15,066 research outputs found
Remark on Remnant and Residue Entropy with GUP
In this article, close to the Planck scale, we discuss on the remnant and
residue entropy from a Rutz-Schwarzschild black hole in the frame of Finsler
geometry. Employing the corrected Hamilton-Jacobi equation, the tunneling
radiation of a scalar particle is presented, and the revised tunneling
temperature and revised entropy are also found. Taking into account generalized
uncertainty principle (GUP), we analyze the remnant stability and residue
entropy based on thermodynamic phase transition. In addition, the effects of
the Finsler perturbation parameter, GUP parameter and angular momentum
parameter on remnant and residual entropy are also discussed.Comment: 18 pages, 5 figures, 2 table
ADFactory: An Effective Framework for Generalizing Optical Flow with Nerf
A significant challenge facing current optical flow methods is the difficulty
in generalizing them well to the real world. This is mainly due to the high
cost of hand-crafted datasets, and existing self-supervised methods are limited
by indirect loss and occlusions, resulting in fuzzy outcomes. To address this
challenge, we introduce a novel optical flow training framework: automatic data
factory (ADF). ADF only requires RGB images as input to effectively train the
optical flow network on the target data domain. Specifically, we use advanced
Nerf technology to reconstruct scenes from photo groups collected by a
monocular camera, and then calculate optical flow labels between camera pose
pairs based on the rendering results. To eliminate erroneous labels caused by
defects in the scene reconstructed by Nerf, we screened the generated labels
from multiple aspects, such as optical flow matching accuracy, radiation field
confidence, and depth consistency. The filtered labels can be directly used for
network supervision. Experimentally, the generalization ability of ADF on KITTI
surpasses existing self-supervised optical flow and monocular scene flow
algorithms. In addition, ADF achieves impressive results in real-world
zero-point generalization evaluations and surpasses most supervised methods.Comment: 8 page
Pixelated Semantic Colorization
While many image colorization algorithms have recently shown the capability
of producing plausible color versions from gray-scale photographs, they still
suffer from limited semantic understanding. To address this shortcoming, we
propose to exploit pixelated object semantics to guide image colorization. The
rationale is that human beings perceive and distinguish colors based on the
semantic categories of objects. Starting from an autoregressive model, we
generate image color distributions, from which diverse colored results are
sampled. We propose two ways to incorporate object semantics into the
colorization model: through a pixelated semantic embedding and a pixelated
semantic generator. Specifically, the proposed convolutional neural network
includes two branches. One branch learns what the object is, while the other
branch learns the object colors. The network jointly optimizes a color
embedding loss, a semantic segmentation loss and a color generation loss, in an
end-to-end fashion. Experiments on PASCAL VOC2012 and COCO-stuff reveal that
our network, when trained with semantic segmentation labels, produces more
realistic and finer results compared to the colorization state-of-the-art
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