1,589 research outputs found
Alice Benchmarks: Connecting Real World Object Re-Identification with the Synthetic
For object re-identification (re-ID), learning from synthetic data has become
a promising strategy to cheaply acquire large-scale annotated datasets and
effective models, with few privacy concerns. Many interesting research problems
arise from this strategy, e.g., how to reduce the domain gap between synthetic
source and real-world target. To facilitate developing more new approaches in
learning from synthetic data, we introduce the Alice benchmarks, large-scale
datasets providing benchmarks as well as evaluation protocols to the research
community. Within the Alice benchmarks, two object re-ID tasks are offered:
person and vehicle re-ID. We collected and annotated two challenging real-world
target datasets: AlicePerson and AliceVehicle, captured under various
illuminations, image resolutions, etc. As an important feature of our real
target, the clusterability of its training set is not manually guaranteed to
make it closer to a real domain adaptation test scenario. Correspondingly, we
reuse existing PersonX and VehicleX as synthetic source domains. The primary
goal is to train models from synthetic data that can work effectively in the
real world. In this paper, we detail the settings of Alice benchmarks, provide
an analysis of existing commonly-used domain adaptation methods, and discuss
some interesting future directions. An online server will be set up for the
community to evaluate methods conveniently and fairly.Comment: 9 pages, 4 figures, 4 table
Large-scale Training Data Search for Object Re-identification
We consider a scenario where we have access to the target domain, but cannot
afford on-the-fly training data annotation, and instead would like to construct
an alternative training set from a large-scale data pool such that a
competitive model can be obtained. We propose a search and pruning (SnP)
solution to this training data search problem, tailored to object
re-identification (re-ID), an application aiming to match the same object
captured by different cameras. Specifically, the search stage identifies and
merges clusters of source identities which exhibit similar distributions with
the target domain. The second stage, subject to a budget, then selects
identities and their images from the Stage I output, to control the size of the
resulting training set for efficient training. The two steps provide us with
training sets 80\% smaller than the source pool while achieving a similar or
even higher re-ID accuracy. These training sets are also shown to be superior
to a few existing search methods such as random sampling and greedy sampling
under the same budget on training data size. If we release the budget, training
sets resulting from the first stage alone allow even higher re-ID accuracy. We
provide interesting discussions on the specificity of our method to the re-ID
problem and particularly its role in bridging the re-ID domain gap. The code is
available at https://github.com/yorkeyao/SnP.Comment: Accepted to CVPR202
Parameter-Efficient Person Re-identification in the 3D Space
People live in a 3D world. However, existing works on person
re-identification (re-id) mostly consider the semantic representation learning
in a 2D space, intrinsically limiting the understanding of people. In this
work, we address this limitation by exploring the prior knowledge of the 3D
body structure. Specifically, we project 2D images to a 3D space and introduce
a novel parameter-efficient Omni-scale Graph Network (OG-Net) to learn the
pedestrian representation directly from 3D point clouds. OG-Net effectively
exploits the local information provided by sparse 3D points and takes advantage
of the structure and appearance information in a coherent manner. With the help
of 3D geometry information, we can learn a new type of deep re-id feature free
from noisy variants, such as scale and viewpoint. To our knowledge, we are
among the first attempts to conduct person re-identification in the 3D space.
We demonstrate through extensive experiments that the proposed method (1) eases
the matching difficulty in the traditional 2D space, (2) exploits the
complementary information of 2D appearance and 3D structure, (3) achieves
competitive results with limited parameters on four large-scale person re-id
datasets, and (4) has good scalability to unseen datasets.Comment: The code is available at https://github.com/layumi/person-reid-3
StRDAN: Synthetic-to-Real Domain Adaptation Network for Vehicle Re-Identification
Vehicle re-identification aims to obtain the same vehicles from vehicle
images. This is challenging but essential for analyzing and predicting traffic
flow in the city. Although deep learning methods have achieved enormous
progress for this task, their large data requirement is a critical shortcoming.
Therefore, we propose a synthetic-to-real domain adaptation network (StRDAN)
framework, which can be trained with inexpensive large-scale synthetic and real
data to improve performance. The StRDAN training method combines domain
adaptation and semi-supervised learning methods and their associated losses.
StRDAN offers significant improvement over the baseline model, which can only
be trained using real data, for VeRi and CityFlow-ReID datasets, achieving 3.1%
and 12.9% improved mean average precision, respectively.Comment: 7 pages, 2 figures, CVPR Workshop Paper (Revised
Enhancing vehicle re-identification via synthetic training datasets and re-ranking based on video-clips information
Vehicle re-identification (ReID) aims to find a specific vehicle identity across multiple non-overlapping cameras. The main challenge of this task is the large intra-class and small inter-class variability of vehicles appearance, sometimes related with large viewpoint variations, illumination changes or different camera resolutions. To tackle these problems, we proposed a vehicle ReID system based on ensembling deep learning features and adding different post-processing techniques. In this paper, we improve that proposal by: incorporating large-scale synthetic datasets in the training step; performing an exhaustive ablation study showing and analyzing the influence of synthetic content in ReID datasets, in particular CityFlow-ReID and VeRi-776; and extending post-processing by including different approaches to the use of gallery video-clips of the target vehicles in the re-ranking step. Additionally, we present an evaluation framework in order to evaluate CityFlow-ReID: as this dataset has not public ground truth annotations, AI City Challenge provided an on-line evaluation service which is no more available; our evaluation framework allows researchers to keep on evaluating the performance of their systems in the CityFlow-ReID datasetOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Natur
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