Salient object detection via bootstrap learning

By Na Tong, Huchuan Lu, Xiang Ruan, Ming-Hsuan Yang. Computer Vision and Pattern Recognition, 2015

307 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7fe7d2765b571c3a5b91dac7168cfcc6852a8871

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In Cognitive Neuroscience

In Computer Vision and Pattern Recognition

Citations in the corpus

Progressive Attention Guided Recurrent Network for Salient Object Detection (2018, methodology): “BL [23], KSR [29], LEGS [25], ELD [12], MDF [13], DS [16], MCDL [37], DCL [14], RFCN [27], DHS [18], UCF [36] and Amulet [35].”
A Stagewise Refinement Model for Detecting Salient Objects in Images (2017, methodology): “We compare our method with eleven state-of-the-art deep learning-based and classic saliency detection methods, including DRFI [24], BL [43], LEGS [46], MDF [28], MCDL [56], DS [31], DHS [33], ELD [26], DCL [29], KSR [49] and RFCN [48].”
New Contour Cue-Based Hybrid Sparse Learning for Salient Object Detection (2019, background): “Tong et al. [39] addressed this problem with bootstrap learning in a two-stage manner.”
Learning Uncertain Convolutional Features for Accurate Saliency Detection (2017, methodology): “We compare the proposed UCF algorithm with other 10 state-of-the-art ones including 6 deep learning based algorithms (DCL [24], DS [26], ELD [22], LEGS [44], MDF [50], RFCN [45]) and 4 conventional counterparts (BL [41], BSCA [33], DRFI [18], DSR [25]).”
Towards Explanation of DNN-based Prediction with Guided Feature Inversion (2018, methodology): “g to the generated saliency maps, so as to ensure fair comparison. Our method is comparedwithsixstate-of-the-artsalientobjectdetectionmethods, including SS [14], SIM [22], RC [4], FT [1], SF [23], BL [35]. The precision/recall/F-measure, and MAE result are presented in Fig. 8 (a) and (b) respectivel”
Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection (2017, methodology): “We compare our algorithm with other 11 state-of-the-art ones including 7 deep learning based algorithms (DCL [22], DHS [27], DS [24], ELD [20], LEGS [41], MDF [50], RFCN [44]) and 4 conventional algorithms (BL[39], BSCA [32], DRFI [19], DSR [23]).”
Non-local Deep Features for Salient Object Detection (2017, methodology): “Although some traditional AI methods such as SVM [41] perform well, deep learning methods, speciﬁcally CNN models, have raised the bar and imposed themselves as the un-avoidable standard.”
Deep Level Sets for Salient Object Detection (2017, methodology): “Qualitative comparison with FT [1], HC [10], GMR [56], BSCA [47], MTDS [31], MDF [29], MCDL [62], ELD [26], LEGS [50].”
Detect Globally, Refine Locally: A Novel Approach to Saliency Detection (2018, methodology): “We compare the proposed algorithm against 13 stateof-the-art algorithms, including the deep learning based methods as well as other non-deep competitors, DRFI [12], BL [25], LEGS [26], MDF [17], MCDL [36], DS [20], DCL [18], DHS [22], RFCN [28], KSR [30], UCF [34], Amulet [33] and SRM [29].”
Cancer Diagnosis Using Deep Learning: A Bibliographic Review (2019, methodology): “There are many other methods for image segmentation: histogram thresholding, adaptive thresholding, gradient flow vector, distributed and localized region identification, clustering and statistical region growing [31], bootstrap learning [32], active contours, supervised learning, edge detection, fu”
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection (2015, methodology): “We qualitatively and quantitatively compare the proposed approach with several state-of-the-art methods including DRFI [33], Wco [26], IT [11], SF [50], GC [6], GMR [25], FT [2], MC [5], GB [1], HS [24], SVO [4], DSR [51], LEGS [34], BL15 [52] and BSCA [27].”