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.

翻译：全无监督行人及车辆再识别因其在监控、刑侦、事件理解及智慧城市等领域的广泛应用且无需任何人工标注而受到日益关注。然而，现有大多数方法仅在包含几千个样本的数据集上进行评估。这种小规模数据环境常允许使用耗时且占用大量内存的技术（如重排序）来改善聚类结果。此外，部分先前工作甚至为每个数据集预设最佳聚类超参数，这在大规模全无监督场景下并不现实。针对这一背景，本文致力于解决更贴近实际的大规模无标签数据学习场景，并提出两种学习策略。第一种策略通过局部邻域采样减少每次迭代的数据集规模，同时保持邻域关系不变；第二种策略则利用一种新型重排序技术，其时间复杂度上界更低，并将内存复杂度从O(n²)降至O(kn)（其中k << n）。为避免为聚类算法预设特定超参数值，我们进一步提出一种新型调度算法，在训练过程中动态调整密度参数，以充分利用样本多样性并保持对噪声标注的鲁棒性。最后，针对不同模型学习的互补知识，我们引入一种协同训练策略，该策略通过骨干网络间伪标签的排列实现，无需任何超参数或权重优化。所提方法在主流基准测试及具有挑战性的大规模Veri-Wild数据集上均超越现有最先进方法，同时实现了更快且内存高效的重排序策略，以及一种适用于大规模、噪声鲁棒且基于集成的学习方案。