2,362 research outputs found
Generative Adversarial Models for People Attribute Recognition in Surveillance
In this paper we propose a deep architecture for detecting people attributes (e.g. gender, race, clothing ...) in surveillance contexts. Our proposal explicitly deal with poor resolution and occlusion issues that often occur in surveillance footages by enhancing the images by means of Deep Convolutional Generative Adversarial Networks (DCGAN). Experiments show that by combining both our Generative Reconstruction and Deep Attribute Classification Network we can effectively extract attributes even when resolution is poor and in presence of strong occlusions up to 80% of the whole person figure
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
Prominent Attribute Modification using Attribute Dependent Generative Adversarial Network
Modifying the facial images with desired attributes is important, though
challenging tasks in computer vision, where it aims to modify single or
multiple attributes of the face image. Some of the existing methods are either
based on attribute independent approaches where the modification is done in the
latent representation or attribute dependent approaches. The attribute
independent methods are limited in performance as they require the desired
paired data for changing the desired attributes. Secondly, the attribute
independent constraint may result in the loss of information and, hence, fail
in generating the required attributes in the face image. In contrast, the
attribute dependent approaches are effective as these approaches are capable of
modifying the required features along with preserving the information in the
given image. However, attribute dependent approaches are sensitive and require
a careful model design in generating high-quality results. To address this
problem, we propose an attribute dependent face modification approach. The
proposed approach is based on two generators and two discriminators that
utilize the binary as well as the real representation of the attributes and, in
return, generate high-quality attribute modification results. Experiments on
the CelebA dataset show that our method effectively performs the multiple
attribute editing with preserving other facial details intactly
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