18,625 research outputs found
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation
In real-world crowd counting applications, the crowd densities vary greatly
in spatial and temporal domains. A detection based counting method will
estimate crowds accurately in low density scenes, while its reliability in
congested areas is downgraded. A regression based approach, on the other hand,
captures the general density information in crowded regions. Without knowing
the location of each person, it tends to overestimate the count in low density
areas. Thus, exclusively using either one of them is not sufficient to handle
all kinds of scenes with varying densities. To address this issue, a novel
end-to-end crowd counting framework, named DecideNet (DEteCtIon and Density
Estimation Network) is proposed. It can adaptively decide the appropriate
counting mode for different locations on the image based on its real density
conditions. DecideNet starts with estimating the crowd density by generating
detection and regression based density maps separately. To capture inevitable
variation in densities, it incorporates an attention module, meant to
adaptively assess the reliability of the two types of estimations. The final
crowd counts are obtained with the guidance of the attention module to adopt
suitable estimations from the two kinds of density maps. Experimental results
show that our method achieves state-of-the-art performance on three challenging
crowd counting datasets.Comment: CVPR 201
Accuracy Booster: Performance Boosting using Feature Map Re-calibration
Convolution Neural Networks (CNN) have been extremely successful in solving
intensive computer vision tasks. The convolutional filters used in CNNs have
played a major role in this success, by extracting useful features from the
inputs. Recently researchers have tried to boost the performance of CNNs by
re-calibrating the feature maps produced by these filters, e.g.,
Squeeze-and-Excitation Networks (SENets). These approaches have achieved better
performance by Exciting up the important channels or feature maps while
diminishing the rest. However, in the process, architectural complexity has
increased. We propose an architectural block that introduces much lower
complexity than the existing methods of CNN performance boosting while
performing significantly better than them. We carry out experiments on the
CIFAR, ImageNet and MS-COCO datasets, and show that the proposed block can
challenge the state-of-the-art results. Our method boosts the ResNet-50
architecture to perform comparably to the ResNet-152 architecture, which is a
three times deeper network, on classification. We also show experimentally that
our method is not limited to classification but also generalizes well to other
tasks such as object detection.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
202
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