5,546 research outputs found
Joint-SRVDNet: Joint Super Resolution and Vehicle Detection Network
In many domestic and military applications, aerial vehicle detection and
super-resolutionalgorithms are frequently developed and applied independently.
However, aerial vehicle detection on super-resolved images remains a
challenging task due to the lack of discriminative information in the
super-resolved images. To address this problem, we propose a Joint
Super-Resolution and Vehicle DetectionNetwork (Joint-SRVDNet) that tries to
generate discriminative, high-resolution images of vehicles fromlow-resolution
aerial images. First, aerial images are up-scaled by a factor of 4x using a
Multi-scaleGenerative Adversarial Network (MsGAN), which has multiple
intermediate outputs with increasingresolutions. Second, a detector is trained
on super-resolved images that are upscaled by factor 4x usingMsGAN architecture
and finally, the detection loss is minimized jointly with the super-resolution
loss toencourage the target detector to be sensitive to the subsequent
super-resolution training. The network jointlylearns hierarchical and
discriminative features of targets and produces optimal super-resolution
results. Weperform both quantitative and qualitative evaluation of our proposed
network on VEDAI, xView and DOTAdatasets. The experimental results show that
our proposed framework achieves better visual quality than thestate-of-the-art
methods for aerial super-resolution with 4x up-scaling factor and improves the
accuracy ofaerial vehicle detection
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
CANU-ReID: A Conditional Adversarial Network for Unsupervised person Re-IDentification
Unsupervised person re-ID is the task of identifying people on a target data
set for which the ID labels are unavailable during training. In this paper, we
propose to unify two trends in unsupervised person re-ID: clustering &
fine-tuning and adversarial learning. On one side, clustering groups training
images into pseudo-ID labels, and uses them to fine-tune the feature extractor.
On the other side, adversarial learning is used, inspired by domain adaptation,
to match distributions from different domains. Since target data is distributed
across different camera viewpoints, we propose to model each camera as an
independent domain, and aim to learn domain-independent features.
Straightforward adversarial learning yields negative transfer, we thus
introduce a conditioning vector to mitigate this undesirable effect. In our
framework, the centroid of the cluster to which the visual sample belongs is
used as conditioning vector of our conditional adversarial network, where the
vector is permutation invariant (clusters ordering does not matter) and its
size is independent of the number of clusters. To our knowledge, we are the
first to propose the use of conditional adversarial networks for unsupervised
person re-ID. We evaluate the proposed architecture on top of two
state-of-the-art clustering-based unsupervised person re-identification (re-ID)
methods on four different experimental settings with three different data sets
and set the new state-of-the-art performance on all four of them. Our code and
model will be made publicly available at
https://team.inria.fr/perception/canu-reid/
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