5,186 research outputs found
Large-scale Fully-Unsupervised Re-Identification
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.Comment: This paper has been submitted for possible publication in an IEEE
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VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification
One fundamental challenge of vehicle re-identification (re-id) is to learn
robust and discriminative visual representation, given the significant
intra-class vehicle variations across different camera views. As the existing
vehicle datasets are limited in terms of training images and viewpoints, we
propose to build a unique large-scale vehicle dataset (called VehicleNet) by
harnessing four public vehicle datasets, and design a simple yet effective
two-stage progressive approach to learning more robust visual representation
from VehicleNet. The first stage of our approach is to learn the generic
representation for all domains (i.e., source vehicle datasets) by training with
the conventional classification loss. This stage relaxes the full alignment
between the training and testing domains, as it is agnostic to the target
vehicle domain. The second stage is to fine-tune the trained model purely based
on the target vehicle set, by minimizing the distribution discrepancy between
our VehicleNet and any target domain. We discuss our proposed multi-source
dataset VehicleNet and evaluate the effectiveness of the two-stage progressive
representation learning through extensive experiments. We achieve the
state-of-art accuracy of 86.07% mAP on the private test set of AICity
Challenge, and competitive results on two other public vehicle re-id datasets,
i.e., VeRi-776 and VehicleID. We hope this new VehicleNet dataset and the
learned robust representations can pave the way for vehicle re-id in the
real-world environments
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