15 research outputs found
FactorMatte: Redefining Video Matting for Re-Composition Tasks
We propose "factor matting", an alternative formulation of the video matting
problem in terms of counterfactual video synthesis that is better suited for
re-composition tasks. The goal of factor matting is to separate the contents of
video into independent components, each visualizing a counterfactual version of
the scene where contents of other components have been removed. We show that
factor matting maps well to a more general Bayesian framing of the matting
problem that accounts for complex conditional interactions between layers.
Based on this observation, we present a method for solving the factor matting
problem that produces useful decompositions even for video with complex
cross-layer interactions like splashes, shadows, and reflections. Our method is
trained per-video and requires neither pre-training on external large datasets,
nor knowledge about the 3D structure of the scene. We conduct extensive
experiments, and show that our method not only can disentangle scenes with
complex interactions, but also outperforms top methods on existing tasks such
as classical video matting and background subtraction. In addition, we
demonstrate the benefits of our approach on a range of downstream tasks. Please
refer to our project webpage for more details: https://factormatte.github.ioComment: Project webpage: https://factormatte.github.i
Neural Lens Modeling
Recent methods for 3D reconstruction and rendering increasingly benefit from
end-to-end optimization of the entire image formation process. However, this
approach is currently limited: effects of the optical hardware stack and in
particular lenses are hard to model in a unified way. This limits the quality
that can be achieved for camera calibration and the fidelity of the results of
3D reconstruction. In this paper, we propose NeuroLens, a neural lens model for
distortion and vignetting that can be used for point projection and ray casting
and can be optimized through both operations. This means that it can
(optionally) be used to perform pre-capture calibration using classical
calibration targets, and can later be used to perform calibration or refinement
during 3D reconstruction, e.g., while optimizing a radiance field. To evaluate
the performance of our proposed model, we create a comprehensive dataset
assembled from the Lensfun database with a multitude of lenses. Using this and
other real-world datasets, we show that the quality of our proposed lens model
outperforms standard packages as well as recent approaches while being much
easier to use and extend. The model generalizes across many lens types and is
trivial to integrate into existing 3D reconstruction and rendering systems.Comment: To be presented at CVPR 2023, Project webpage:
https://neural-lens.github.i
TextureGAN: Controlling Deep Image Synthesis with Texture Patches
In this paper, we investigate deep image synthesis guided by sketch, color,
and texture. Previous image synthesis methods can be controlled by sketch and
color strokes but we are the first to examine texture control. We allow a user
to place a texture patch on a sketch at arbitrary locations and scales to
control the desired output texture. Our generative network learns to synthesize
objects consistent with these texture suggestions. To achieve this, we develop
a local texture loss in addition to adversarial and content loss to train the
generative network. We conduct experiments using sketches generated from real
images and textures sampled from a separate texture database and results show
that our proposed algorithm is able to generate plausible images that are
faithful to user controls. Ablation studies show that our proposed pipeline can
generate more realistic images than adapting existing methods directly.Comment: CVPR 2018 spotligh
Suppression of Fading Noise in Satellite-Mediated Continuous-Variable Quantum Key Distribution via Clusterization
The satellite-mediated continuous-variable quantum key distribution (CV-QKD) protocol, which relies on off-the-shelf telecommunication components, has the potential for a global quantum communication network with all-day operation. However, the transmittance fluctuation of satellite-mediated links leads to the arriving quantum state showing non-Gaussian property, introducing extra fading noise in security analysis and limiting the secret key rate of the protocol. Here, we consider the clusterization method for data post-processing to suppress the fading noise in both downlink and uplink scenarios, where the measurement data are divided into several clusters, and we perform security analysis separately. In particular, we set the optimal upper and lower bounds of each cluster in terms of the probability distribution of transmittance (PDT), while finding an optimal cluster number for the trade-off between fading noise and the composable finite-size effect. Numerical analysis shows that the proposed method can improve the composable finite-size rate when the fading noise is large enough, even with only two clusters. Moreover, a high-speed CV-QKD system with a higher frequency of signal preparation and detection can extend the proposed method to work in the case of lower fading noise
The Impact of Financial Derivatives on the Enterprise Value of Chinese Listed Companies: Moderating Effects of Managerial Characteristics
Corporate managers are the central figures of corporate activity who can control the strategic direction of companies. The company’s use of financial derivatives can avoid risks and has an important impact on the value of the company. This study examines A-share listed firms in Shanghai over the period 2011–2020, uses an OLS panel and a moderating effects model, and investigates the impact of financial derivatives on firm value from the perspective of managers’ characteristics. We find that financial derivatives can significantly increase the enterprise value of Chinese listed companies, while exchange rate derivatives have a stronger impact on enterprise value. We also find that the higher the proportion of managers who hold shares and have a financial background, the better the effect of firms using financial derivatives. These research results are of great significance to the application of financial derivatives and provide companies with risk management decisions after COVID-19
Can China’s Digital Inclusive Finance Alleviate Rural Poverty? An Empirical Analysis from the Perspective of Regional Economic Development and an Income Gap
Digital inclusive finance (DIF) plays an active role in preventing poverty-stricken groups from returning to poverty and reducing poverty. This paper empirically tests the impact of DIF on rural poverty alleviation using panel data from 30 Chinese provinces from 2011 to 2020 as a sample. It employs multiple linear regression, mediation effect models, and threshold effect models. The results show that: (1) DIF and its three sub-indicators (coverage breadth, depth of use, and digitalization degree) have significant poverty reduction effects, and the findings hold even when endogeneity is taken into account; (2) a study of regional heterogeneity found that DIF and its sub-indices, coverage and depth of use in the eastern region, have the greatest effect on the poverty alleviation of rural residents, and the effects in the central and western regions have the least effect; (3) the mediation effect test found that DIF could indirectly promote poverty alleviation in rural areas by promoting regional economic growth and narrowing the urban-rural income gap. The Sobel test shows that the mediating effect of regional economic growth is greater than the mediating effect of the urban-rural income gap; (4) it is found through the threshold effect test that regional economic growth has a double threshold effect on rural poverty alleviation, and as the threshold value continues to increase, the poverty reduction effect increases in turn. Therefore, this paper puts forward policy suggestions for the aspects of accelerating the development of DIF in rural areas, implementing regionally differentiated poverty reduction strategies according to local conditions, promoting regional economic growth, and narrowing the urban-rural income gap