IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 7, JULY 1998 Comments on "Geodesic Saliency of

1998

Citations in the corpus

• Domain Adaptive Dialog Generation via Meta Learning (2019, background): “Transfer learning (Caruana, 1997a; Bengio, 2012; Cohn et al., 1994; Mo et al., 2018), few-shot learning (Salakhutdinov et al., 2012; Li et al., 2006; Norouzi et al., 2013; Socher et al., 2013) and meta-learning (Finn et al., 2017) are introduced in solving such data scarcity problem in machine…”
• TURN TAP: Temporal Unit Regression Network for Temporal Action Proposals (2017, methodology): “Number of retrieved proposals (AR-N) [31, 9], Average Recall vs.”
• Modelling Crowd Scenes for Event Detection (2006, methodology): “Preprocessing involves the construction of a Mixture of Gaussians background model for the scene based on [11].”
• Understanding How Feature Structure Transfers in Transfer Learning (2017, background): “We have proven that in this setting tasks could regularize tasks [Liu et al., 2017b] and therefore the algorithms generalize fast [Liu et al., 2017a].”
• Graph Clustering with Graph Neural Networks (2020, methodology): “normalizes the cut by the product of the number of nodes in two partitions, or normalized cut [51], which uses total edge volume of the partition as normalization.”
• Visualization of Supervised and Self-Supervised Neural Networks via Attribution Guided Factorization (2020, background): “…classes include those that generate salient feature maps (Dabkowski and Gal 2017; Simonyan, Vedaldi, and Zisserman 2013; Mahendran and Vedaldi 2016; Zhou et al. 2016; Zeiler and Fergus 2014; Zhou et al. 2018), Activation Maximization (Erhan et al. 2009) and Excitation Backprop (Zhang et al. 2018).”
• Collaborative Learning for Weakly Supervised Object Detection (2018, methodology): “[Cinbis et al., 2017] 38.1 47.6 28.2 13.9 13.2 45.2 48.0 19.3 17.1 27.7 17.3 19.0 30.1 45.4 13.5 17.0 28.8 24.8 38.2 15.0 27.4 [Wang et al., 2014] 48.9 42.3 26.1 11.3 11.9 41.3 40.9 34.7 10.8 34.7 18.8 34.4 35.4 52.7 19.1 17.4 35.9 33.3 34.8 46.5 31.6 [Bilen and Vedaldi, 2016] 39.4 50.1 31.5 16.3…”