In this paper, we propose a novel unsupervised graph association
(UGA) to learn the underlying view-invariant representations
from the video pedestrian tracklets. The core
points of it are mining the cross-view relationships and reducing
the damage of noisy associations. To this end, UGA
adopts a two-stage training strategy: (1) intra-camera
learning stage and (2) inter-camera learning stage. The
former is to learn representations of a person with regards
to camera information, which helps to reduce false crossview
associations in the second stage. Compared with existing
tracklet-based methods, ours can build more accurate
cross-view associations and require lower GPU memory.
Extensive experiments and ablation studies on seven
RE-ID datasets demonstrate the superiority of the proposed
UGA over most state-of-the-art unsupervised and domain
adaptation RE-ID methods. Code is available at github1.

In this paper, we propose a novel unsupervised graph association
(UGA) to learn the underlying view-invariant representations
from the video pedestrian tracklets. The core
points of it are mining the cross-view relationships and reducing
the damage of noisy associations. To this end, UGA
adopts a two-stage training strategy: (1) intra-camera
learning stage and (2) inter-camera learning stage. The
former is to learn representations of a person with regards
to camera information, which helps to reduce false crossview
associations in the second stage. Compared with existing
tracklet-based methods, ours can build more accurate
cross-view associations and require lower GPU memory.
Extensive experiments and ablation studies on seven
RE-ID datasets demonstrate the superiority of the proposed
UGA over most state-of-the-art unsupervised and domain
adaptation RE-ID methods. Code is available at github1.