7,754 research outputs found
Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification
This paper considers the domain adaptive person re-identification (re-ID)
problem: learning a re-ID model from a labeled source domain and an unlabeled
target domain. Conventional methods are mainly to reduce feature distribution
gap between the source and target domains. However, these studies largely
neglect the intra-domain variations in the target domain, which contain
critical factors influencing the testing performance on the target domain. In
this work, we comprehensively investigate into the intra-domain variations of
the target domain and propose to generalize the re-ID model w.r.t three types
of the underlying invariance, i.e., exemplar-invariance, camera-invariance and
neighborhood-invariance. To achieve this goal, an exemplar memory is introduced
to store features of the target domain and accommodate the three invariance
properties. The memory allows us to enforce the invariance constraints over
global training batch without significantly increasing computation cost.
Experiment demonstrates that the three invariance properties and the proposed
memory are indispensable towards an effective domain adaptation system. Results
on three re-ID domains show that our domain adaptation accuracy outperforms the
state of the art by a large margin. Code is available at:
https://github.com/zhunzhong07/ECNComment: To appear in CVPR 201
Illumination Variation Correction Using Image Synthesis For Unsupervised Domain Adaptive Person Re-Identification
Unsupervised domain adaptive (UDA) person re-identification (re-ID) aims to
learn identity information from labeled images in source domains and apply it
to unlabeled images in a target domain. One major issue with many unsupervised
re-identification methods is that they do not perform well relative to large
domain variations such as illumination, viewpoint, and occlusions. In this
paper, we propose a Synthesis Model Bank (SMB) to deal with illumination
variation in unsupervised person re-ID. The proposed SMB consists of several
convolutional neural networks (CNN) for feature extraction and Mahalanobis
matrices for distance metrics. They are trained using synthetic data with
different illumination conditions such that their synergistic effect makes the
SMB robust against illumination variation. To better quantify the illumination
intensity and improve the quality of synthetic images, we introduce a new 3D
virtual-human dataset for GAN-based image synthesis. From our experiments, the
proposed SMB outperforms other synthesis methods on several re-ID benchmarks.Comment: 10 pages, 5 figures, 5 table
Making the Invisible Visible: Inviting Persons with Disabilities into the Life of the Church
Christianity espouses the dignity of all humanity and professes welcome for all to the communion of saints. Yet people with disabilities, especially those with more severe or profound physical or psychological disabilities, are largely invisible inside our houses of worship. This article examines the meaning of dignity and inclusion through the lenses of Christian anthropology, disabilities liberation theology, and the lived experience of persons with disabilities. It concludes with some suggestions on how to begin inclusion
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
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