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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023

作者： Wang, Kangkan Zhang, Guofeng Cong, Suxu Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China

This paper addresses the challenge of capturing performance for the clothed humans from sparse-view or monocular videos. Previous methods capture the performance of full humans with a personalized template or recover the garments from a single frame with static human poses. However, it is inconvenient to extract cloth semantics and capture clothing motion with one-piece template, while single frame-based methods may suffer from instable tracking across videos. To address these problems, we propose a novel method for human performance capture by tracking clothing and human body motion separately with a double-layer neural radiance fields (NeRFs). Specifically, we propose a double-layer NeRFs for the body and garments, and track the densely deforming template of the clothing and body by jointly optimizing the deformation fields and the canonical double-layer NeRFs. In the optimization, we introduce a physics-aware cloth simulation network which can help generate physically plausible cloth dynamics and body-cloth interactions. Compared with existing methods, our method is fully differentiable and can capture both the body and clothing motion robustly from dynamic videos. Also, our method represents the clothing with an independent NeRFs, allowing us to model implicit fields of general clothes feasibly. The experimental evaluations validate its effectiveness on real multi-view or monocular videos. ©2023 IEEE.

关键词： Multilayer neural networks

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Pan-sharpening via coupled unitary dictionary learning 8th

Pan-sharpening via coupled unitary dictionary learning

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8th International Conference on Image and Graphics, ICIG 2015

作者： Chen, Shumiao Xiao, Liang Wei, Zhihui Huang, Wei School of Computer Science and Engineering Nanjing University of Science and Technology Xiaolingwei Street 200 NanjingJiangsu210094 China Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Xiaolingwei Street 200 NanjingJiangsu210094 China

ISBN: (纸本)9783319219684

In this paper, we propose a new pan-sharpening method by coupled unitary dictionary learning and clustered sparse representation. First, we randomly sample image patch pairs from the training images exclude the smooth patches, and divide these patch pairs into different groups by K-means clustering. Then, we learn sub-dictionaries offline from corresponding group patch pairs. Particularly, we use the principal component analysis (PCA) technique to learn sub-dictionaries. For a given LR MS patch, we adaptively select one sub-dictionary to reconstruct the HR MS patch online. Experiments show that the proposed method produces images with higher spectral resolution while maintaining the high-quality spatial resolution and gives better visual perception compared with the conventional methods. © Springer International Publishing Switzerland 2015.

关键词： Principal component analysis

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Coarse-to-Fine Attribute Editing for Fashion Images 1st

Coarse-to-Fine Attribute Editing for Fashion Images

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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Wang, Qinghu Qian, Jianjun Zou, Xingxing Yang, Jian Wong, Waikeung PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China Institute of Textiles and Clothing The Hong Kong Polytechnic University Hung Hom Hong Kong Laboratory for Artificial Intelligence in Design Hung Hom Hong Kong

ISBN: (纸本)9783030930455

With the development of Generative Adversarial Networks, attribute editing has been more and more popular in computer vision. The previous works employed the multi-domain image-to-image translation framework to solve attribute editing. However, it is difficult to generate consistent texture with the original images. Meanwhile, the un-target attribute regions are changed by using these methods. To address these problems, this paper presents a coarse-to-fine attribute editing scheme (CFAE) for fashion images. CFAE is composed of coarse stage and refine stage. In the coarse stage, we design a landmark-based attention scheme in conjunction with StarGAN [5] to locate and edit the target attribute regions. Subsequently, DeepFillv2 [16] is employed in the refine stage to make the target attribute regions consistent with the original image. In experiment section, we compare our method with several state-of-the-art methods on OUTFIT Dataset and the results demonstrate the effectiveness of CFAE. © 2021, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Counterfactual discriminative micro-expression recognition

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Visual Intelligence 2024年第1期2卷 1-10页

作者： Li, Yong Liu, Menglin Lao, Lingjie Wang, Yuanzhi Cui, Zhen Department of Computer Science City University of Hong Kong 999077 Hong Kong School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China

Micro-expressions are spontaneous, rapid and subtle facial movements that can hardly be suppressed or fabricated. Micro-expression recognition (MER) is one of the most challenging topics in affective computing. It aims to recognize subtle facial movements which are quite difficult for humans to perceive in a fleeting period. Recently, many deep learning-based MER methods have been developed. However, how to effectively capture subtle temporal variations for robust MER still perplexes us. We propose a counterfactual discriminative micro-expression recognition (CoDER) method to effectively learn the slight temporal variations for video-based MER. To explicitly capture the causality from temporal dynamics hidden in the micro-expression (ME) sequence, we propose ME counterfactual reasoning by comparing the effects of the facts w.r.t. original ME sequences and the counterfactuals w.r.t. counterfactually-revised ME sequences, and then perform causality-aware prediction to encourage the model to learn those latent ME temporal cues. Extensive experiments on four widely-used ME databases demonstrate the effectiveness of CoDER, which results in comparable and superior MER performance compared with that of the state-of-the-art methods. The visualization results show that CoDER successfully perceives the meaningful temporal variations in sequential faces. © The Author(s) 2024.

关键词： Affective computing Causal graph Counterfactual reasoning Micro-expression recognition Temporal variation

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Structure-Sensitive Graph Dictionary Embedding for Graph Classification

arXiv

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arXiv 2023年

作者： Liu, Guangbu Zhang, Tong Wang, Xudong Zhao, Wenting Zhou, Chuanwei Cui, Zhen The Key Lab of Intelligent Perception and Systems for High-Dimensional The School of Computer Science and Engineering Nanjing University of Science and Technology China

Graph structure expression plays a vital role in distinguishing various graphs. In this work, we propose a Structure-Sensitive Graph Dictionary Embedding (SS-GDE) framework to transform input graphs into the embedding space of a graph dictionary for the graph classification task. Instead of a plain use of a base graph dictionary, we propose the variational graph dictionary adaptation (VGDA) to generate a personalized dictionary (named adapted graph dictionary) for catering to each input graph. In particular, for the adaptation, the Bernoulli sampling is introduced to adjust substructures of base graph keys according to each input, which increases the expression capacity of the base dictionary tremendously. To make cross-graph measurement sensitive as well as stable, multi-sensitivity Wasserstein encoding is proposed to produce the embeddings by designing multi-scale attention on optimal transport. To optimize the framework, we introduce mutual information as the objective, which further deduces to variational inference of the adapted graph dictionary. We perform our SS-GDE on multiple datasets of graph classification, and the experimental results demonstrate the effectiveness and superiority over the state-of-the-art methods. © 2023, CC BY-NC-SA.

关键词： Embeddings

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Global Convergence Guarantees of (A)GIST for a Family of Nonconvex Sparse Learning Problems

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IEEE Transactions on Cybernetics 2022年第5期52卷 3276-3288页

作者： Zhang, Hengmin Qian, Feng Shang, Fanhua Du, Wenli Qian, Jianjun Yang, Jian East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and Engineering Shanghai200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xi'an710071 China Peng Cheng Laboratory Shenzhen518066 China Nanjing University of Science and Technology Pca Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China

In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the \ell _{0} -norm usually achieve better performance than the convex case (e.g., \ell _{1} -norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Lojasiewica (KL) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones. © 2013 IEEE.

关键词： Optimization

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A novel scheduling algorithm for file fetch in transparent computing 15th

A novel scheduling algorithm for file fetch in transparent c...

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15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： Guo, Kehua Tang, Yayuan Gu, Jiacheng School of Information Science and Engineering Central South University Changsha410083 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China Department of Computer Science and Enginnering Texas University College StationTX77840 United States

ISBN: (纸本)9783319271392

A novel scheduling algorithm is proposed to optimize file fetch process in transparent computing (TC) environment. A single TC server will receive file requests for operating systems, applications or user data from multiple clients. Considering limited size of server’s memory and the dependency between these file requests, the paper firstly addresses the features of valid file fetch sequence generating problem. Then explodes the methods to determine time cost for file fetch sequence. Based on the model established, we propose a heuristic and greedy (HG) algorithm at the end. According to the simulation results, we can conclude that HG algorithm can reduce overall file fetch time roughly by 50% in the best cases compared to the time cost of traditional approaches. © Springer International Publishing Switzerland 2015.

关键词： Green computing

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Want more? Pay more!

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作者： Yang, Xibei Qi, Yong Yu, Hualong Yang, Jingyu School of Economics and Management Nanjing University of Science and Technology Nanjing 210094 China Artificial Intelligence Key Laboratory of Sichuan Province Zigong 643000 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Nanjing University of Science and Technology Ministry of Education Nanjing 210094 China School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang 212003 China

ISBN: (纸本)9783319086439

Decision-theoretic rough set is a special rough set approach, which includes both misclassification and delayed decision costs. Though the property of monotonicity does not always hold in decision-theoretic rough set, the decision-monotonicity reduct may help us to increase both lower approximation and upper approximation. The experimental results in this paper tell us that by comparing with the original decision system, more cost is required with decision-monotonicity reduct. The implied philosophy is: if you want to get more, you should pay more! © 2014 Springer International Publishing.

关键词： Costs

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Discriminant Graph Regularized Non-negative Matrix Factorization（DGNMF） for Face Rrecognition

Discriminant Graph Regularized Non-negative Matrix Factoriza...

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2nd International Conference on Communication Technology(ICCT 2015)

作者： Minghua Wan Shan Gai School of Information Science and Engineering Southeast University Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education (Nanjing University of Science and Technology)

Cai et al develops a graph based approach called Graph Regularized Non-negative Matrix Factorization（GNMF） for parts-based data representation. It should be considered as an unsupervised method since class information is not used in the training set. To take advantage of more information in the training images and improve the performance for classification problem, in this paper, we propose a Discriminant Graph Regularized Non-negative Matrix Factorization（DGNMF） method to discover the manifold structure embedded in high-dimensional face space that is applied for face representation and recognition. In DGNMF, firstly, we encode the geometrical class information by constructing an affinity graph. Secondly, we seek a matrix factorization to respect the graph structure. At last, we do experimental results showing that DGNMF provides a better representation and achieves higher recognition rates in face recognition.

关键词： Non-negative Matrix Factorization(NMF) Graph Regularized Non-negative Matrix Factorization(GNMF) Face recognition Manifold

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United Domain Cognition Network for Salient Object Detection in Optical Remote Sensing Images

arXiv

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arXiv 2024年

作者： Sun, Yanguang Yang, Jian Luo, Lei The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Recently, deep learning-based salient object detection (SOD) in optical remote sensing images (ORSIs) have achieved significant breakthroughs. We observe that existing ORSIs-SOD methods consistently center around optimizing pixel features in the spatial domain, progressively distinguishing between backgrounds and objects. However, pixel information represents local attributes, which are often correlated with their surrounding context. Even with strategies expanding the local region, spatial features remain biased towards local characteristics, lacking the ability of global perception. To address this problem, we introduce the Fourier transform that generate global frequency features and achieve an image-size receptive field. To be specific, we propose a novel United Domain Cognition Network (UDCNet) to jointly explore the global-local information in the frequency and spatial domains. Technically, we first design a frequency-spatial domain transformer block that mutually amalgamates the complementary local spatial and global frequency features to strength the capability of initial input features. Furthermore, a dense semantic excavation module is constructed to capture higher-level semantic for guiding the positioning of remote sensing objects. Finally, we devise a dual-branch joint optimization decoder that applies the saliency and edge branches to generate high-quality representations for predicting salient objects. Experimental results demonstrate the superiority of the proposed UDCNet method over 24 state-of-the-art models, through extensive quantitative and qualitative comparisons in three widely-used ORSIs-SOD datasets. The source code is available at: https://***/CSYSI/UDCNet. © 2024, CC BY.

关键词： Optical remote sensing

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