200 research outputs found
Mild Solution of Semilinear SPDEs with Young Drifts
In this paper, we study a semilinear SPDE with a linear Young drift
, where is the generator of an analytical semigroup,
is an -H\"older continuous path with and is a Brownian motion. After establishing through two
different approaches the Young convolution integrals for stochastic integrands,
we introduce the corresponding definition of mild solutions and continuous mild
solutions, and give via a fixed-point argument the existence and uniqueness of
the (continuous) mild solution under suitable conditions.Comment: 17 page
Exploring Disentangled Content Information for Face Forgery Detection
Convolutional neural network based face forgery detection methods have
achieved remarkable results during training, but struggled to maintain
comparable performance during testing. We observe that the detector is prone to
focus more on content information than artifact traces, suggesting that the
detector is sensitive to the intrinsic bias of the dataset, which leads to
severe overfitting. Motivated by this key observation, we design an easily
embeddable disentanglement framework for content information removal, and
further propose a Content Consistency Constraint (C2C) and a Global
Representation Contrastive Constraint (GRCC) to enhance the independence of
disentangled features. Furthermore, we cleverly construct two unbalanced
datasets to investigate the impact of the content bias. Extensive
visualizations and experiments demonstrate that our framework can not only
ignore the interference of content information, but also guide the detector to
mine suspicious artifact traces and achieve competitive performance
DH-AUG: DH Forward Kinematics Model Driven Augmentation for 3D Human Pose Estimation
Due to the lack of diversity of datasets, the generalization ability of the
pose estimator is poor. To solve this problem, we propose a pose augmentation
solution via DH forward kinematics model, which we call DH-AUG. We observe that
the previous work is all based on single-frame pose augmentation, if it is
directly applied to video pose estimator, there will be several previously
ignored problems: (i) angle ambiguity in bone rotation (multiple solutions);
(ii) the generated skeleton video lacks movement continuity. To solve these
problems, we propose a special generator based on DH forward kinematics model,
which is called DH-generator. Extensive experiments demonstrate that DH-AUG can
greatly increase the generalization ability of the video pose estimator. In
addition, when applied to a single-frame 3D pose estimator, our method
outperforms the previous best pose augmentation method. The source code has
been released at
https://github.com/hlz0606/DH-AUG-DH-Forward-Kinematics-Model-Driven-Augmentation-for-3D-Human-Pose-Estimation
LSTM Pose Machines
We observed that recent state-of-the-art results on single image human pose
estimation were achieved by multi-stage Convolution Neural Networks (CNN).
Notwithstanding the superior performance on static images, the application of
these models on videos is not only computationally intensive, it also suffers
from performance degeneration and flicking. Such suboptimal results are mainly
attributed to the inability of imposing sequential geometric consistency,
handling severe image quality degradation (e.g. motion blur and occlusion) as
well as the inability of capturing the temporal correlation among video frames.
In this paper, we proposed a novel recurrent network to tackle these problems.
We showed that if we were to impose the weight sharing scheme to the
multi-stage CNN, it could be re-written as a Recurrent Neural Network (RNN).
This property decouples the relationship among multiple network stages and
results in significantly faster speed in invoking the network for videos. It
also enables the adoption of Long Short-Term Memory (LSTM) units between video
frames. We found such memory augmented RNN is very effective in imposing
geometric consistency among frames. It also well handles input quality
degradation in videos while successfully stabilizes the sequential outputs. The
experiments showed that our approach significantly outperformed current
state-of-the-art methods on two large-scale video pose estimation benchmarks.
We also explored the memory cells inside the LSTM and provided insights on why
such mechanism would benefit the prediction for video-based pose estimations.Comment: Poster in IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), 201
Inertia of partial transpose of positive semidefinite matrices
We show that the partial transpose of positive semidefinite
matrices do not have inertia (4,1,4) and (3,2,4). It solves an open problem in
"LINEAR AND MULTILINEAR ALGEBRA, Changchun Feng et al, 2022". We apply our
results to construct some inertia, as well as present the list of all possible
inertia of partial transpose of positive semidefinite matrices.Comment: 20 pages, comments are welcom
Electric Field-Induced Magnetization Reversal of Multiferroic Nanomagnet
Using the inverse piezoelectric effect and inverse magnetostrictive effect in a multiferroic heterojunction, an electric field is able to control the magnetization switching of a uniaxial nanomagnet. Compared with traditional spintronic devices based on magnetic field, multiferroic nanomagnet devices have the advantages of ultra-low consumption and high radiation resistance, showing great application potential in modern high-integrated circuits and military electronic systems. However, the difficulties of electric field control of complete magnetization reversal of the nanomagnet and nanomagnet arrays in a nanomagnetic logic gate still restrict the developments of multiferroic nanomagnet device. In this chapter, the uniaxial nanomagnets in multiferroic heterojunctions are mainly discussed. The two core problems of the electric field control of nanomagnets and nanomagnetic logic gate are well solved
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