229 research outputs found
From Indoor To Outdoor: Unsupervised Domain Adaptive Gait Recognition
Gait recognition is an important AI task, which has been progressed rapidly
with the development of deep learning. However, existing learning based gait
recognition methods mainly focus on the single domain, especially the
constrained laboratory environment. In this paper, we study a new problem of
unsupervised domain adaptive gait recognition (UDA-GR), that learns a gait
identifier with supervised labels from the indoor scenes (source domain), and
is applied to the outdoor wild scenes (target domain). For this purpose, we
develop an uncertainty estimation and regularization based UDA-GR method.
Specifically, we investigate the characteristic of gaits in the indoor and
outdoor scenes, for estimating the gait sample uncertainty, which is used in
the unsupervised fine-tuning on the target domain to alleviate the noises of
the pseudo labels. We also establish a new benchmark for the proposed problem,
experimental results on which show the effectiveness of the proposed method. We
will release the benchmark and source code in this work to the public
Dynamic Semiparametric Factor Model with a Common Break
For change-point analysis of high dimensional time series, we consider a semiparametric model with dynamic structural break factors. The observations are described by a few low dimensional factors with time-invariate loading functions of covariates. The unknown structural break in time models the regime switching e ects introduced by exogenous shocks. In particular, the factors are assumed to be nonstationary and follow a Vector Autoregression (VAR) process with a structural break. In addition, to account for the known spatial discrepancies, we introduce discrete loading functions. We study the theoretical properties of the estimates of the loading functions and the factors. Moreover, we provide both the consistency and the asymptotic convergence results for making inference on the common breakpoint in time. The estimation precision is evaluated via a simulation study. Finally we present two empirical illustrations on modeling the dynamics of the minimum wage policy in China and analyzing a limit order book dataset
Combining the Silhouette and Skeleton Data for Gait Recognition
Gait recognition, a promising long-distance biometric technology, has aroused
intense interest in computer vision. Existing works on gait recognition can be
divided into appearance-based methods and model-based methods, which extract
features from silhouettes and skeleton data, respectively. However, since
appearance-based methods are greatly affected by clothing changing and carrying
condition, and model-based methods are limited by the accuracy of pose
estimation approaches, gait recognition remains challenging in practical
applications. In order to integrate the advantages of such two approaches, a
two-branch neural network (NN) is proposed in this paper. Our method contains
two branches, namely a CNN-based branch taking silhouettes as input and a
GCN-based branch taking skeletons as input. In addition, two new modules are
proposed in the GCN-based branch for better gait representation. First, we
present a simple yet effective fully connected graph convolution operator to
integrate the multi-scale graph convolutions and alleviate the dependence on
natural human joint connections. Second, we deploy a multi-dimension attention
module named STC-Att to learn spatial, temporal and channel-wise attention
simultaneously. We evaluated the proposed two-branch neural network on the
CASIA-B dataset. The experimental results show that our method achieves
state-of-the-art performance in various conditions.Comment: The paper is under consideration at Computer Vision and Image
Understandin
Inference for Change Points in High-Dimensional Time Series via a Two-Way MOSUM
We propose an inference method for detecting multiple change points in
high-dimensional time series, targeting dense or spatially clustered signals.
Our method aggregates moving sum (MOSUM) statistics cross-sectionally by an
-norm and maximizes them over time. We further introduce a novel
Two-Way MOSUM, which utilizes spatial-temporal moving regions to search for
breaks, with the added advantage of enhancing testing power when breaks occur
in only a few groups. The limiting distribution of an -aggregated
statistic is established for testing break existence by extending a
high-dimensional Gaussian approximation theorem to spatial-temporal
non-stationary processes. Simulation studies exhibit promising performance of
our test in detecting non-sparse weak signals. Two applications, analyzing
equity returns and COVID-19 cases in the United States, showcase the real-world
relevance of our proposed algorithms.Comment: 111 pages, 10 figure
Controlling single rare earth ion emission in an electro-optical nanocavity
Rare earth emitters enable critical quantum resources including spin qubits,
single photon sources, and quantum memories. Yet, probing of single ions
remains challenging due to low emission rate of their intra-4f optical
transitions. One feasible approach is through Purcell enhanced emission in
optical cavities. The ability to modulate cavity-ion coupling in real time will
further elevate the capacity of such systems. Here, we demonstrate direct
control of single ion emission by embedding erbium dopants in an
electro-optically active photonic crystal cavity patterned from thin-film
lithium niobate. Purcell factor over 170 enables single ion detection, which is
verified by second-order autocorrelation measurement. Dynamic control of
emission rate is realized by leveraging electro-optic tuning of resonance
frequency. Using this feature, storage and retrieval of single ion excitation
is further demonstrated, without perturbing the emission characteristics. These
results promise new opportunities for controllable single photon sources and
efficient spin-photon interfaces
A Benchmark of Video-Based Clothes-Changing Person Re-Identification
Person re-identification (Re-ID) is a classical computer vision task and has
achieved great progress so far. Recently, long-term Re-ID with clothes-changing
has attracted increasing attention. However, existing methods mainly focus on
image-based setting, where richer temporal information is overlooked. In this
paper, we focus on the relatively new yet practical problem of clothes-changing
video-based person re-identification (CCVReID), which is less studied. We
systematically study this problem by simultaneously considering the challenge
of the clothes inconsistency issue and the temporal information contained in
the video sequence for the person Re-ID problem. Based on this, we develop a
two-branch confidence-aware re-ranking framework for handling the CCVReID
problem. The proposed framework integrates two branches that consider both the
classical appearance features and cloth-free gait features through a
confidence-guided re-ranking strategy. This method provides the baseline method
for further studies. Also, we build two new benchmark datasets for CCVReID
problem, including a large-scale synthetic video dataset and a real-world one,
both containing human sequences with various clothing changes. We will release
the benchmark and code in this work to the public
Dynamic Semiparametric Factor Model with Structural Breaks
For the change-point analysis of a high-dimensional time series, we consider a semiparametric model with dynamic structural break factors. With our model, the observations are described by a few low-dimensional factors with time-invariant loading functions of the covariates. Regarding the structural break, the factors are assumed to be nonstationary and follow a vector autoregression (VAR) process with a change in the parameter values. In addition, to account for the known spatial discrepancies, we introduce discrete loading functions. We study the theoretical properties of the estimates of the loading functions and the factors. Moreover, we provide both the consistency and the asymptotic normality for making an inference on the estimated breakpoint. {Importantly, our results hold for both large and small breaks in the factor dependency structure.} The estimation precision is further illustrated via a simulation study. Finally, we present two empirical applications in modeling the dynamics of the minimum wage policy in China and analyzing a limit order book dataset
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