Current person re-identification (re-id) methods assume that (1) pre-labelled training data are available for every camera pair, (2) the gallery size for re-identification is moderate. Both assumptions scale poorly to real-world applications when camera network size increases and gallery size becomes large. Human verification of automatic model ranked re-id results becomes inevitable. In this work, a novel human-in-the-loop re-id model based on Human Verification Incremental Learning (HVIL) is formulated which does not require any pre-labelled training data to learn a model, therefore readily scalable to new camera pairs. This HVIL model learns cumulatively from human feedback to provide instant improvement to re-id ranking of each probe on-the-fly enabling the model scalable to large gallery sizes. We further formulate a Regularised Metric Ensemble Learning (RMEL) model to combine a series of incrementally learned HVIL models into a single ensemble model to be used when human feedback becomes unavailable.
翻译:目前的人重新识别(重新标识)方法假定:(1) 每一对照相机都有预先标签的培训数据,(2) 用于再识别的画廊大小是适度的。当照相机网络规模扩大,画廊规模扩大时,两种假设的规模都低到真实世界应用程序。人类对自动模型排名重新标识结果的核查是不可避免的。在这项工作中,根据人类核查增量学习(HVIL)制定了一个新的人类在环形重新定位模型,不需要事先标签的培训数据来学习模型,因此很容易对新的照相机配对进行缩放。这个HVIL模型从人类反馈中累积学习,以便提供即时改进,使每个在天上的探测器重新定位,使模型能够缩放到大画廊大小。我们进一步制定了一个正规化的集成学习(RMEL)模型,将一系列逐步学习的HVIL模型合并成一个单一的混合模型,在人类反馈不可用时使用。