2,055 research outputs found
Learning models for semantic classification of insufficient plantar pressure images
Establishing a reliable and stable model to predict a target by using insufficient labeled samples is feasible and
effective, particularly, for a sensor-generated data-set. This paper has been inspired with insufficient data-set
learning algorithms, such as metric-based, prototype networks and meta-learning, and therefore we propose
an insufficient data-set transfer model learning method. Firstly, two basic models for transfer learning are
introduced. A classification system and calculation criteria are then subsequently introduced. Secondly, a dataset
of plantar pressure for comfort shoe design is acquired and preprocessed through foot scan system; and by
using a pre-trained convolution neural network employing AlexNet and convolution neural network (CNN)-
based transfer modeling, the classification accuracy of the plantar pressure images is over 93.5%. Finally,
the proposed method has been compared to the current classifiers VGG, ResNet, AlexNet and pre-trained
CNN. Also, our work is compared with known-scaling and shifting (SS) and unknown-plain slot (PS) partition
methods on the public test databases: SUN, CUB, AWA1, AWA2, and aPY with indices of precision (tr, ts, H)
and time (training and evaluation). The proposed method for the plantar pressure classification task shows high
performance in most indices when comparing with other methods. The transfer learning-based method can be
applied to other insufficient data-sets of sensor imaging fields
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
Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain
It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Moreover, little is known about how such preferences are actually constructed in the brain. Here we developed and tested a computational framework to gain an understanding of how the human brain constructs aesthetic value. We show that it is possible to explain human preferences for a piece of art based on an analysis of features present in the image. This was achieved by analyzing the visual properties of drawings and photographs by multiple means, ranging from image statistics extracted by computer vision tools, subjective human ratings about attributes, to a deep convolutional neural network. Crucially, it is possible to predict subjective value ratings not only within but also across individuals, speaking to the possibility that much of the variance in human visual preference is shared across individuals. Neuroimaging data revealed that preference computations occur in the brain by means of a graded hierarchical representation of lower and higher level features in the visual system. These features are in turn integrated to compute an overall subjective preference in the parietal and prefrontal cortex. Our findings suggest that rather than being idiosyncratic, human preferences for art can be explained at least in part as a product of a systematic neural integration over underlying visual features of an image. This work not only advances our understanding of the brain-wide computations underlying value construction but also brings new mechanistic insights to the study of visual aesthetics and art appreciation
Affect recognition & generation in-the-wild
Affect recognition based on a subject’s facial expressions has been a topic of major research in the attempt to generate machines that can understand the way subjects feel, act and react. In the past, due to the unavailability of large amounts of data captured in real-life situations, research has mainly focused on controlled environments. However, recently, social media and platforms have been widely used. Moreover, deep learning has emerged as a means to solve visual analysis and recognition problems. This Ph.D. Thesis exploits these advances and makes significant contributions for affect analysis and recognition in-the-wild.
We tackle affect analysis and recognition as a dual knowledge generation problem: i) we create new, large and rich in-the-wild databases and ii) we design and train novel deep neural architectures that are able to analyse affect over these databases and to successfully generalise their performance on other datasets.
At first, we present the creation of Aff-Wild database annotated according to valence-arousal and an end-to-end CNN-RNN architecture, AffWildNet. Then we use AffWildNet as a robust prior for dimensional and categorical affect recognition and extend it by extracting low-/mid-/high-level latent information and analysing this via multiple RNNs. Additionally, we propose a novel loss function for DNN-based categorical affect recognition.
Next, we generate Aff-Wild2, the first database containing annotations for all main behavior tasks: estimate Valence-Arousal; classify into Basic Expressions; detect Action Units. We develop multi-task and multi-modal extensions of AffWildNet by fusing these tasks and propose a novel holistic approach that utilises all existing databases with non-overlapping annotations and couples them through co-annotation and distribution matching.
Finally, we present an approach for valence-arousal, or basic expressions’ facial affect synthesis. We generate an image with a given affect, or a sequence of images with evolving affect, by annotating a 4-D database and utilising a 3-D morphable model.Open Acces
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