32,019 research outputs found
Temporal Model Adaptation for Person Re-Identification
Person re-identification is an open and challenging problem in computer
vision. Majority of the efforts have been spent either to design the best
feature representation or to learn the optimal matching metric. Most approaches
have neglected the problem of adapting the selected features or the learned
model over time. To address such a problem, we propose a temporal model
adaptation scheme with human in the loop. We first introduce a
similarity-dissimilarity learning method which can be trained in an incremental
fashion by means of a stochastic alternating directions methods of multipliers
optimization procedure. Then, to achieve temporal adaptation with limited human
effort, we exploit a graph-based approach to present the user only the most
informative probe-gallery matches that should be used to update the model.
Results on three datasets have shown that our approach performs on par or even
better than state-of-the-art approaches while reducing the manual pairwise
labeling effort by about 80%
The impact of cultural intelligence on communication effectiveness, job satisfaction and anxiety for Chinese host country managers working for foreign multinationals
Cultural intelligence (CQ) is an important construct attracting growing attention in academic literature and describing cross-cultural competencies. To date, researchers have only partially tested the relationship between CQ and its dependent variables, such as performance. In this study, the relationship between CQ and communication effectiveness and job satisfaction is measured in a sample of 225 Chinese managers working for foreign multinational enterprises in China. The results show that CQ plays an important role in reducing anxiety and influencing both communication effectiveness and job satisfaction positively. Another outcome is the unexpected influence of anxiety on job satisfaction but not on communication effectiveness. These findings contribute to the development of theory with regard to the CQ construct
Supervised Classification: Quite a Brief Overview
The original problem of supervised classification considers the task of
automatically assigning objects to their respective classes on the basis of
numerical measurements derived from these objects. Classifiers are the tools
that implement the actual functional mapping from these measurements---also
called features or inputs---to the so-called class label---or output. The
fields of pattern recognition and machine learning study ways of constructing
such classifiers. The main idea behind supervised methods is that of learning
from examples: given a number of example input-output relations, to what extent
can the general mapping be learned that takes any new and unseen feature vector
to its correct class? This chapter provides a basic introduction to the
underlying ideas of how to come to a supervised classification problem. In
addition, it provides an overview of some specific classification techniques,
delves into the issues of object representation and classifier evaluation, and
(very) briefly covers some variations on the basic supervised classification
task that may also be of interest to the practitioner
The 2005 AMI system for the transcription of speech in meetings
In this paper we describe the 2005 AMI system for the transcription\ud
of speech in meetings used for participation in the 2005 NIST\ud
RT evaluations. The system was designed for participation in the speech\ud
to text part of the evaluations, in particular for transcription of speech\ud
recorded with multiple distant microphones and independent headset\ud
microphones. System performance was tested on both conference room\ud
and lecture style meetings. Although input sources are processed using\ud
different front-ends, the recognition process is based on a unified system\ud
architecture. The system operates in multiple passes and makes use\ud
of state of the art technologies such as discriminative training, vocal\ud
tract length normalisation, heteroscedastic linear discriminant analysis,\ud
speaker adaptation with maximum likelihood linear regression and minimum\ud
word error rate decoding. In this paper we describe the system performance\ud
on the official development and test sets for the NIST RT05s\ud
evaluations. The system was jointly developed in less than 10 months\ud
by a multi-site team and was shown to achieve very competitive performance
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