19,110 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
Support Neighbor Loss for Person Re-Identification
Person re-identification (re-ID) has recently been tremendously boosted due
to the advancement of deep convolutional neural networks (CNN). The majority of
deep re-ID methods focus on designing new CNN architectures, while less
attention is paid on investigating the loss functions. Verification loss and
identification loss are two types of losses widely used to train various deep
re-ID models, both of which however have limitations. Verification loss guides
the networks to generate feature embeddings of which the intra-class variance
is decreased while the inter-class ones is enlarged. However, training networks
with verification loss tends to be of slow convergence and unstable performance
when the number of training samples is large. On the other hand, identification
loss has good separating and scalable property. But its neglect to explicitly
reduce the intra-class variance limits its performance on re-ID, because the
same person may have significant appearance disparity across different camera
views. To avoid the limitations of the two types of losses, we propose a new
loss, called support neighbor (SN) loss. Rather than being derived from data
sample pairs or triplets, SN loss is calculated based on the positive and
negative support neighbor sets of each anchor sample, which contain more
valuable contextual information and neighborhood structure that are beneficial
for more stable performance. To ensure scalability and separability, a
softmax-like function is formulated to push apart the positive and negative
support sets. To reduce intra-class variance, the distance between the anchor's
nearest positive neighbor and furthest positive sample is penalized.
Integrating SN loss on top of Resnet50, superior re-ID results to the
state-of-the-art ones are obtained on several widely used datasets.Comment: Accepted by ACM Multimedia (ACM MM) 201
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting
For person re-identification, existing deep networks often focus on
representation learning. However, without transfer learning, the learned model
is fixed as is, which is not adaptable for handling various unseen scenarios.
In this paper, beyond representation learning, we consider how to formulate
person image matching directly in deep feature maps. We treat image matching as
finding local correspondences in feature maps, and construct query-adaptive
convolution kernels on the fly to achieve local matching. In this way, the
matching process and results are interpretable, and this explicit matching is
more generalizable than representation features to unseen scenarios, such as
unknown misalignments, pose or viewpoint changes. To facilitate end-to-end
training of this architecture, we further build a class memory module to cache
feature maps of the most recent samples of each class, so as to compute image
matching losses for metric learning. Through direct cross-dataset evaluation,
the proposed Query-Adaptive Convolution (QAConv) method gains large
improvements over popular learning methods (about 10%+ mAP), and achieves
comparable results to many transfer learning methods. Besides, a model-free
temporal cooccurrence based score weighting method called TLift is proposed,
which improves the performance to a further extent, achieving state-of-the-art
results in cross-dataset person re-identification. Code is available at
https://github.com/ShengcaiLiao/QAConv.Comment: This is the ECCV 2020 version, including the appendi
Pedestrian Attribute Recognition: A Survey
Recognizing pedestrian attributes is an important task in computer vision
community due to it plays an important role in video surveillance. Many
algorithms has been proposed to handle this task. The goal of this paper is to
review existing works using traditional methods or based on deep learning
networks. Firstly, we introduce the background of pedestrian attributes
recognition (PAR, for short), including the fundamental concepts of pedestrian
attributes and corresponding challenges. Secondly, we introduce existing
benchmarks, including popular datasets and evaluation criterion. Thirdly, we
analyse the concept of multi-task learning and multi-label learning, and also
explain the relations between these two learning algorithms and pedestrian
attribute recognition. We also review some popular network architectures which
have widely applied in the deep learning community. Fourthly, we analyse
popular solutions for this task, such as attributes group, part-based,
\emph{etc}. Fifthly, we shown some applications which takes pedestrian
attributes into consideration and achieve better performance. Finally, we
summarized this paper and give several possible research directions for
pedestrian attributes recognition. The project page of this paper can be found
from the following website:
\url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey:
https://sites.google.com/view/ahu-pedestrianattributes
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