37 research outputs found
Natural Image Matting via Guided Contextual Attention
Over the last few years, deep learning based approaches have achieved
outstanding improvements in natural image matting. Many of these methods can
generate visually plausible alpha estimations, but typically yield blurry
structures or textures in the semitransparent area. This is due to the local
ambiguity of transparent objects. One possible solution is to leverage the
far-surrounding information to estimate the local opacity. Traditional
affinity-based methods often suffer from the high computational complexity,
which are not suitable for high resolution alpha estimation. Inspired by
affinity-based method and the successes of contextual attention in inpainting,
we develop a novel end-to-end approach for natural image matting with a guided
contextual attention module, which is specifically designed for image matting.
Guided contextual attention module directly propagates high-level opacity
information globally based on the learned low-level affinity. The proposed
method can mimic information flow of affinity-based methods and utilize rich
features learned by deep neural networks simultaneously. Experiment results on
Composition-1k testing set and alphamatting.com benchmark dataset demonstrate
that our method outperforms state-of-the-art approaches in natural image
matting. Code and models are available at
https://github.com/Yaoyi-Li/GCA-Matting.Comment: AAAI-2
Topological edge and corner states in Bi fractals on InSb
Topological materials hosting metallic edges characterized by integer
quantized conductivity in an insulating bulk have revolutionized our
understanding of transport in matter. The topological protection of these edge
states is based on symmetries and dimensionality. However, only
integer-dimensional models have been classified, and the interplay of topology
and fractals, which may have a non-integer dimension, remained largely
unexplored. Quantum fractals have recently been engineered in metamaterials,
but up to present no topological states were unveiled in fractals realized in
real materials. Here, we show theoretically and experimentally that topological
edge and corner modes arise in fractals formed upon depositing thin layers of
bismuth on an indium antimonide substrate. Scanning tunneling microscopy
reveals the appearance of (nearly) zero-energy modes at the corners of
Sierpi\'nski triangles, as well as the formation of outer and inner edge modes
at higher energies. Unexpectedly, a robust and sharp depleted mode appears at
the outer and inner edges of the samples at negative bias voltages. The
experimental findings are corroborated by theoretical calculations in the
framework of a continuum muffin-tin and a lattice tight-binding model. The
stability of the topological features to the introduction of a Rashba
spin-orbit coupling and disorder is discussed. This work opens the perspective
to novel electronics in real materials at non-integer dimensions with robust
and protected topological states.Comment: Main manuscript 14 pages, supplementary material 34 page
Realization of A Knowledge-based Intelligent System for Power Dispatching Plan Management
With the expanding of power grid scale in Chinese metropolis, the task intensity of power dispatchers increases rapidly in regulation of the power system operation structure and states to deal with everyday scheduled maintenance. In this paper, we propose a knowledge-based intelligent system developed to deal with daily management of the power dispatching plans. The system will analyse all the operation state changing tasks arranged for the next day and group the plans according to their association. It will automatically check the security of each power dispatching plan and generate the corresponding dispatching-order tickets. The proposed system builds up power grid ontology knowledge and first-order logic rules and integrates techniques of knowledge reasoning, natural language understanding and network topology analysis. Application shows that it can effectively realize the day-ahead power dispatching plan management (PDPM) instead of the human dispatchers