11,574 research outputs found
Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification
Re-identification is generally carried out by encoding the appearance of a
subject in terms of outfit, suggesting scenarios where people do not change
their attire. In this paper we overcome this restriction, by proposing a
framework based on a deep convolutional neural network, SOMAnet, that
additionally models other discriminative aspects, namely, structural attributes
of the human figure (e.g. height, obesity, gender). Our method is unique in
many respects. First, SOMAnet is based on the Inception architecture, departing
from the usual siamese framework. This spares expensive data preparation
(pairing images across cameras) and allows the understanding of what the
network learned. Second, and most notably, the training data consists of a
synthetic 100K instance dataset, SOMAset, created by photorealistic human body
generation software. Synthetic data represents a good compromise between
realistic imagery, usually not required in re-identification since surveillance
cameras capture low-resolution silhouettes, and complete control of the
samples, which is useful in order to customize the data w.r.t. the surveillance
scenario at-hand, e.g. ethnicity. SOMAnet, trained on SOMAset and fine-tuned on
recent re-identification benchmarks, outperforms all competitors, matching
subjects even with different apparel. The combination of synthetic data with
Inception architectures opens up new research avenues in re-identification.Comment: 14 page
Crossing Generative Adversarial Networks for Cross-View Person Re-identification
Person re-identification (\textit{re-id}) refers to matching pedestrians
across disjoint yet non-overlapping camera views. The most effective way to
match these pedestrians undertaking significant visual variations is to seek
reliably invariant features that can describe the person of interest
faithfully. Most of existing methods are presented in a supervised manner to
produce discriminative features by relying on labeled paired images in
correspondence. However, annotating pair-wise images is prohibitively expensive
in labors, and thus not practical in large-scale networked cameras. Moreover,
seeking comparable representations across camera views demands a flexible model
to address the complex distributions of images. In this work, we study the
co-occurrence statistic patterns between pairs of images, and propose to
crossing Generative Adversarial Network (Cross-GAN) for learning a joint
distribution for cross-image representations in a unsupervised manner. Given a
pair of person images, the proposed model consists of the variational
auto-encoder to encode the pair into respective latent variables, a proposed
cross-view alignment to reduce the view disparity, and an adversarial layer to
seek the joint distribution of latent representations. The learned latent
representations are well-aligned to reflect the co-occurrence patterns of
paired images. We empirically evaluate the proposed model against challenging
datasets, and our results show the importance of joint invariant features in
improving matching rates of person re-id with comparison to semi/unsupervised
state-of-the-arts.Comment: 12 pages. arXiv admin note: text overlap with arXiv:1702.03431 by
other author
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
A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression
Many machine learning problems can be formulated as predicting labels for a
pair of objects. Problems of that kind are often referred to as pairwise
learning, dyadic prediction or network inference problems. During the last
decade kernel methods have played a dominant role in pairwise learning. They
still obtain a state-of-the-art predictive performance, but a theoretical
analysis of their behavior has been underexplored in the machine learning
literature.
In this work we review and unify existing kernel-based algorithms that are
commonly used in different pairwise learning settings, ranging from matrix
filtering to zero-shot learning. To this end, we focus on closed-form efficient
instantiations of Kronecker kernel ridge regression. We show that independent
task kernel ridge regression, two-step kernel ridge regression and a linear
matrix filter arise naturally as a special case of Kronecker kernel ridge
regression, implying that all these methods implicitly minimize a squared loss.
In addition, we analyze universality, consistency and spectral filtering
properties. Our theoretical results provide valuable insights in assessing the
advantages and limitations of existing pairwise learning methods.Comment: arXiv admin note: text overlap with arXiv:1606.0427
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