  While supervised techniques in re-identification are extremely effective, the
need for large amounts of annotations makes them impractical for large camera
networks. One-shot re-identification, which uses a singular labeled tracklet
for each identity along with a pool of unlabeled tracklets, is a potential
candidate towards reducing this labeling effort. Current one-shot
re-identification methods function by modeling the inter-relationships amongst
the labeled and the unlabeled data, but fail to fully exploit such
relationships that exist within the pool of unlabeled data itself. In this
paper, we propose a new framework named Temporal Consistency Progressive
Learning, which uses temporal coherence as a novel self-supervised auxiliary
task in the one-shot learning paradigm to capture such relationships amongst
the unlabeled tracklets. Optimizing two new losses, which enforce consistency
on a local and global scale, our framework can learn learn richer and more
discriminative representations. Extensive experiments on two challenging video
re-identification datasets - MARS and DukeMTMC-VideoReID - demonstrate that our
proposed method is able to estimate the true labels of the unlabeled data more
accurately by up to $8\%$, and obtain significantly better re-identification
performance compared to the existing state-of-the-art techniques.

Computer Vision and Pattern Recognition