Multi-manifold deep metric learning for image set classification

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

SphereFace: Deep Hypersphere Embedding for Face Recognition (2017, background): “Recently, prevailing deep metric learning [7, 17, 24, 30, 25, 22, 34] usually uses neural networks to automatically learn discriminative features x1,x2 followed by a simple distance metric such as Euclidean distance ‖x1−x2‖2.”
SphereFace Revived: Unifying Hyperspherical Face Recognition (2021, background): “The development of deep FR is also closely related to deep metric learning [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28].”
Quality Aware Network for Set to Set Recognition (2017, background): “Under these settings, samples in a set distribute in a Hilbert space or Grassmann mainfold so that this issue can be formulated as a metric learning problem [23, 39].”
Deep Face Recognition: A Survey (2018, methodology): “One is to use these representations for similarity comparison between the media in two sets and pool the results into a single, final score, such as max score pooling [108], average score pooling [105] and its variations [220], [17].”