2,595 research outputs found
Cross-Country Skiing Gears Classification using Deep Learning
Human Activity Recognition has witnessed a significant progress in the last
decade. Although a great deal of work in this field goes in recognizing normal
human activities, few studies focused on identifying motion in sports.
Recognizing human movements in different sports has high impact on
understanding the different styles of humans in the play and on improving their
performance. As deep learning models proved to have good results in many
classification problems, this paper will utilize deep learning to classify
cross-country skiing movements, known as gears, collected using a 3D
accelerometer. It will also provide a comparison between different deep
learning models such as convolutional and recurrent neural networks versus
standard multi-layer perceptron. Results show that deep learning is more
effective and has the highest classification accuracy.Comment: 15 pages, 8 figures, 1 tabl
Permutation Matters: Anisotropic Convolutional Layer for Learning on Point Clouds
It has witnessed a growing demand for efficient representation learning on
point clouds in many 3D computer vision applications. Behind the success story
of convolutional neural networks (CNNs) is that the data (e.g., images) are
Euclidean structured. However, point clouds are irregular and unordered.
Various point neural networks have been developed with isotropic filters or
using weighting matrices to overcome the structure inconsistency on point
clouds. However, isotropic filters or weighting matrices limit the
representation power. In this paper, we propose a permutable anisotropic
convolutional operation (PAI-Conv) that calculates soft-permutation matrices
for each point using dot-product attention according to a set of evenly
distributed kernel points on a sphere's surface and performs shared anisotropic
filters. In fact, dot product with kernel points is by analogy with the
dot-product with keys in Transformer as widely used in natural language
processing (NLP). From this perspective, PAI-Conv can be regarded as the
transformer for point clouds, which is physically meaningful and is robust to
cooperate with the efficient random point sampling method. Comprehensive
experiments on point clouds demonstrate that PAI-Conv produces competitive
results in classification and semantic segmentation tasks compared to
state-of-the-art methods
LSANet: Feature Learning on Point Sets by Local Spatial Aware Layer
Directly learning features from the point cloud has become an active research
direction in 3D understanding. Existing learning-based methods usually
construct local regions from the point cloud and extract the corresponding
features. However, most of these processes do not adequately take the spatial
distribution of the point cloud into account, limiting the ability to perceive
fine-grained patterns. We design a novel Local Spatial Aware (LSA) layer, which
can learn to generate Spatial Distribution Weights (SDWs) hierarchically based
on the spatial relationship in local region for spatial independent operations,
to establish the relationship between these operations and spatial
distribution, thus capturing the local geometric structure sensitively.We
further propose the LSANet, which is based on LSA layer, aggregating the
spatial information with associated features in each layer of the network
better in network design.The experiments show that our LSANet can achieve on
par or better performance than the state-of-the-art methods when evaluating on
the challenging benchmark datasets. For example, our LSANet can achieve 93.2%
accuracy on ModelNet40 dataset using only 1024 points, significantly higher
than other methods under the same conditions. The source code is available at
https://github.com/LinZhuoChen/LSANet
On the Importance of Consistency in Training Deep Neural Networks
We explain that the difficulties of training deep neural networks come from a
syndrome of three consistency issues. This paper describes our efforts in their
analysis and treatment. The first issue is the training speed inconsistency in
different layers. We propose to address it with an intuitive,
simple-to-implement, low footprint second-order method. The second issue is the
scale inconsistency between the layer inputs and the layer residuals. We
explain how second-order information provides favorable convenience in removing
this roadblock. The third and most challenging issue is the inconsistency in
residual propagation. Based on the fundamental theorem of linear algebra, we
provide a mathematical characterization of the famous vanishing gradient
problem. Thus, an important design principle for future optimization and neural
network design is derived. We conclude this paper with the construction of a
novel contractive neural network
Relation-Shape Convolutional Neural Network for Point Cloud Analysis
Point cloud analysis is very challenging, as the shape implied in irregular
points is difficult to capture. In this paper, we propose RS-CNN, namely,
Relation-Shape Convolutional Neural Network, which extends regular grid CNN to
irregular configuration for point cloud analysis. The key to RS-CNN is learning
from relation, i.e., the geometric topology constraint among points.
Specifically, the convolutional weight for local point set is forced to learn a
high-level relation expression from predefined geometric priors, between a
sampled point from this point set and the others. In this way, an inductive
local representation with explicit reasoning about the spatial layout of points
can be obtained, which leads to much shape awareness and robustness. With this
convolution as a basic operator, RS-CNN, a hierarchical architecture can be
developed to achieve contextual shape-aware learning for point cloud analysis.
Extensive experiments on challenging benchmarks across three tasks verify
RS-CNN achieves the state of the arts.Comment: Accepted to CVPR 2019 as an oral presentation. Project page at
https://yochengliu.github.io/Relation-Shape-CN
1D-Convolutional Capsule Network for Hyperspectral Image Classification
Recently, convolutional neural networks (CNNs) have achieved excellent
performances in many computer vision tasks. Specifically, for hyperspectral
images (HSIs) classification, CNNs often require very complex structure due to
the high dimension of HSIs. The complex structure of CNNs results in
prohibitive training efforts. Moreover, the common situation in HSIs
classification task is the lack of labeled samples, which results in accuracy
deterioration of CNNs. In this work, we develop an easy-to-implement capsule
network to alleviate the aforementioned problems, i.e., 1D-convolution capsule
network (1D-ConvCapsNet). Firstly, 1D-ConvCapsNet separately extracts spatial
and spectral information on spatial and spectral domains, which is more
lightweight than 3D-convolution due to fewer parameters. Secondly,
1D-ConvCapsNet utilizes the capsule-wise constraint window method to reduce
parameter amount and computational complexity of conventional capsule network.
Finally, 1D-ConvCapsNet obtains accurate predictions with respect to input
samples via dynamic routing. The effectiveness of the 1D-ConvCapsNet is
verified by three representative HSI datasets. Experimental results demonstrate
that 1D-ConvCapsNet is superior to state-of-the-art methods in both the
accuracy and training effort
Deep transfer learning in the assessment of the quality of protein models
MOTIVATION: Proteins fold into complex structures that are crucial for their
biological functions. Experimental determination of protein structures is
costly and therefore limited to a small fraction of all known proteins. Hence,
different computational structure prediction methods are necessary for the
modelling of the vast majority of all proteins. In most structure prediction
pipelines, the last step is to select the best available model and to estimate
its accuracy. This model quality estimation problem has been growing in
importance during the last decade, and progress is believed to be important for
large scale modelling of proteins. The current generation of model quality
estimation programs performs well at separating incorrect and good models, but
fails to consistently identify the best possible model. State-of-the-art model
quality assessment methods use a combination of features that describe a model
and the agreement of the model with features predicted from the protein
sequence.
RESULTS: We first introduce a deep neural network architecture to predict
model quality using significantly fewer input features than state-of-the-art
methods. Thereafter, we propose a methodology to train the deep network that
leverages the comparative structure of the problem. We also show the
possibility of applying transfer learning on databases of known protein
structures. We demonstrate its viability by reaching state-of-the-art
performance using only a reduced set of input features and a coarse description
of the models.
AVAILABILITY: The code will be freely available for download at
github.com/ElofssonLab/ProQ4
End-to-end Recurrent Multi-Object Tracking and Trajectory Prediction with Relational Reasoning
The majority of contemporary object-tracking approaches do not model
interactions between objects. This contrasts with the fact that objects' paths
are not independent: a cyclist might abruptly deviate from a previously planned
trajectory in order to avoid colliding with a car. Building upon HART, a neural
class-agnostic single-object tracker, we introduce a multi-object tracking
method MOHART capable of relational reasoning. Importantly, the entire system,
including the understanding of interactions and relations between objects, is
class-agnostic and learned simultaneously in an end-to-end fashion. We explore
a number of relational reasoning architectures and show that
permutation-invariant models outperform non-permutation-invariant alternatives.
We also find that architectures using a single permutation invariant operation
like DeepSets, despite, in theory, being universal function approximators, are
nonetheless outperformed by a more complex architecture based on multi-headed
attention. The latter better accounts for complex physical interactions in a
challenging toy experiment. Further, we find that modelling interactions leads
to consistent performance gains in tracking as well as future trajectory
prediction on three real-world datasets (MOTChallenge, UA-DETRAC, and Stanford
Drone dataset), particularly in the presence of ego-motion, occlusions, crowded
scenes, and faulty sensor inputs
Octree guided CNN with Spherical Kernels for 3D Point Clouds
We propose an octree guided neural network architecture and spherical
convolutional kernel for machine learning from arbitrary 3D point clouds. The
network architecture capitalizes on the sparse nature of irregular point
clouds, and hierarchically coarsens the data representation with space
partitioning. At the same time, the proposed spherical kernels systematically
quantize point neighborhoods to identify local geometric structures in the
data, while maintaining the properties of translation-invariance and asymmetry.
We specify spherical kernels with the help of network neurons that in turn are
associated with spatial locations. We exploit this association to avert dynamic
kernel generation during network training that enables efficient learning with
high resolution point clouds. The effectiveness of the proposed technique is
established on the benchmark tasks of 3D object classification and
segmentation, achieving new state-of-the-art on ShapeNet and RueMonge2014
datasets.Comment: Accepted in IEEE CVPR 2019. arXiv admin note: substantial text
overlap with arXiv:1805.0787
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review
Recently, the advancement of deep learning in discriminative feature learning
from 3D LiDAR data has led to rapid development in the field of autonomous
driving. However, automated processing uneven, unstructured, noisy, and massive
3D point clouds is a challenging and tedious task. In this paper, we provide a
systematic review of existing compelling deep learning architectures applied in
LiDAR point clouds, detailing for specific tasks in autonomous driving such as
segmentation, detection, and classification. Although several published
research papers focus on specific topics in computer vision for autonomous
vehicles, to date, no general survey on deep learning applied in LiDAR point
clouds for autonomous vehicles exists. Thus, the goal of this paper is to
narrow the gap in this topic. More than 140 key contributions in the recent
five years are summarized in this survey, including the milestone 3D deep
architectures, the remarkable deep learning applications in 3D semantic
segmentation, object detection, and classification; specific datasets,
evaluation metrics, and the state of the art performance. Finally, we conclude
the remaining challenges and future researches.Comment: 21 pages, submitted to IEEE Transactions on Neural Networks and
Learning System
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