112 research outputs found
Constructing a Non-Negative Low Rank and Sparse Graph with Data-Adaptive Features
This paper aims at constructing a good graph for discovering intrinsic data
structures in a semi-supervised learning setting. Firstly, we propose to build
a non-negative low-rank and sparse (referred to as NNLRS) graph for the given
data representation. Specifically, the weights of edges in the graph are
obtained by seeking a nonnegative low-rank and sparse matrix that represents
each data sample as a linear combination of others. The so-obtained NNLRS-graph
can capture both the global mixture of subspaces structure (by the low
rankness) and the locally linear structure (by the sparseness) of the data,
hence is both generative and discriminative. Secondly, as good features are
extremely important for constructing a good graph, we propose to learn the data
embedding matrix and construct the graph jointly within one framework, which is
termed as NNLRS with embedded features (referred to as NNLRS-EF). Extensive
experiments on three publicly available datasets demonstrate that the proposed
method outperforms the state-of-the-art graph construction method by a large
margin for both semi-supervised classification and discriminative analysis,
which verifies the effectiveness of our proposed method
A Coupled Model for Solution Flow and Bioleaching Reaction Based on the Evolution of Heap Pore Structure
Based on the basic seepage law, equations have been derived to descript the solution flow within the copper ore heap which is treated as anisotropy porous media. The relationship between heap permeability and pore ratio has been revealed. Given the consideration of cover pressure and particle dissolution, pore evolution model has been set up. The pore evolution mechanism, due to the process of dissolution, precipitation, blockage, collapse, and caking, has been investigated. The comprehensive model for pore evolution and solution flow under the effect of solute transport and leaching reaction has been established. A trapezoidal heap was calculated, and the estimated results show that permeability decreases with the decreasing of pore ratio. Therefore, the permeability of the heap with small particles is relatively low because of its low pore ratio. Furthermore, permeability and height are found to be the two main factors influencing the solution flow
Target localization based on bistatic T/R pair selection in GNSS-based multistatic radar system
To cope with the increasingly complex electromagnetic environment, multistatic radar systems, especially the passive multistatic radar, are becoming a trend of future radar development due to their advantages in anti-electronic jam, anti-destruction properties, and no electromagnetic pollution. However, one problem with this multi-source network is that it brings a huge amount of information and leads to considerable computational load. Aiming at the problem, this paper introduces the idea of selecting external illuminators in the multistatic passive radar system. Its essence is to optimize the configuration of multistatic T/R pairs. Based on this, this paper respectively proposes two multi-source optimization algorithms from the perspective of resolution unit and resolution capability, the Covariance Matrix Fusion Method and Convex Hull Optimization Method, and then uses a Global Navigation Satellite System (GNSS) as an external illuminator to verify the algorithms. The experimental results show that the two optimization methods significantly improve the accuracy of multistatic positioning, and obtain a more reasonable use of system resources. To evaluate the algorithm performance under large number of transmitting/receiving stations, further simulation was conducted, in which a combination of the two algorithms were applied and the combined algorithm has shown its effectiveness in minimize the computational load and retain the target localization precision at the same time
Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis
We tackle the human motion imitation, appearance transfer, and novel view
synthesis within a unified framework, which means that the model once being
trained can be used to handle all these tasks. The existing task-specific
methods mainly use 2D keypoints (pose) to estimate the human body structure.
However, they only expresses the position information with no abilities to
characterize the personalized shape of the individual person and model the
limbs rotations. In this paper, we propose to use a 3D body mesh recovery
module to disentangle the pose and shape, which can not only model the joint
location and rotation but also characterize the personalized body shape. To
preserve the source information, such as texture, style, color, and face
identity, we propose a Liquid Warping GAN with Liquid Warping Block (LWB) that
propagates the source information in both image and feature spaces, and
synthesizes an image with respect to the reference. Specifically, the source
features are extracted by a denoising convolutional auto-encoder for
characterizing the source identity well. Furthermore, our proposed method is
able to support a more flexible warping from multiple sources. In addition, we
build a new dataset, namely Impersonator (iPER) dataset, for the evaluation of
human motion imitation, appearance transfer, and novel view synthesis.
Extensive experiments demonstrate the effectiveness of our method in several
aspects, such as robustness in occlusion case and preserving face identity,
shape consistency and clothes details. All codes and datasets are available on
https://svip-lab.github.io/project/impersonator.htmlComment: accepted by ICCV201
- …