16,030 research outputs found
Multi-View Region Adaptive Multi-temporal DMM and RGB Action Recognition
Human action recognition remains an important yet challenging task. This work
proposes a novel action recognition system. It uses a novel Multiple View
Region Adaptive Multi-resolution in time Depth Motion Map (MV-RAMDMM)
formulation combined with appearance information. Multiple stream 3D
Convolutional Neural Networks (CNNs) are trained on the different views and
time resolutions of the region adaptive Depth Motion Maps. Multiple views are
synthesised to enhance the view invariance. The region adaptive weights, based
on localised motion, accentuate and differentiate parts of actions possessing
faster motion. Dedicated 3D CNN streams for multi-time resolution appearance
information (RGB) are also included. These help to identify and differentiate
between small object interactions. A pre-trained 3D-CNN is used here with
fine-tuning for each stream along with multiple class Support Vector Machines
(SVM)s. Average score fusion is used on the output. The developed approach is
capable of recognising both human action and human-object interaction. Three
public domain datasets including: MSR 3D Action,Northwestern UCLA multi-view
actions and MSR 3D daily activity are used to evaluate the proposed solution.
The experimental results demonstrate the robustness of this approach compared
with state-of-the-art algorithms.Comment: 14 pages, 6 figures, 13 tables. Submitte
A Gauss-Jacobi Kernel Compression Scheme for Fractional Differential Equations
A scheme for approximating the kernel of the fractional -integral
by a linear combination of exponentials is proposed and studied. The scheme is
based on the application of a composite Gauss-Jacobi quadrature rule to an
integral representation of . This results in an approximation of in an
interval , with , which converges rapidly in the number
of quadrature nodes associated with each interval of the composite rule.
Using error analysis for Gauss-Jacobi quadratures for analytic functions, an
estimate of the relative pointwise error is obtained. The estimate shows that
the number of terms required for the approximation to satisfy a prescribed
error tolerance is bounded for all , and that is bounded
for , , and
A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction
In spite of its importance, passenger demand prediction is a highly
challenging problem, because the demand is simultaneously influenced by the
complex interactions among many spatial and temporal factors and other external
factors such as weather. To address this problem, we propose a Spatio-TEmporal
Fuzzy neural Network (STEF-Net) to accurately predict passenger demands
incorporating the complex interactions of all known important factors. We
design an end-to-end learning framework with different neural networks modeling
different factors. Specifically, we propose to capture spatio-temporal feature
interactions via a convolutional long short-term memory network and model
external factors via a fuzzy neural network that handles data uncertainty
significantly better than deterministic methods. To keep the temporal relations
when fusing two networks and emphasize discriminative spatio-temporal feature
interactions, we employ a novel feature fusion method with a convolution
operation and an attention layer. As far as we know, our work is the first to
fuse a deep recurrent neural network and a fuzzy neural network to model
complex spatial-temporal feature interactions with additional uncertain input
features for predictive learning. Experiments on a large-scale real-world
dataset show that our model achieves more than 10% improvement over the
state-of-the-art approaches.Comment: https://epubs.siam.org/doi/abs/10.1137/1.9781611975673.1
Graph Convolutional Matrix Completion
We consider matrix completion for recommender systems from the point of view
of link prediction on graphs. Interaction data such as movie ratings can be
represented by a bipartite user-item graph with labeled edges denoting observed
ratings. Building on recent progress in deep learning on graph-structured data,
we propose a graph auto-encoder framework based on differentiable message
passing on the bipartite interaction graph. Our model shows competitive
performance on standard collaborative filtering benchmarks. In settings where
complimentary feature information or structured data such as a social network
is available, our framework outperforms recent state-of-the-art methods.Comment: 9 pages, 3 figures, updated with additional experimental evaluatio
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