  Fully-unsupervised Person and Vehicle Re-Identification have received
increasing attention due to their broad applicability in surveillance,
forensics, event understanding, and smart cities, without requiring any manual
annotation. However, most of the prior art has been evaluated in datasets that
have just a couple thousand samples. Such small-data setups often allow the use
of costly techniques in time and memory footprints, such as Re-Ranking, to
improve clustering results. Moreover, some previous work even pre-selects the
best clustering hyper-parameters for each dataset, which is unrealistic in a
large-scale fully-unsupervised scenario. In this context, this work tackles a
more realistic scenario and proposes two strategies to learn from large-scale
unlabeled data. The first strategy performs a local neighborhood sampling to
reduce the dataset size in each iteration without violating neighborhood
relationships. A second strategy leverages a novel Re-Ranking technique, which
has a lower time upper bound complexity and reduces the memory complexity from
O(n^2) to O(kn) with k << n. To avoid the pre-selection of specific
hyper-parameter values for the clustering algorithm, we also present a novel
scheduling algorithm that adjusts the density parameter during training, to
leverage the diversity of samples and keep the learning robust to noisy
labeling. Finally, due to the complementary knowledge learned by different
models, we also introduce a co-training strategy that relies upon the
permutation of predicted pseudo-labels, among the backbones, with no need for
any hyper-parameters or weighting optimization. The proposed methodology
outperforms the state-of-the-art methods in well-known benchmarks and in the
challenging large-scale Veri-Wild dataset, with a faster and memory-efficient
Re-Ranking strategy, and a large-scale, noisy-robust, and ensemble-based
learning approach.
