4,924 research outputs found
Learning and Refining of Privileged Information-based RNNs for Action Recognition from Depth Sequences
Existing RNN-based approaches for action recognition from depth sequences
require either skeleton joints or hand-crafted depth features as inputs. An
end-to-end manner, mapping from raw depth maps to action classes, is
non-trivial to design due to the fact that: 1) single channel map lacks texture
thus weakens the discriminative power; 2) relatively small set of depth
training data. To address these challenges, we propose to learn an RNN driven
by privileged information (PI) in three-steps: An encoder is pre-trained to
learn a joint embedding of depth appearance and PI (i.e. skeleton joints). The
learned embedding layers are then tuned in the learning step, aiming to
optimize the network by exploiting PI in a form of multi-task loss. However,
exploiting PI as a secondary task provides little help to improve the
performance of a primary task (i.e. classification) due to the gap between
them. Finally, a bridging matrix is defined to connect two tasks by discovering
latent PI in the refining step. Our PI-based classification loss maintains a
consistency between latent PI and predicted distribution. The latent PI and
network are iteratively estimated and updated in an expectation-maximization
procedure. The proposed learning process provides greater discriminative power
to model subtle depth difference, while helping avoid overfitting the scarcer
training data. Our experiments show significant performance gains over
state-of-the-art methods on three public benchmark datasets and our newly
collected Blanket dataset.Comment: conference cvpr 201
Occlusion-aware Hand Pose Estimation Using Hierarchical Mixture Density Network
Learning and predicting the pose parameters of a 3D hand model given an
image, such as locations of hand joints, is challenging due to large viewpoint
changes and articulations, and severe self-occlusions exhibited particularly in
egocentric views. Both feature learning and prediction modeling have been
investigated to tackle the problem. Though effective, most existing
discriminative methods yield a single deterministic estimation of target poses.
Due to their single-value mapping intrinsic, they fail to adequately handle
self-occlusion problems, where occluded joints present multiple modes. In this
paper, we tackle the self-occlusion issue and provide a complete description of
observed poses given an input depth image by a novel method called hierarchical
mixture density networks (HMDN). The proposed method leverages the
state-of-the-art hand pose estimators based on Convolutional Neural Networks to
facilitate feature learning, while it models the multiple modes in a two-level
hierarchy to reconcile single-valued and multi-valued mapping in its output.
The whole framework with a mixture of two differentiable density functions is
naturally end-to-end trainable. In the experiments, HMDN produces interpretable
and diverse candidate samples, and significantly outperforms the
state-of-the-art methods on two benchmarks with occlusions, and performs
comparably on another benchmark free of occlusions
Deep Convolutional Decision Jungle for Image Classification
We propose a novel method called deep convolutional decision jungle (CDJ) and
its learning algorithm for image classification. The CDJ maintains the
structure of standard convolutional neural networks (CNNs), i.e. multiple
layers of multiple response maps fully connected. Each response map-or node-in
both the convolutional and fully-connected layers selectively respond to class
labels s.t. each data sample travels via a specific soft route of those
activated nodes. The proposed method CDJ automatically learns features, whereas
decision forests and jungles require pre-defined feature sets. Compared to
CNNs, the method embeds the benefits of using data-dependent discriminative
functions, which better handles multi-modal/heterogeneous data; further,the
method offers more diverse sparse network responses, which in turn can be used
for cost-effective learning/classification. The network is learnt by combining
conventional softmax and proposed entropy losses in each layer. The entropy
loss,as used in decision tree growing, measures the purity of data activation
according to the class label distribution. The back-propagation rule for the
proposed loss function is derived from stochastic gradient descent (SGD)
optimization of CNNs. We show that our proposed method outperforms
state-of-the-art methods on three public image classification benchmarks and
one face verification dataset. We also demonstrate the use of auxiliary data
labels, when available, which helps our method to learn more discriminative
routing and representations and leads to improved classification
Condensate of a charged boson fluid at non-integer dimensions
Condensate of a charged boson fluid at non-integer dimensions between 2 and 3
is studied. Interaction between particles is assumed to be Coulombic, and
Bogoliubov approximation is applied for a weak coupling regime. The condensate
and the superfluid fraction at finite temperatures and at non-integer
dimensions are calculated. The theoretical results are compared with the
superfluid densities of superconducting films, which show a universal splitting
behavior. The qualitative similarity between the charged boson fluid and the
superconducting films gives a strong clue for the origin of the universality.Comment: 10 pages, 8 figure
Transition Forests: Learning Discriminative Temporal Transitions for Action Recognition and Detection
A human action can be seen as transitions between one's body poses over time,
where the transition depicts a temporal relation between two poses. Recognizing
actions thus involves learning a classifier sensitive to these pose transitions
as well as to static poses. In this paper, we introduce a novel method called
transitions forests, an ensemble of decision trees that both learn to
discriminate static poses and transitions between pairs of two independent
frames. During training, node splitting is driven by alternating two criteria:
the standard classification objective that maximizes the discrimination power
in individual frames, and the proposed one in pairwise frame transitions.
Growing the trees tends to group frames that have similar associated
transitions and share same action label incorporating temporal information that
was not available otherwise. Unlike conventional decision trees where the best
split in a node is determined independently of other nodes, the transition
forests try to find the best split of nodes jointly (within a layer) for
incorporating distant node transitions. When inferring the class label of a new
frame, it is passed down the trees and the prediction is made based on previous
frame predictions and the current one in an efficient and online manner. We
apply our method on varied skeleton action recognition and online detection
datasets showing its suitability over several baselines and state-of-the-art
approaches.Comment: to appear in CVPR 201
Decoherence from Internal Dynamics in Macroscopic Matter-Wave Interferometry
Usually, decoherence is generated from the coupling with an outer
environment. However, a macroscopic object generically possesses its own
environment in itself, namely the complicated dynamics of internal degrees of
freedom. We address a question: when and how the internal dynamics decohere
interference of the center of mass motion of a macroscopic object. We will also
show that weak localization of a macroscopic object in disordered potentials
can be destroyed by such decoherence.Comment: 5 pages, 2 figure
Atomic-scale simulations on the sliding of incommensurate surfaces: The breakdown of superlubricity
Molecular dynamics simulations of frictional sliding in an Atomic Force
Microscope (AFM) show a clear dependence of superlubricity between
incommensurate surfaces on tip compliance and applied normal force. While the
kinetic friction vanishes for rigid tips and low normal force, superlubric
behavior breaks down for softer tips and high normal force. The simulations
provide evidence that the Frenkel-Kontorova-Tomlinson (FKT) scaling applies
equally to a more realistic 3-D incommensurate AFM model except in the limit of
very low stiffness and high normal load limit. Unlike the FKT model in which
the breakdown of superlubricity coincides to the emergence of the meta-stable
states, in the 3-D model some meta-stable states appear to reduce frictional
force leading to non-monotonic dependence of force on normal load and tip
compliance. Meta-stable states vary with the slider positions, and the relative
stabilities of these meta-stable states result in varying transition mechanisms
depending on sliding velocity.Comment: 31 pages, 15 figures, 1 tabl
Reformulating hyperdynamics without a transition state theory dividing surface
Reformulating hyperdynamics without using a transition state theory (TST)
dividing surface makes it possible to accelerate conventional molecular
dynamics (MD) simulation using a broader range of bias potentials. A new scheme
to calculate the boost factor is also introduced that makes the hyperdynamics
method more accurate and efficient. Novel bias potentials using the
hyper-distance and the potential energy slope and curvature along the direction
vector from a minimum to a current position can significantly reduce the
computational overhead required. Results simulating an atomic force microscope
(AFM) system validate the new methodology
Learning Deep Convolutional Embeddings for Face Representation Using Joint Sample- and Set-based Supervision
In this work, we investigate several methods and strategies to learn deep
embeddings for face recognition, using joint sample- and set-based
optimization. We explain our framework that expands traditional learning with
set-based supervision together with the strategies used to maintain set
characteristics. We, then, briefly review the related set-based loss functions,
and subsequently propose a novel Max-Margin Loss which maximizes maximum
possible inter-class margin with assistance of Support Vector Machines (SVMs).
It implicitly pushes all the samples towards correct side of the margin with a
vector perpendicular to the hyperplane and a strength exponentially growing
towards to negative side of the hyperplane. We show that the introduced loss
outperform the previous sample-based and set-based ones in terms verification
of faces on two commonly used benchmarks.Comment: 8 pages, 5 figures, 2 tables, workshop pape
Alpha invariants of birationally rigid Fano threefolds
We compute global log canonical thresholds, or equivalently alpha invariants,
of birationally rigid orbifold Fano threefolds embedded in weighted projective
spaces as codimension two or three. As an important application, we prove that
most of them are weakly exceptional, -stable and admit K\"{a}hler--Einstien
metric.Comment: 38 page
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