203 research outputs found
Person Re-identification by Local Maximal Occurrence Representation and Metric Learning
Person re-identification is an important technique towards automatic search
of a person's presence in a surveillance video. Two fundamental problems are
critical for person re-identification, feature representation and metric
learning. An effective feature representation should be robust to illumination
and viewpoint changes, and a discriminant metric should be learned to match
various person images. In this paper, we propose an effective feature
representation called Local Maximal Occurrence (LOMO), and a subspace and
metric learning method called Cross-view Quadratic Discriminant Analysis
(XQDA). The LOMO feature analyzes the horizontal occurrence of local features,
and maximizes the occurrence to make a stable representation against viewpoint
changes. Besides, to handle illumination variations, we apply the Retinex
transform and a scale invariant texture operator. To learn a discriminant
metric, we propose to learn a discriminant low dimensional subspace by
cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is
learned on the derived subspace. We also present a practical computation method
for XQDA, as well as its regularization. Experiments on four challenging person
re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show
that the proposed method improves the state-of-the-art rank-1 identification
rates by 2.2%, 4.88%, 28.91%, and 31.55% on the four databases, respectively.Comment: This paper has been accepted by CVPR 2015. For source codes and
extracted features please visit
http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda
Learning Discriminative Features with Class Encoder
Deep neural networks usually benefit from unsupervised pre-training, e.g.
auto-encoders. However, the classifier further needs supervised fine-tuning
methods for good discrimination. Besides, due to the limits of full-connection,
the application of auto-encoders is usually limited to small, well aligned
images. In this paper, we incorporate the supervised information to propose a
novel formulation, namely class-encoder, whose training objective is to
reconstruct a sample from another one of which the labels are identical.
Class-encoder aims to minimize the intra-class variations in the feature space,
and to learn a good discriminative manifolds on a class scale. We impose the
class-encoder as a constraint into the softmax for better supervised training,
and extend the reconstruction on feature-level to tackle the parameter size
issue and translation issue. The experiments show that the class-encoder helps
to improve the performance on benchmarks of classification and face
recognition. This could also be a promising direction for fast training of face
recognition models.Comment: Accepted by CVPR2016 Workshop of Robust Features for Computer Visio
SFD: Single Shot Scale-invariant Face Detector
This paper presents a real-time face detector, named Single Shot
Scale-invariant Face Detector (SFD), which performs superiorly on various
scales of faces with a single deep neural network, especially for small faces.
Specifically, we try to solve the common problem that anchor-based detectors
deteriorate dramatically as the objects become smaller. We make contributions
in the following three aspects: 1) proposing a scale-equitable face detection
framework to handle different scales of faces well. We tile anchors on a wide
range of layers to ensure that all scales of faces have enough features for
detection. Besides, we design anchor scales based on the effective receptive
field and a proposed equal proportion interval principle; 2) improving the
recall rate of small faces by a scale compensation anchor matching strategy; 3)
reducing the false positive rate of small faces via a max-out background label.
As a consequence, our method achieves state-of-the-art detection performance on
all the common face detection benchmarks, including the AFW, PASCAL face, FDDB
and WIDER FACE datasets, and can run at 36 FPS on a Nvidia Titan X (Pascal) for
VGA-resolution images.Comment: Accepted by ICCV 2017 + its supplementary materials; Updated the
latest results on WIDER FAC
Multi-criterion two-sided matching of Public-Private Partnership infrastructure projects: Criteria and methods
Two kinds of evaluative criteria are associated with Public-Private Partnership (PPP) infrastructure projects, i.e., private evaluative criteria and public evaluative criteria. These evaluative criteria are inversely related, that is, the higher the public benefits; the lower the private surplus. To balance evaluative criteria in the Two-Sided Matching (TSM) decision, this paper develops a quantitative matching decision model to select an optimal matching scheme for PPP infrastructure projects based on the Hesitant Fuzzy Set (HFS) under unknown evaluative criterion weights. In the model, HFS is introduced to describe values of the evaluative criteria and multi-criterion information is fully considered given by groups. The optimal model is built and solved by maximizing the whole deviation of each criterion so that the evaluative criterion weights are determined objectively. Then, the match-degree of the two sides is calculated and a multi-objective optimization model is introduced to select an optimal matching scheme v ia a min-max approach. The results provide new insights and implications of the influence on evaluative criteria in the TSM decision
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