  Single-camera-training person re-identification (SCT re-ID) aims to train a
re-ID model using SCT datasets where each person appears in only one camera.
The main challenge of SCT re-ID is to learn camera-invariant feature
representations without cross-camera same-person (CCSP) data as supervision.
Previous methods address it by assuming that the most similar person should be
found in another camera. However, this assumption is not guaranteed to be
correct. In this paper, we propose a Camera-Invariant Meta-Learning Network
(CIMN) for SCT re-ID. CIMN assumes that the camera-invariant feature
representations should be robust to camera changes. To this end, we split the
training data into meta-train set and meta-test set based on camera IDs and
perform a cross-camera simulation via meta-learning strategy, aiming to enforce
the representations learned from the meta-train set to be robust to the
meta-test set. With the cross-camera simulation, CIMN can learn
camera-invariant and identity-discriminative representations even there are no
CCSP data. However, this simulation also causes the separation of the
meta-train set and the meta-test set, which ignores some beneficial relations
between them. Thus, we introduce three losses: meta triplet loss, meta
classification loss, and meta camera alignment loss, to leverage the ignored
relations. The experiment results demonstrate that our method achieves
comparable performance with and without CCSP data, and outperforms the
state-of-the-art methods on SCT re-ID benchmarks. In addition, it is also
effective in improving the domain generalization ability of the model.
