Does luminance-contrast contribute to a saliency map for overt visual attention?, Eur. J. Neurosci., 17, 1089, 10.1046/j.1460-9568.2003.02508.x
Li, 2015, Visual saliency based on multiscale deep features, 5455
Liu, 2010, Learning to detect a salient object, IEEE Trans. Pattern Anal. Mach. Intell., 33, 353
Li, 2014, The secrets of salient object segmentation, 280
Martin, 2001, A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, 416
Fan, 2018
Fan, 2017, Structure-measure: A new way to evaluate foreground maps, 4548
Margolin, 2014, How to evaluate foreground maps?, 248
Chen, 2004, Adaptive random testing, 320
Zhao, 2015, Saliency detection by multi-context deep learning, 1265
Hou, 2017, Deeply supervised salient object detection with short connections, 5300
Chen, 2020, Reverse attention-based residual network for salient object detection, IEEE Trans. Image Process., 29, 3763, 10.1109/TIP.2020.2965989