Efficient multi-label ranking for multi-class learning: Application to object recognition

51 citations recorded by the source corpus.

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Structured Prediction Energy Networks (2015, background): “Modeling improvements use max-margin or ranking losses that directly address the multi-label structure (Elisseeff & Weston, 2001; Godbole & Sarawagi, 2004; Bucak et al., 2009).”
Large-scale Multi-label Learning with Missing Labels (2013, methodology): “• Average AUC: we follow Bucak et al. (2009) to calculate area under ROC curve for each instance and report the average AUC among all test instances.”
Improving Pairwise Ranking for Multi-label Image Classification (2017, methodology): “, label dependency [1, 17, 38, 25], label sparsity [10, 12, 27], and label noise [33, 39, 4].”
Proposal generation for object detection using cascaded ranking SVMs (2011, methodology): “Ranking SVMs have been used recently in object detection [2] and segmentation [3, 11, 15].”
The Emerging Trends of Multi-Label Learning (2020, background): “[81] notices that in MLML setting, the traditional multi-label ranking error [111] may overestimate the classification error.”
CU-Net: Component Unmixing Network for Textile Fiber Identification (2019, methodology): “…2014, 2018; Zhou and Tao 2012; Zhang et al. 2015), label dependency (Shu et al. 2015; Alessandro et al. 2013; Liu and Tsang 2015; Verma and Jawahar 2016; Zhang and Zhou 2007; Zhou et al. 2012; Wu et al. 2018), and label noise (Wu et al. 2015a; Zhao and Guo 2015; Xu et al. 2015; Bucak et al. 2009).”