Minimum Barrier Salient Object Detection at 80 FPS

By Jianming Zhang, Stan Sclaroff, Zhe L. Lin, Xiaohui Shen, Brian L. Price, Radomír Mech. IEEE International Conference on Computer Vision, 2015

421 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/975be8f281912f52a0b94b79ac56c59332b61f05

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In Computer Vision and Pattern Recognition

Citations in the corpus

Salient Object Detection in the Deep Learning Era: An In-Depth Survey (2019, background): “• SBF [80] generates saliency predictions through a fusion process that integrates the weak saliency maps yielded by several classical un-supervised salient object detectors [34], [106], [107] at intra-and inter-image levels.”
Learning to Detect Salient Objects with Image-Level Supervision (2017, methodology): “Unsupervised Weakly Fully MR wCtr MBS BSCA WSS LEGS MDF MC DS SELD DCL RFCN ECSSD 0.690 0.676 0.673 0.705 0.823 0.785 0.807 0.796 0.826 0.810 0.829 0.834 SED 0.782 0.786 0.776 0.756 0.838 0.800 0.795 0.817 0.794 0.815 0.825 0.813 PASAL-S 0.583 0.597 0.604 0.597 0.720 – 0.705 0.687 0.655 0.714 0.710 ”
Next-active-object prediction from egocentric videos (2017, background): “[53] for salient object segmentation; • Random: starting from the Faster-RCNN detection, each bounding box is assigned a random score in the interval [0, 1].”
BASNet: Boundary-Aware Salient Object Detection (2019, methodology): “Traditional Methods: Early methods detect salient objects by searching for pixels according to a predefined saliency measure computed based on handcrafted features [69, 80, 60, 71].”
Surface defect saliency of magnetic tile (2018, methodology): “GMR [28], MBP [29], MC [30], RBD [31] and DRFI [32] are top models of non-deeplearning models in natural scene datasets [27] , so they are used to compare to our MCue model.”
Salient Object Subitizing (2015, positive): “We pick an operating point that gives the best F-score 3 on the Multi-Salient-Object (MSO) dataset (Zhang et al., 2015a) in this experiment.”
Exploiting Saliency for Object Segmentation from Image Level Labels (2017, methodology): “Albeit our convnet saliency model is better than handcrafted methods [14, 52], in the end only about 20% of images have reasonably good (IoU > 0.”
A Survey of Deep Learning-Based Object Detection (2019, background): “provide benchmarks for video salient object detection, and existing good algorithms [230] [231] [224] [232] [227] [233] [234] [235] [236] [237] [238][239][240][241] devote in this domain.”
Shifting More Attention to Video Salient Object Detection (2019, methodology): “…datasets [35,40,43,52,56,59,75], we present a comprehensive evaluation of 17 state-of-the-art models [8, 11,35,41,44,52,53,62,67,68,70,74–76, 81, 87, 92 Our two contributions represent a complete benchmark suite with the necessary tools for a complementary evaluation, bring a more insightful…”
New Contour Cue-Based Hybrid Sparse Learning for Salient Object Detection (2019, methodology): “…we denote as BMS [10], CA [52], CB [35], CGVS [53], CRF [15], DSR [20], FES [54], FT [16], GMR [30], GR [36], GS [28], HS [55], LC [56], LEGS [42], LMLC [41], LPS [57], MBS (MBS+) [45], MC [29], MNP [58], PSO [59], RC [17], SEG [60], SeR [61], SF [49], ST [62], and SVO [34], respectively.”
Anabranch network for camouflaged object segmentation (2019, methodology): “Various patch-based methods were also proposed that estimate dissimilarity between image patches (Cheng et al., 2011; Margolin et al., 2013; Zhang et al., 2015).”
Unconstrained Salient Object Detection via Proposal Subset Optimization (2016, methodology): “However, many previous methods [1, 11, 41, 30, 25, 6, 54] only solve the task of foreground segmentation, i.”
Deep Level Sets for Salient Object Detection (2017, background): “Assuming image boundaries belong to the background, Zhang et al [60] and Tu et al [48] compute the difference between pixels and image boundaries to be saliency and achieve realtime testing speed.”