45,176 research outputs found
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
Person re-identification (re-id) aims to match pedestrians observed by
disjoint camera views. It attracts increasing attention in computer vision due
to its importance to surveillance system. To combat the major challenge of
cross-view visual variations, deep embedding approaches are proposed by
learning a compact feature space from images such that the Euclidean distances
correspond to their cross-view similarity metric. However, the global Euclidean
distance cannot faithfully characterize the ideal similarity in a complex
visual feature space because features of pedestrian images exhibit unknown
distributions due to large variations in poses, illumination and occlusion.
Moreover, intra-personal training samples within a local range are robust to
guide deep embedding against uncontrolled variations, which however, cannot be
captured by a global Euclidean distance. In this paper, we study the problem of
person re-id by proposing a novel sampling to mine suitable \textit{positives}
(i.e. intra-class) within a local range to improve the deep embedding in the
context of large intra-class variations. Our method is capable of learning a
deep similarity metric adaptive to local sample structure by minimizing each
sample's local distances while propagating through the relationship between
samples to attain the whole intra-class minimization. To this end, a novel
objective function is proposed to jointly optimize similarity metric learning,
local positive mining and robust deep embedding. This yields local
discriminations by selecting local-ranged positive samples, and the learned
features are robust to dramatic intra-class variations. Experiments on
benchmarks show state-of-the-art results achieved by our method.Comment: Published on Pattern Recognitio
Blind Estimation of Multiple Carrier Frequency Offsets
Multiple carrier-frequency offsets (CFO) arise in a distributed antenna
system, where data are transmitted simultaneously from multiple antennas. In
such systems the received signal contains multiple CFOs due to mismatch between
the local oscillators of transmitters and receiver. This results in a
time-varying rotation of the data constellation, which needs to be compensated
for at the receiver before symbol recovery. This paper proposes a new approach
for blind CFO estimation and symbol recovery. The received base-band signal is
over-sampled, and its polyphase components are used to formulate a virtual
Multiple-Input Multiple-Output (MIMO) problem. By applying blind MIMO system
estimation techniques, the system response is estimated and used to
subsequently transform the multiple CFOs estimation problem into many
independent single CFO estimation problems. Furthermore, an initial estimate of
the CFO is obtained from the phase of the MIMO system response. The Cramer-Rao
Lower bound is also derived, and the large sample performance of the proposed
estimator is compared to the bound.Comment: To appear in the Proceedings of the 18th Annual IEEE International
Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC),
Athens, Greece, September 3-7, 200
Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-Identification
In person re-identification (ReID) task, because of its shortage of trainable
dataset, it is common to utilize fine-tuning method using a classification
network pre-trained on a large dataset. However, it is relatively difficult to
sufficiently fine-tune the low-level layers of the network due to the gradient
vanishing problem. In this work, we propose a novel fine-tuning strategy that
allows low-level layers to be sufficiently trained by rolling back the weights
of high-level layers to their initial pre-trained weights. Our strategy
alleviates the problem of gradient vanishing in low-level layers and robustly
trains the low-level layers to fit the ReID dataset, thereby increasing the
performance of ReID tasks. The improved performance of the proposed strategy is
validated via several experiments. Furthermore, without any add-ons such as
pose estimation or segmentation, our strategy exhibits state-of-the-art
performance using only vanilla deep convolutional neural network architecture.Comment: Accepted to AAAI 201
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