Cross Dataset Person Re-identification

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How researchers see this paper

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Faceless Person Recognition: Privacy Implications in Social Media (2016, background): “Like other areas of computer vision, the last years have seen a shift from classifiers based on handcrafted features and metric learning approaches [13, 14, 15, 16, 17, 18, 19] towards methods based on deep learning [20, 21, 22, 23, 24, 25, 26, 27].”
Person Recognition in Personal Photo Collections (2015, background): “Both settings have seen a recent shift from sophisticated classifiers based on hand-crafted features and metric learning approaches [20, 7, 5, 29, 26, 41, 1], towards methods based on deep learning [37, 36, 43, 33, 27, 38, 21].”
Person Recognition in Personal Photo Collections (2017, background): “Standard benchmarks include VIPeR [16], CAVIAR [17], CUHK [18], and Caltech Roadside Pedestrians [19], with active line of research that previously focused on devising good hand-crafted features [20], [21], [22], and now focusing more on developing effective convnet architectures [23], [24], [25], [”
Recurrent Convolutional Network for Video-Based Person Re-identification (2016, background): “However it should be noted that the rank 1 performance is not much below [19] (see Table 1), and is well above other single-shot re-identification systems, such as [17], even those specifically trained in PRID, such as [13] with a rank 1 CMC scores of 28.”