  Recent advances in person re-identification have demonstrated enhanced
discriminability, especially with supervised learning or transfer learning.
However, since the data requirements---including the degree of data
curations---are becoming increasingly complex and laborious, there is a
critical need for unsupervised methods that are robust to large intra-class
variations, such as changes in perspective, illumination, articulated motion,
resolution, etc. Therefore, we propose an unsupervised framework for person
re-identification which is trained in an end-to-end manner without any
pre-training. Our proposed framework leverages a new attention mechanism that
combines group convolutions to (1) enhance spatial attention at multiple scales
and (2) reduce the number of trainable parameters by 59.6%. Additionally, our
framework jointly optimizes the network with agglomerative clustering and
instance learning to tackle hard samples. We perform extensive analysis using
the Market1501 and DukeMTMC-reID datasets to demonstrate that our method
consistently outperforms the state-of-the-art methods (with and without
pre-trained weights).
