  With the surge of deep learning techniques, the field of person
re-identification has witnessed rapid progress in recent years. Deep learning
based methods focus on learning a feature space where samples are clustered
compactly according to their corresponding identities. Most existing methods
rely on powerful CNNs to transform the samples individually. In contrast, we
propose to consider the sample relations in the transformation. To achieve this
goal, we incorporate spectral clustering technique into CNN. We derive a novel
module named Spectral Feature Transformation and seamlessly integrate it into
existing CNN pipeline with negligible cost,which makes our method enjoy the
best of two worlds. Empirical studies show that the proposed approach
outperforms previous state-of-the-art methods on four public benchmarks by a
considerable margin without bells and whistles.
