15,254 research outputs found
Im2Flow: Motion Hallucination from Static Images for Action Recognition
Existing methods to recognize actions in static images take the images at
their face value, learning the appearances---objects, scenes, and body
poses---that distinguish each action class. However, such models are deprived
of the rich dynamic structure and motions that also define human activity. We
propose an approach that hallucinates the unobserved future motion implied by a
single snapshot to help static-image action recognition. The key idea is to
learn a prior over short-term dynamics from thousands of unlabeled videos,
infer the anticipated optical flow on novel static images, and then train
discriminative models that exploit both streams of information. Our main
contributions are twofold. First, we devise an encoder-decoder convolutional
neural network and a novel optical flow encoding that can translate a static
image into an accurate flow map. Second, we show the power of hallucinated flow
for recognition, successfully transferring the learned motion into a standard
two-stream network for activity recognition. On seven datasets, we demonstrate
the power of the approach. It not only achieves state-of-the-art accuracy for
dense optical flow prediction, but also consistently enhances recognition of
actions and dynamic scenes.Comment: Published in CVPR 2018, project page:
http://vision.cs.utexas.edu/projects/im2flow
Empirical properties of inter-cancellation durations in the Chinese stock market
Order cancellation process plays a crucial role in the dynamics of price
formation in order-driven stock markets and is important in the construction
and validation of computational finance models. Based on the order flow data of
18 liquid stocks traded on the Shenzhen Stock Exchange in 2003, we investigate
the empirical statistical properties of inter-cancellation durations in units
of events defined as the waiting times between two consecutive cancellations.
The inter-cancellation durations for both buy and sell orders of all the stocks
favor a -exponential distribution when the maximum likelihood estimation
method is adopted; In contrast, both cancelled buy orders of 6 stocks and
cancelled sell orders of 3 stocks prefer Weibull distribution when the
nonlinear least-square estimation is used. Applying detrended fluctuation
analysis (DFA), centered detrending moving average (CDMA) and multifractal
detrended fluctuation analysis (MF-DFA) methods, we unveil that the
inter-cancellation duration time series process long memory and multifractal
nature for both buy and sell cancellations of all the stocks. Our findings show
that order cancellation processes exhibit long-range correlated bursty
behaviors and are thus not Poissonian.Comment: 14 pages, 7 figures and 5 table
Image Restoration Using Joint Statistical Modeling in Space-Transform Domain
This paper presents a novel strategy for high-fidelity image restoration by
characterizing both local smoothness and nonlocal self-similarity of natural
images in a unified statistical manner. The main contributions are three-folds.
First, from the perspective of image statistics, a joint statistical modeling
(JSM) in an adaptive hybrid space-transform domain is established, which offers
a powerful mechanism of combining local smoothness and nonlocal self-similarity
simultaneously to ensure a more reliable and robust estimation. Second, a new
form of minimization functional for solving image inverse problem is formulated
using JSM under regularization-based framework. Finally, in order to make JSM
tractable and robust, a new Split-Bregman based algorithm is developed to
efficiently solve the above severely underdetermined inverse problem associated
with theoretical proof of convergence. Extensive experiments on image
inpainting, image deblurring and mixed Gaussian plus salt-and-pepper noise
removal applications verify the effectiveness of the proposed algorithm.Comment: 14 pages, 18 figures, 7 Tables, to be published in IEEE Transactions
on Circuits System and Video Technology (TCSVT). High resolution pdf version
and Code can be found at: http://idm.pku.edu.cn/staff/zhangjian/IRJSM
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