208,025 research outputs found
A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label Learning
In reality, learning from multi-view multi-label data inevitably confronts
three challenges: missing labels, incomplete views, and non-aligned views.
Existing methods mainly concern the first two and commonly need multiple
assumptions to attack them, making even state-of-the-arts involve at least two
explicit hyper-parameters such that model selection is quite difficult. More
roughly, they will fail in handling the third challenge, let alone addressing
the three jointly. In this paper, we aim at meeting these under the least
assumption by building a concise yet effective model with just one
hyper-parameter. To ease insufficiency of available labels, we exploit not only
the consensus of multiple views but also the global and local structures hidden
among multiple labels. Specifically, we introduce an indicator matrix to tackle
the first two challenges in a regression form while aligning the same
individual labels and all labels of different views in a common label space to
battle the third challenge. In aligning, we characterize the global and local
structures of multiple labels to be high-rank and low-rank, respectively.
Subsequently, an efficient algorithm with linear time complexity in the number
of samples is established. Finally, even without view-alignment, our method
substantially outperforms state-of-the-arts with view-alignment on five real
datasets.Comment: 15 pages, 7 figure
DeepWalk: Online Learning of Social Representations
We present DeepWalk, a novel approach for learning latent representations of
vertices in a network. These latent representations encode social relations in
a continuous vector space, which is easily exploited by statistical models.
DeepWalk generalizes recent advancements in language modeling and unsupervised
feature learning (or deep learning) from sequences of words to graphs. DeepWalk
uses local information obtained from truncated random walks to learn latent
representations by treating walks as the equivalent of sentences. We
demonstrate DeepWalk's latent representations on several multi-label network
classification tasks for social networks such as BlogCatalog, Flickr, and
YouTube. Our results show that DeepWalk outperforms challenging baselines which
are allowed a global view of the network, especially in the presence of missing
information. DeepWalk's representations can provide scores up to 10%
higher than competing methods when labeled data is sparse. In some experiments,
DeepWalk's representations are able to outperform all baseline methods while
using 60% less training data. DeepWalk is also scalable. It is an online
learning algorithm which builds useful incremental results, and is trivially
parallelizable. These qualities make it suitable for a broad class of real
world applications such as network classification, and anomaly detection.Comment: 10 pages, 5 figures, 4 table
Learning to Purification for Unsupervised Person Re-identification
Unsupervised person re-identification is a challenging and promising task in
computer vision. Nowadays unsupervised person re-identification methods have
achieved great progress by training with pseudo labels. However, how to purify
feature and label noise is less explicitly studied in the unsupervised manner.
To purify the feature, we take into account two types of additional features
from different local views to enrich the feature representation. The proposed
multi-view features are carefully integrated into our cluster contrast learning
to leverage more discriminative cues that the global feature easily ignored and
biased. To purify the label noise, we propose to take advantage of the
knowledge of teacher model in an offline scheme. Specifically, we first train a
teacher model from noisy pseudo labels, and then use the teacher model to guide
the learning of our student model. In our setting, the student model could
converge fast with the supervision of the teacher model thus reduce the
interference of noisy labels as the teacher model greatly suffered. After
carefully handling the noise and bias in the feature learning, our purification
modules are proven to be very effective for unsupervised person
re-identification. Extensive experiments on three popular person
re-identification datasets demonstrate the superiority of our method.
Especially, our approach achieves a state-of-the-art accuracy 85.8\% @mAP and
94.5\% @Rank-1 on the challenging Market-1501 benchmark with ResNet-50 under
the fully unsupervised setting. The code will be released
Person Re-Identification by Deep Joint Learning of Multi-Loss Classification
Existing person re-identification (re-id) methods rely mostly on either
localised or global feature representation alone. This ignores their joint
benefit and mutual complementary effects. In this work, we show the advantages
of jointly learning local and global features in a Convolutional Neural Network
(CNN) by aiming to discover correlated local and global features in different
context. Specifically, we formulate a method for joint learning of local and
global feature selection losses designed to optimise person re-id when using
only generic matching metrics such as the L2 distance. We design a novel CNN
architecture for Jointly Learning Multi-Loss (JLML) of local and global
discriminative feature optimisation subject concurrently to the same re-id
labelled information. Extensive comparative evaluations demonstrate the
advantages of this new JLML model for person re-id over a wide range of
state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03,
Market-1501).Comment: Accepted by IJCAI 201
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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