17,804 research outputs found
Unsupervised Person Re-identification by Deep Learning Tracklet Association
Mostexistingpersonre-identification(re-id)methods relyon supervised model
learning on per-camera-pair manually labelled pairwise training data. This
leads to poor scalability in practical re-id deployment due to the lack of
exhaustive identity labelling of image positive and negative pairs for every
camera pair. In this work, we address this problem by proposing an unsupervised
re-id deep learning approach capable of incrementally discovering and
exploiting the underlying re-id discriminative information from automatically
generated person tracklet data from videos in an end-to-end model optimisation.
We formulate a Tracklet Association Unsupervised Deep Learning (TAUDL)
framework characterised by jointly learning per-camera (within-camera) tracklet
association (labelling) and cross-camera tracklet correlation by maximising the
discovery of most likely tracklet relationships across camera views. Extensive
experiments demonstrate the superiority of the proposed TAUDL model over the
state-of-the-art unsupervised and domain adaptation re- id methods using six
person re-id benchmarking datasets.Comment: ECCV 2018 Ora
Unsupervised Domain Adaptive Re-Identification: Theory and Practice
We study the problem of unsupervised domain adaptive re-identification
(re-ID) which is an active topic in computer vision but lacks a theoretical
foundation. We first extend existing unsupervised domain adaptive
classification theories to re-ID tasks. Concretely, we introduce some
assumptions on the extracted feature space and then derive several loss
functions guided by these assumptions. To optimize them, a novel self-training
scheme for unsupervised domain adaptive re-ID tasks is proposed. It iteratively
makes guesses for unlabeled target data based on an encoder and trains the
encoder based on the guessed labels. Extensive experiments on unsupervised
domain adaptive person re-ID and vehicle re-ID tasks with comparisons to the
state-of-the-arts confirm the effectiveness of the proposed theories and
self-training framework. Our code is available at
\url{https://github.com/LcDog/DomainAdaptiveReID}
Weakly Supervised Person Re-Identification
In the conventional person re-id setting, it is assumed that the labeled
images are the person images within the bounding box for each individual; this
labeling across multiple nonoverlapping camera views from raw video
surveillance is costly and time-consuming. To overcome this difficulty, we
consider weakly supervised person re-id modeling. The weak setting refers to
matching a target person with an untrimmed gallery video where we only know
that the identity appears in the video without the requirement of annotating
the identity in any frame of the video during the training procedure. Hence,
for a video, there could be multiple video-level labels. We cast this weakly
supervised person re-id challenge into a multi-instance multi-label learning
(MIML) problem. In particular, we develop a Cross-View MIML (CV-MIML) method
that is able to explore potential intraclass person images from all the camera
views by incorporating the intra-bag alignment and the cross-view bag
alignment. Finally, the CV-MIML method is embedded into an existing deep neural
network for developing the Deep Cross-View MIML (Deep CV-MIML) model. We have
performed extensive experiments to show the feasibility of the proposed weakly
supervised setting and verify the effectiveness of our method compared to
related methods on four weakly labeled datasets.Comment: to appear at CVPR1
Transfer Metric Learning: Algorithms, Applications and Outlooks
Distance metric learning (DML) aims to find an appropriate way to reveal the
underlying data relationship. It is critical in many machine learning, pattern
recognition and data mining algorithms, and usually require large amount of
label information (such as class labels or pair/triplet constraints) to achieve
satisfactory performance. However, the label information may be insufficient in
real-world applications due to the high-labeling cost, and DML may fail in this
case. Transfer metric learning (TML) is able to mitigate this issue for DML in
the domain of interest (target domain) by leveraging knowledge/information from
other related domains (source domains). Although achieved a certain level of
development, TML has limited success in various aspects such as selective
transfer, theoretical understanding, handling complex data, big data and
extreme cases. In this survey, we present a systematic review of the TML
literature. In particular, we group TML into different categories according to
different settings and metric transfer strategies, such as direct metric
approximation, subspace approximation, distance approximation, and distribution
approximation. A summarization and insightful discussion of the various TML
approaches and their applications will be presented. Finally, we indicate some
challenges and provide possible future directions.Comment: 14 pages, 5 figure
Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification
Person re-identification (Re-ID) aims at recognizing the same person from
images taken across different cameras. To address this task, one typically
requires a large amount labeled data for training an effective Re-ID model,
which might not be practical for real-world applications. To alleviate this
limitation, we choose to exploit a sufficient amount of pre-existing labeled
data from a different (auxiliary) dataset. By jointly considering such an
auxiliary dataset and the dataset of interest (but without label information),
our proposed adaptation and re-identification network (ARN) performs
unsupervised domain adaptation, which leverages information across datasets and
derives domain-invariant features for Re-ID purposes. In our experiments, we
verify that our network performs favorably against state-of-the-art
unsupervised Re-ID approaches, and even outperforms a number of baseline Re-ID
methods which require fully supervised data for training.Comment: 7 pages, 3 figures. CVPR 2018 workshop pape
Domain Adaptive Person Re-Identification via Camera Style Generation and Label Propagation
Unsupervised domain adaptation in person re-identification resorts to labeled
source data to promote the model training on target domain, facing the dilemmas
caused by large domain shift and large camera variations. The non-overlapping
labels challenge that source domain and target domain have entirely different
persons further increases the re-identification difficulty. In this paper, we
propose a novel algorithm to narrow such domain gaps. We derive a camera style
adaptation framework to learn the style-based mappings between different camera
views, from the target domain to the source domain, and then we can transfer
the identity-based distribution from the source domain to the target domain on
the camera level. To overcome the non-overlapping labels challenge and guide
the person re-identification model to narrow the gap further, an efficient and
effective soft-labeling method is proposed to mine the intrinsic local
structure of the target domain through building the connection between
GAN-translated source domain and the target domain. Experiment results
conducted on real benchmark datasets indicate that our method gets
state-of-the-art results
Transfer Adaptation Learning: A Decade Survey
The world we see is ever-changing and it always changes with people, things,
and the environment. Domain is referred to as the state of the world at a
certain moment. A research problem is characterized as transfer adaptation
learning (TAL) when it needs knowledge correspondence between different
moments/domains. Conventional machine learning aims to find a model with the
minimum expected risk on test data by minimizing the regularized empirical risk
on the training data, which, however, supposes that the training and test data
share similar joint probability distribution. TAL aims to build models that can
perform tasks of target domain by learning knowledge from a semantic related
but distribution different source domain. It is an energetic research filed of
increasing influence and importance, which is presenting a blowout publication
trend. This paper surveys the advances of TAL methodologies in the past decade,
and the technical challenges and essential problems of TAL have been observed
and discussed with deep insights and new perspectives. Broader solutions of
transfer adaptation learning being created by researchers are identified, i.e.,
instance re-weighting adaptation, feature adaptation, classifier adaptation,
deep network adaptation and adversarial adaptation, which are beyond the early
semi-supervised and unsupervised split. The survey helps researchers rapidly
but comprehensively understand and identify the research foundation, research
status, theoretical limitations, future challenges and under-studied issues
(universality, interpretability, and credibility) to be broken in the field
toward universal representation and safe applications in open-world scenarios.Comment: 26 pages, 4 figure
Imitating Targets from all sides: An Unsupervised Transfer Learning method for Person Re-identification
Person re-identification (Re-ID) models usually show a limited performance
when they are trained on one dataset and tested on another dataset due to the
inter-dataset bias (e.g. completely different identities and backgrounds) and
the intra-dataset difference (e.g. camera invariance). In terms of this issue,
given a labelled source training set and an unlabelled target training set, we
propose an unsupervised transfer learning method characterized by 1) bridging
inter-dataset bias and intra-dataset difference via a proposed ImitateModel
simultaneously; 2) regarding the unsupervised person Re-ID problem as a
semi-supervised learning problem formulated by a dual classification loss to
learn a discriminative representation across domains; 3) exploiting the
underlying commonality across different domains from the class-style space to
improve the generalization ability of re-ID models. Extensive experiments are
conducted on two widely employed benchmarks, including Market-1501 and
DukeMTMC-reID, and experimental results demonstrate that the proposed method
can achieve a competitive performance against other state-of-the-art
unsupervised Re-ID approaches
Learning to Align Multi-Camera Domains using Part-Aware Clustering for Unsupervised Video Person Re-Identification
Most video person re-identification (re-ID) methods are mainly based on
supervised learning, which requires cross-camera ID labeling. Since the cost of
labeling increases dramatically as the number of cameras increases, it is
difficult to apply the re-identification algorithm to a large camera network.
In this paper, we address the scalability issue by presenting deep
representation learning without ID information across multiple cameras.
Technically, we train neural networks to generate both ID-discriminative and
camera-invariant features. To achieve the ID discrimination ability of the
embedding features, we maximize feature distances between different person IDs
within a camera by using a metric learning approach. At the same time,
considering each camera as a different domain, we apply adversarial learning
across multiple camera domains for generating camera-invariant features. We
also propose a part-aware adaptation module, which effectively performs
multi-camera domain invariant feature learning in different spatial regions. We
carry out comprehensive experiments on three public re-ID datasets (i.e.,
PRID-2011, iLIDS-VID, and MARS). Our method outperforms state-of-the-art
methods by a large margin of about 20\% in terms of rank-1 accuracy on the
large-scale MARS dataset
Frustratingly Easy Person Re-Identification: Generalizing Person Re-ID in Practice
Contemporary person re-identification (\reid) methods usually require access
to data from the deployment camera network during training in order to perform
well. This is because contemporary \reid{} models trained on one dataset do not
generalise to other camera networks due to the domain-shift between datasets.
This requirement is often the bottleneck for deploying \reid{} systems in
practical security or commercial applications, as it may be impossible to
collect this data in advance or prohibitively costly to annotate it. This paper
alleviates this issue by proposing a simple baseline for domain
generalizable~(DG) person re-identification. That is, to learn a \reid{} model
from a set of source domains that is suitable for application to unseen
datasets out-of-the-box, without any model updating. Specifically, we observe
that the domain discrepancy in \reid{} is due to style and content variance
across datasets and demonstrate appropriate Instance and Feature Normalization
alleviates much of the resulting domain-shift in Deep \reid{} models. Instance
Normalization~(IN) in early layers filters out style statistic variations and
Feature Normalization~(FN) in deep layers is able to further eliminate
disparity in content statistics. Compared to contemporary alternatives, this
approach is extremely simple to implement, while being faster to train and
test, thus making it an extremely valuable baseline for implementing \reid{} in
practice. With a few lines of code, it increases the rank 1 \reid{} accuracy by
{11.8\%, 33.2\%, 12.8\% and 8.5\%} on the VIPeR, PRID, GRID, and i-LIDS
benchmarks respectively. Source codes are available at
\url{https://github.com/BJTUJia/person_reID_DualNorm}.Comment: 14 pages,2 figure
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