361 research outputs found
CAP-VSTNet: Content Affinity Preserved Versatile Style Transfer
Content affinity loss including feature and pixel affinity is a main problem
which leads to artifacts in photorealistic and video style transfer. This paper
proposes a new framework named CAP-VSTNet, which consists of a new reversible
residual network and an unbiased linear transform module, for versatile style
transfer. This reversible residual network can not only preserve content
affinity but not introduce redundant information as traditional reversible
networks, and hence facilitate better stylization. Empowered by Matting
Laplacian training loss which can address the pixel affinity loss problem led
by the linear transform, the proposed framework is applicable and effective on
versatile style transfer. Extensive experiments show that CAP-VSTNet can
produce better qualitative and quantitative results in comparison with the
state-of-the-art methods.Comment: CVPR 202
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On-line part deformation prediction based on deep learning
Deformation prediction is the basis of deformation control in manufacturing process planning. This paper presents an on-line part deformation prediction method using a deep learning model during numerical control machining process, which is different from traditional methods based on finite element simulation of stress release prior to the actual machining process. A fourth-order tensor model is proposed to represent the continuous part geometric information, process information, and monitoring information, which is used as the input to the deep learning model. A deep learning framework with a Conventional Neural Network and a Recurrent Neural Network has been constructed and trained by monitored deformation data and process information associated with interim part geometric information. The proposed method can be generalised for different parts with certain similarities and has the potential to provide a reference for an adaptive machining control strategy for reducing part deformation. The proposed method was validated by actual machining experiments, and the results show that the prediction accuracy has been improved compared with existing methods. Furthermore, this paper shifts the difficult problem of residual stress measurement and off-line deformation prediction to the solution of on-line deformation prediction based on deformation monitoring data
Attention-based Multi-modal Fusion Network for Semantic Scene Completion
This paper presents an end-to-end 3D convolutional network named
attention-based multi-modal fusion network (AMFNet) for the semantic scene
completion (SSC) task of inferring the occupancy and semantic labels of a
volumetric 3D scene from single-view RGB-D images. Compared with previous
methods which use only the semantic features extracted from RGB-D images, the
proposed AMFNet learns to perform effective 3D scene completion and semantic
segmentation simultaneously via leveraging the experience of inferring 2D
semantic segmentation from RGB-D images as well as the reliable depth cues in
spatial dimension. It is achieved by employing a multi-modal fusion
architecture boosted from 2D semantic segmentation and a 3D semantic completion
network empowered by residual attention blocks. We validate our method on both
the synthetic SUNCG-RGBD dataset and the real NYUv2 dataset and the results
show that our method respectively achieves the gains of 2.5% and 2.6% on the
synthetic SUNCG-RGBD dataset and the real NYUv2 dataset against the
state-of-the-art method.Comment: Accepted by AAAI 202
A mutation degree model for the identification of transcriptional regulatory elements
<p>Abstract</p> <p>Background</p> <p>Current approaches for identifying transcriptional regulatory elements are mainly via the combination of two properties, the evolutionary conservation and the overrepresentation of functional elements in the promoters of co-regulated genes. Despite the development of many motif detection algorithms, the discovery of conserved motifs in a wide range of phylogenetically related promoters is still a challenge, especially for the short motifs embedded in distantly related gene promoters or very closely related promoters, or in the situation that there are not enough orthologous genes available.</p> <p>Results</p> <p>A mutation degree model is proposed and a new word counting method is developed for the identification of transcriptional regulatory elements from a set of co-expressed genes. The new method comprises two parts: 1) identifying overrepresented oligo-nucleotides in promoters of co-expressed genes, 2) estimating the conservation of the oligo-nucleotides in promoters of phylogenetically related genes by the mutation degree model. Compared with the performance of other algorithms, our method shows the advantages of low false positive rate and higher specificity, especially the robustness to noisy data. Applying the method to co-expressed gene sets from Arabidopsis, most of known <it>cis</it>-elements were successfully detected. The tool and example are available at <url>http://mcube.nju.edu.cn/jwang/lab/soft/ocw/OCW.html</url>.</p> <p>Conclusions</p> <p>The mutation degree model proposed in this paper is adapted to phylogenetic data of different qualities, and to a wide range of evolutionary distances. The new word-counting method based on this model has the advantage of better performance in detecting short sequence of <it>cis</it>-elements from co-expressed genes of eukaryotes and is robust to less complete phylogenetic data.</p
A General Implicit Framework for Fast NeRF Composition and Rendering
A variety of Neural Radiance Fields (NeRF) methods have recently achieved
remarkable success in high render speed. However, current accelerating methods
are specialized and incompatible with various implicit methods, preventing
real-time composition over various types of NeRF works. Because NeRF relies on
sampling along rays, it is possible to provide general guidance for
acceleration. To that end, we propose a general implicit pipeline for composing
NeRF objects quickly. Our method enables the casting of dynamic shadows within
or between objects using analytical light sources while allowing multiple NeRF
objects to be seamlessly placed and rendered together with any arbitrary rigid
transformations. Mainly, our work introduces a new surface representation known
as Neural Depth Fields (NeDF) that quickly determines the spatial relationship
between objects by allowing direct intersection computation between rays and
implicit surfaces. It leverages an intersection neural network to query NeRF
for acceleration instead of depending on an explicit spatial structure.Our
proposed method is the first to enable both the progressive and interactive
composition of NeRF objects. Additionally, it also serves as a previewing
plugin for a range of existing NeRF works.Comment: 7 pages for main conten
Special issue on digital enterprise technologies (editorial)
The special issue includes carefully selected papers presented at the 9th International Conference on Digital Enterprise Technology (DET2016) which was held on 29-31 March, 2016 in Nanjing, China. Authors were invited to re-write, extend and significantly improve their papers presented at DET2016. The main aim of the conference is to provide an international forum for the exchange of leading edge scientific knowledge and industrial experiences, regarding the development, integration and applications of the various aspects of Digital Enterprise Technologies, in the global manufafturing of the knowledge economy era
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