Computer-aided design(CAD)software continues to be a crucial tool in digital twin application and manufacturing,facilitating the design of various products.We present a novel CAD generation method,an agent that constr...Computer-aided design(CAD)software continues to be a crucial tool in digital twin application and manufacturing,facilitating the design of various products.We present a novel CAD generation method,an agent that constructs the CAD sequences containing the sketch-and-extrude modelling operations efficiently and with high quality.Starting from the sketch and extrusion operation sequences,we utilise the transformer encoder to encode them into different disentangled codebooks to represent their distribution properties while considering their correlations.Then,a combination of auto-regressive and non-autoregressive samplers is trained to sample the code for CAD sequence con-struction.Extensive experiments demonstrate that our model generates diverse and high-quality CAD models.We also show some cases of real digital twin applications and indicate that our generated model can be used as the data source for the digital twin platform,exhibiting designers'potential.展开更多
Intelligent video coding(IVC),which dates back to the late 1980s with the concept of encoding videos with knowledge and semantics,includes visual content compact representation models and methods enabling structural,d...Intelligent video coding(IVC),which dates back to the late 1980s with the concept of encoding videos with knowledge and semantics,includes visual content compact representation models and methods enabling structural,detailed descriptions of visual information at different granularity levels(i.e.,block,mesh,region,and object)and in different areas.It aims to support and facilitate a wide range of applications,such as visual media coding,content broadcasting,and ubiquitous multimedia computing.We present a high-level overview of the IVC technology from model-based coding(MBC)to learning-based coding(LBC).MBC mainly adopts a manually designed coding scheme to explicitly decompose videos to be coded into blocks or semantic components.Thanks to emerging deep learning technologies such as neural networks and generative models,LBC has become a rising topic in the coding area.In this paper,wefirst review the classical MBC approaches,followed by the LBC approaches for image and video data.We also discuss and overview our recent attempts at neural coding approaches,which are inspiring for both academic research and industrial implementation.Some critical yet less studied issues are discussed at the end of this paper.展开更多
Binary multi-view clustering has attracted intense attention from researchers due to its efficiency in handling large-scale datasets.However,previous clustering approaches suffer from at least two limitations.First,th...Binary multi-view clustering has attracted intense attention from researchers due to its efficiency in handling large-scale datasets.However,previous clustering approaches suffer from at least two limitations.First,they ignore correlations among the features of original data.As a result,the geometric consistency of data is not preserved in the to-be-learnt binary representation space.Second,redundant and noisy features mixed in original data inevitably limit the ultimate clustering performance.In light of this,we propose a novel discriminative binary multi-view clustering(DBMVC)method to address the issues.Specifically,the proposed DBMVC first maps original data onto the Hamming space to obtain corresponding binary codes,which can effectively reduce the computational complexity and storage costs in the following steps.To enable our method to select useful features from original data and get a discriminative representation,the-norm is used to constrain the feature projection matrix.In addition,a graph regularization term is further introduced to preserve the local manifold structure of the learned binary representation.Finally,an alternative iterative optimization algorithm is designed to solve the optimization problems of the objective function.Comprehensive experiments on six large-scale multi-view datasets validate that the proposed DBMVC markedly outperforms other state-of-the-art methods in terms of effectiveness and efficiency.展开更多
基金National Key Research and Development Program of China,Grant/Award Number:2022YFF0904303Beijing Science and Technology Planning Project,Grant/Award Number:Z221100006322003National Natural Science Foundation of China,Grant/Award Number:61932003。
文摘Computer-aided design(CAD)software continues to be a crucial tool in digital twin application and manufacturing,facilitating the design of various products.We present a novel CAD generation method,an agent that constructs the CAD sequences containing the sketch-and-extrude modelling operations efficiently and with high quality.Starting from the sketch and extrusion operation sequences,we utilise the transformer encoder to encode them into different disentangled codebooks to represent their distribution properties while considering their correlations.Then,a combination of auto-regressive and non-autoregressive samplers is trained to sample the code for CAD sequence con-struction.Extensive experiments demonstrate that our model generates diverse and high-quality CAD models.We also show some cases of real digital twin applications and indicate that our generated model can be used as the data source for the digital twin platform,exhibiting designers'potential.
基金supported by the National Natural Science Foundation of China(Grant No.62025101,62088102,62101007 and 61931014)the Young Elite Scientist Sponsorship Program by the Beijing Association of Science and Technology(Grant No.BYSS2022019).
文摘Intelligent video coding(IVC),which dates back to the late 1980s with the concept of encoding videos with knowledge and semantics,includes visual content compact representation models and methods enabling structural,detailed descriptions of visual information at different granularity levels(i.e.,block,mesh,region,and object)and in different areas.It aims to support and facilitate a wide range of applications,such as visual media coding,content broadcasting,and ubiquitous multimedia computing.We present a high-level overview of the IVC technology from model-based coding(MBC)to learning-based coding(LBC).MBC mainly adopts a manually designed coding scheme to explicitly decompose videos to be coded into blocks or semantic components.Thanks to emerging deep learning technologies such as neural networks and generative models,LBC has become a rising topic in the coding area.In this paper,wefirst review the classical MBC approaches,followed by the LBC approaches for image and video data.We also discuss and overview our recent attempts at neural coding approaches,which are inspiring for both academic research and industrial implementation.Some critical yet less studied issues are discussed at the end of this paper.
基金supported by the National Natural Science Foundation of China under Grant Nos.62476258,62076228,and 62325604.
文摘Binary multi-view clustering has attracted intense attention from researchers due to its efficiency in handling large-scale datasets.However,previous clustering approaches suffer from at least two limitations.First,they ignore correlations among the features of original data.As a result,the geometric consistency of data is not preserved in the to-be-learnt binary representation space.Second,redundant and noisy features mixed in original data inevitably limit the ultimate clustering performance.In light of this,we propose a novel discriminative binary multi-view clustering(DBMVC)method to address the issues.Specifically,the proposed DBMVC first maps original data onto the Hamming space to obtain corresponding binary codes,which can effectively reduce the computational complexity and storage costs in the following steps.To enable our method to select useful features from original data and get a discriminative representation,the-norm is used to constrain the feature projection matrix.In addition,a graph regularization term is further introduced to preserve the local manifold structure of the learned binary representation.Finally,an alternative iterative optimization algorithm is designed to solve the optimization problems of the objective function.Comprehensive experiments on six large-scale multi-view datasets validate that the proposed DBMVC markedly outperforms other state-of-the-art methods in terms of effectiveness and efficiency.
