882 research outputs found
High frequency dynamics of order flow
In this paper, we focus on the high frequency dynamics of limit order flow and market order flow. We compared the fitting performance of different models for the inter-arrival time of the order flow, including exponential distribution, gamma distribution and power law. We then studied the dependence of the placement of these two order flows, which can be captured by the self-excitation effect and mutual-excitation effect of Hawkes process. We also introduced a new model which combines the Hawkes features with the gamma distribution.\ud
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Key words: High frequency dynamics; order flow; market microstructure; maximum likelihood estimation; Hawkes process; Hawkes-Gamma distribution
Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification
Although great progress in supervised person re-identification (Re-ID) has
been made recently, due to the viewpoint variation of a person, Re-ID remains a
massive visual challenge. Most existing viewpoint-based person Re-ID methods
project images from each viewpoint into separated and unrelated sub-feature
spaces. They only model the identity-level distribution inside an individual
viewpoint but ignore the underlying relationship between different viewpoints.
To address this problem, we propose a novel approach, called
\textit{Viewpoint-Aware Loss with Angular Regularization }(\textbf{VA-reID}).
Instead of one subspace for each viewpoint, our method projects the feature
from different viewpoints into a unified hypersphere and effectively models the
feature distribution on both the identity-level and the viewpoint-level. In
addition, rather than modeling different viewpoints as hard labels used for
conventional viewpoint classification, we introduce viewpoint-aware adaptive
label smoothing regularization (VALSR) that assigns the adaptive soft label to
feature representation. VALSR can effectively solve the ambiguity of the
viewpoint cluster label assignment. Extensive experiments on the Market1501 and
DukeMTMC-reID datasets demonstrated that our method outperforms the
state-of-the-art supervised Re-ID methods
GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
Echocardiogram video segmentation plays an important role in cardiac disease
diagnosis. This paper studies the unsupervised domain adaption (UDA) for
echocardiogram video segmentation, where the goal is to generalize the model
trained on the source domain to other unlabelled target domains. Existing UDA
segmentation methods are not suitable for this task because they do not model
local information and the cyclical consistency of heartbeat. In this paper, we
introduce a newly collected CardiacUDA dataset and a novel GraphEcho method for
cardiac structure segmentation. Our GraphEcho comprises two innovative modules,
the Spatial-wise Cross-domain Graph Matching (SCGM) and the Temporal Cycle
Consistency (TCC) module, which utilize prior knowledge of echocardiogram
videos, i.e., consistent cardiac structure across patients and centers and the
heartbeat cyclical consistency, respectively. These two modules can better
align global and local features from source and target domains, improving UDA
segmentation results. Experimental results showed that our GraphEcho
outperforms existing state-of-the-art UDA segmentation methods. Our collected
dataset and code will be publicly released upon acceptance. This work will lay
a new and solid cornerstone for cardiac structure segmentation from
echocardiogram videos. Code and dataset are available at:
https://github.com/xmed-lab/GraphEchoComment: Accepted By ICCV 202
GL-Fusion: Global-Local Fusion Network for Multi-view Echocardiogram Video Segmentation
Cardiac structure segmentation from echocardiogram videos plays a crucial
role in diagnosing heart disease. The combination of multi-view echocardiogram
data is essential to enhance the accuracy and robustness of automated methods.
However, due to the visual disparity of the data, deriving cross-view context
information remains a challenging task, and unsophisticated fusion strategies
can even lower performance. In this study, we propose a novel Gobal-Local
fusion (GL-Fusion) network to jointly utilize multi-view information globally
and locally that improve the accuracy of echocardiogram analysis. Specifically,
a Multi-view Global-based Fusion Module (MGFM) is proposed to extract global
context information and to explore the cyclic relationship of different
heartbeat cycles in an echocardiogram video. Additionally, a Multi-view
Local-based Fusion Module (MLFM) is designed to extract correlations of cardiac
structures from different views. Furthermore, we collect a multi-view
echocardiogram video dataset (MvEVD) to evaluate our method. Our method
achieves an 82.29% average dice score, which demonstrates a 7.83% improvement
over the baseline method, and outperforms other existing state-of-the-art
methods. To our knowledge, this is the first exploration of a multi-view method
for echocardiogram video segmentation. Code available at:
https://github.com/xmed-lab/GL-FusionComment: Accepted By MICCAI 202
Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement
Simulating turbulence is critical for many societally important applications
in aerospace engineering, environmental science, the energy industry, and
biomedicine. Large eddy simulation (LES) has been widely used as an alternative
to direct numerical simulation (DNS) for simulating turbulent flows due to its
reduced computational cost. However, LES is unable to capture all of the scales
of turbulent transport accurately. Reconstructing DNS from low-resolution LES
is critical for many scientific and engineering disciplines, but it poses many
challenges to existing super-resolution methods due to the spatio-temporal
complexity of turbulent flows. In this work, we propose a new physics-guided
neural network for reconstructing the sequential DNS from low-resolution LES
data. The proposed method leverages the partial differential equation that
underlies the flow dynamics in the design of spatio-temporal model
architecture. A degradation-based refinement method is also developed to
enforce physical constraints and further reduce the accumulated reconstruction
errors over long periods. The results on two different types of turbulent flow
data confirm the superiority of the proposed method in reconstructing the
high-resolution DNS data and preserving the physical characteristics of flow
transport.Comment: 19 page
Rethinking Temporal Fusion for Video-based Person Re-identification on Semantic and Time Aspect
Recently, the research interest of person re-identification (ReID) has
gradually turned to video-based methods, which acquire a person representation
by aggregating frame features of an entire video. However, existing video-based
ReID methods do not consider the semantic difference brought by the outputs of
different network stages, which potentially compromises the information
richness of the person features. Furthermore, traditional methods ignore
important relationship among frames, which causes information redundancy in
fusion along the time axis. To address these issues, we propose a novel general
temporal fusion framework to aggregate frame features on both semantic aspect
and time aspect. As for the semantic aspect, a multi-stage fusion network is
explored to fuse richer frame features at multiple semantic levels, which can
effectively reduce the information loss caused by the traditional single-stage
fusion. While, for the time axis, the existing intra-frame attention method is
improved by adding a novel inter-frame attention module, which effectively
reduces the information redundancy in temporal fusion by taking the
relationship among frames into consideration. The experimental results show
that our approach can effectively improve the video-based re-identification
accuracy, achieving the state-of-the-art performance
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