20,827 research outputs found
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
SAMSON: Spectral Absorption-fluorescence Microscopy System for ON-site-imaging of algae
This paper presents SAMSON, a Spectral Absorption-fluorescence Microscopy
System for ON-site-imaging of algae within a water sample. Designed to be
portable and low-cost for on-site use, the optical sub-system of SAMSON
consists of a mixture of low-cost optics and electronics, designed specifically
to capture both fluorescent and absorption responses from a water sample. The
graphical user interface (GUI) sub-system of SAMSON was designed to enable
flexible visualisation of algae in the water sample in real-time, with the
ability to perform fine-grained exposure control and illumination wavelength
selection. We demonstrate SAMSON's capabilities by equipping the system with
two fluorescent illumination sources and seven absorption illumination sources
to enable the capture of multispectral data from six different algae species
(three from the Cyanophyta phylum (blue-green algae) and three from the
Chlorophyta phylum (green algae)). The key benefit of SAMSON is the ability to
perform rapid acquisition of fluorescence and absorption data at different
wavelengths and magnification levels, thus opening the door for machine
learning methods to automatically identify and enumerate different algae in
water samples using this rich wealth of data
Speeding up Context-based Sentence Representation Learning with Non-autoregressive Convolutional Decoding
Context plays an important role in human language understanding, thus it may
also be useful for machines learning vector representations of language. In
this paper, we explore an asymmetric encoder-decoder structure for unsupervised
context-based sentence representation learning. We carefully designed
experiments to show that neither an autoregressive decoder nor an RNN decoder
is required. After that, we designed a model which still keeps an RNN as the
encoder, while using a non-autoregressive convolutional decoder. We further
combine a suite of effective designs to significantly improve model efficiency
while also achieving better performance. Our model is trained on two different
large unlabelled corpora, and in both cases the transferability is evaluated on
a set of downstream NLP tasks. We empirically show that our model is simple and
fast while producing rich sentence representations that excel in downstream
tasks
- …