检索结果-内蒙古大学图书馆

IEEE International Conference on Data Mining (ICDM)

作者： Yewei Xia Hao Zhang Yixin Ren Jihong Guan Shuigeng Zhou Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University China Department of Computer Science & Technology Tongji University China

Discovering causal relationships from observational data is a challenging topic in artificial intelligence. Recent works formulate causal discovery as a continuous optimization problem with a differentiable acyclic constraint. Although these methods have achieved considerable performance improvement, they have two drawbacks: 1) they require a relatively large number of training samples; and 2) their performance will substantially deteriorate when facing heterogeneous noise. To address these problems, we first propose a low-order conditional independence (CI) constraint for the continuous optimization problem, and then design a soft version of the constraint by transforming it to a regularization term in the loss function of the continuous optimization problem. We show the convergence of continuous optimization with our constraint under some mild conditions, and the consistency of causal structure learning with the CI regularization. Extensive experiments on both synthetic and real-world datasets show that with our CI constraint or regularization, existing continuous optimization methods can achieve considerable performance improvement of causal discovery, especially when sample size is small.

关键词：

来源：评论

学校读者我要写书评

暂无评论

scRNA-seq Data Clustering by Cluster-aware Iterative Contrastive Learning

arXiv

引用

arXiv 2023年

作者： Jiang, Weikang Wang, Jinxian Guan, Jihong Zhou, Shuigeng Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Department of Computer Science and Technology Tongji University China

Single-cell RNA sequencing (scRNA-seq) enables researchers to analyze gene expression at single-cell level. One important task in scRNA-seq data analysis is unsupervised clustering, which helps identify distinct cell types, laying down the foundation for other downstream analysis tasks. In this paper, we propose a novel method called Cluster-aware Iterative Contrastive Learning (CICL in short) for scRNA-seq data clustering, which utilizes an iterative representation learning and clustering framework to progressively learn the clustering structure of scRNA-seq data with a cluster-aware contrastive loss. CICL consists of a Transformer encoder, a clustering head, a projection head and a contrastive loss module. First, CICL extracts the feature vectors of the original and augmented data by the Transformer-encoder. Then, it computes the clustering centroids by K-means and employs the student’s t-distribution to assign pseudo-labels to all cells in the clustering head. The projection-head uses a Multi-Layer Perceptron (MLP) to obtain projections of the augmented data. At last, both pseudo-labels and projections are used in the contrastive loss to guide the model training. Such a process goes iteratively so that the clustering result becomes better and better. Extensive experiments on 25 real-world scRNA-seq datasets show that CICL outperforms the state-of-the-art (SOTA) methods. Concretely, CICL surpasses the existing methods by from 14% to 280%, and from 5% to 133% on average in terms of performance metrics ARI and NMI respectively. Source code is available at https://***/Alunethy/CICL. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

ATTENTION DRIVEN SELF-SIMILARITY CAPTURE FOR MOTION DEBLURRING

ATTENTION DRIVEN SELF-SIMILARITY CAPTURE FOR MOTION DEBLURRI...

引用

2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Zhang, Jie Zhang, Chuanfa Wang, Jiangzhou Xiong, Qingyue Zhang, Yingtao Zhang, Wenqiang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China

ISBN: (纸本)9781665438643

Recently, deep learning-based algorithms have brought impressive results in deblurring tasks. However, as an image prior proved important in image restoration tasks, self-similarity was not exploited in motion deblurring. To tackle this problem, we propose an Attention Self-Similarity Capture (ASSC) module, which takes full advantage of self-similarity by capturing long-range feature dependencies. Besides, to achieve a trade-off between performance and efficiency, we design an Enhanced Spatial Attention (ESA) module, which can dynamically adapt to the spatially-varying motion blur. We employ patch-hierarchical architecture composed of the two modules mentioned above with parameter-free feature flow between different levels. Moreover, we build two large-scale datasets, GOPRO-Supplement and SONY-Extension, to expand the public GOPRO dataset's scene and resolution. Extensive experiments demonstrate that our method outperforms state-of-the-art methods on both the public GOPRO dataset and our datasets. © 2021 IEEE

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

MoME: Mixture of Multi-Domain Experts for Multivariate Long-Term Series Forecasting

MoME: Mixture of Multi-Domain Experts for Multivariate Long-...

引用

International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xinyu Li Zhiheng Yang Hao Xu Yunqi Cai Hong Lu Xin Wang Jin Zhao Fenglin Qi Jiajie Shen School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Informatization Office Fudan University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Time series forecasting is always important, with multivariate long-term series forecasting being its most challenging task. Here, the existing methods typically learn only in a single domain and focus on optimizing model structures, leading to incomplete information mining and imprecise predictions. To address this, we propose a generalized Mixture of Multi-Domain Experts (MoME) for multivariate long-term series forecasting. Unlike most existing methods, MoME focuses on multi-perspective information mining and fusing. To this end, MoME transforms time series into the frequency and spatial domains to learn their respective representations. MoME regards variates information as embedded features and applies fast Fourier transform to the time dimension. Then it learns embedded features in the frequency domain. In spatial domain learning, MoME applies self-attention mechanism on the variates dimension to efficiently capture dependencies among multiple variates. Finally, MoME fuses the outputs from all domains, reinterprets and integrates information across multiple domains, and predicts future time series. Extensive experiments prove that MoME outperforms state-of-the-art (SOTA) methods. Code is available at: https://***/lxy-PhD2022/MoME

关键词： Time-frequency analysis Fuses Time series analysis Transforms Signal processing Predictive models Frequency conversion Forecasting Speech processing Periodic structures

来源：评论

学校读者我要写书评

暂无评论

Semantic-Driven Saliency-Context Separation for Video Captioning

Semantic-Driven Saliency-Context Separation for Video Captio...

引用

2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Jing, Heming Zhang, Yuejie Feng, Rui Zhao, Rui-Wei Zhang, Tao Lu, Xuequan Gao, Shang School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Fudan University Academy for Engineering and Technology China School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics China School of Information Technology Deakin University Australia

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Video captioning aims at generating a natural language de-scription for a given video clip including not only salient sce-narios but also contextual scenarios. The former reveal the highlight of a video and are usually the focus of most existing captioning methods. The latter, however, are not well ex-plored and even ignored easily, though they may provide cer-tain detailed and latent information that can help with a better understanding of the video. To effectively exploit the infor-mation contained in both, a novel video captioning network is proposed. It has two key modules: Cross-Modality Selection (CMS) and Saliency-Context Adaptive Decoder (SCAD). Specifically, CMS mainly focuses on utilizing the semantic information to distinguish saliency and context. Meanwhile, SCAD adaptively identifies both the saliency and context to generate more detailed and precise captions. Experiments on two benchmark datasets, i.e., MSVD and MSR-VTT, demon-strate the effectiveness of our model through the comparison with state-of-the-art methods. © 2022 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

STDNet: Spatio-Temporal Decomposed Network for Video Grounding

STDNet: Spatio-Temporal Decomposed Network for Video Groundi...

引用

2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Xu, Yuanwu Zhang, Yuejie Feng, Rui Zhao, Rui-Wei Zhang, Tao Lu, Xuequan Gao, Shang School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing China Academy for Engineering and Technology Fudan University China School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai Key Laboratory of Financial Information Technology China School of Information Technology Deakin University Australia

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Previous methods for video grounding treated either the query or the video as a whole, while neglecting their respective semantics in the orthogonal space and time dimensions. Since spatial semantics appears frequently in a video, temporal semantics is more discriminative and deserves more attention. Based on such considerations, we propose a novel Spatio-Temporal Decomposed Network (STDNet) which decomposes the query and the video into their spatial and temporal semantics, respectively. Specifically, spatial and temporal words are selected from the query, and the video is split into two pathways. Spatial cross-modal attention is computed first and serves as prior knowledge for temporal attention. A new localization strategy is also devised which regresses the segment's start conditioned on the end and essentially breaks the independence assumption made in previous methods. Experimental results on three public benchmark datasets show that our STDNet outperforms the state-of-the-art methods. © 2022 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Weakly-supervised Temporal Action Localization with Adaptive Clustering and Refining Network

Weakly-supervised Temporal Action Localization with Adaptive...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Hao Ren Wu Ran Xingson Liu Haoran Ren Hong Lu Rui Zhang Cheng Jin Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Peng Cheng Laboratory Shenzhen China

Weakly-supervised temporal action localization task aims to localize temporal boundaries of action instances by using only video-level labels. Existing methods primarily adopt Multi-Instance-Learning (MIL) scheme to handle this task. The effectiveness of MIL scheme depends heavily on the selection of top-k action snippets, which is unstable and requires manual tuning. To address these deficiencies, we propose an Adaptive Clustering and Refining Network (ACRNet). Specifically, we present an action-aware clustering strategy that is adaptable and requires no manual tuning to separate action and background snippets of diverse videos based on intra-class activation distribution. And a cluster refining step is included to eliminate false action snippets by considering inter-class activation distribution, which greatly improves robustness and localization accuracy. Extensive experiments on THUMOS14, ActivityNet 1.2&1.3 benchmarks show that our method achieves state-of-the-art performance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Long-Term Cloth-Changing Person Re-identification 15th

Long-Term Cloth-Changing Person Re-identification

引用

15th Asian Conference on computer Vision, ACCV 2020

作者： Qian, Xuelin Wang, Wenxuan Zhang, Li Zhu, Fangrui Fu, Yanwei Xiang, Tao Jiang, Yu-Gang Xue, Xiangyang School of Computer Science Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China School of Data Science and MOE Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China University of Surrey Guildford United Kingdom

ISBN: (纸本)9783030695347

Person re-identification (Re-ID) aims to match a target person across camera views at different locations and times. Existing Re-ID studies focus on the short-term cloth-consistent setting, under which a person re-appears in different camera views with the same outfit. A discriminative feature representation learned by existing deep Re-ID models is thus dominated by the visual appearance of clothing. In this work, we focus on a much more difficult yet practical setting where person matching is conducted over long-duration, e.g., over days and months and therefore inevitably under the new challenge of changing clothes. This problem, termed Long-Term Cloth-Changing (LTCC) Re-ID is much understudied due to the lack of large scale datasets. The first contribution of this work is a new LTCC dataset containing people captured over a long period of time with frequent clothing changes. As a second contribution, we propose a novel Re-ID method specifically designed to address the cloth-changing challenge. Specifically, we consider that under cloth-changes, soft-biometrics such as body shape would be more reliable. We, therefore, introduce a shape embedding module as well as a cloth-elimination shape-distillation module aiming to eliminate the now unreliable clothing appearance features and focus on the body shape information. Extensive experiments show that superior performance is achieved by the proposed model on the new LTCC dataset. The dataset is available on the project website: https://***/LTCC_Perosn_***. © 2021, Springer Nature Switzerland AG.

关键词： Large dataset

来源：评论

学校读者我要写书评

暂无评论

An Interpretable Causal Approach for Bronchopulmonary Dysplasia Prediction

An Interpretable Causal Approach for Bronchopulmonary Dyspla...

引用

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Sun, Siqi Ji, Wei Tang, Liangfeng Zhang, Xiaobo Zhang, Yuejie Feng, Rui Fudan University School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Fudan University Academy for Engineering and Technology Shanghai China Fudan University Children's Hospital Shanghai China

ISBN: (纸本)9781665468190

In this paper, we focus on the Bronchopulmonary Dysplasia (BPD) prediction task, which aims to identify the BPD in premature infants based on the given medical images. The existing methods for this task sometimes learn the spurious relations (confounders) between the image and label while ignore the causal features due to the limited size of the dataset, which largely influences the interpretability and robustness of the model. To address this challenge problem, we propose an interpretable causal method for BPD prediction, which can eliminate the irrelevant features and capture the causal features. We term our method as Causal Intervention by semantic instrumental Variable (CisiV). First, we design a causal structure graph modeling module to learn the representations of instrumental variables and confounders automatically. The constraints of confounders further guarantee the instrumental variables validity. Meanwhile, we add congenital attributes of premature infants to enable the learned instrumental variables to have medical semantics via a semantic matching module. We apply our model to BPD prediction in premature infants and achieve promising results. Extensive experiments indicate that CisiV could facilitate early intervention and provide support for clinical decision-making. Codes will be released to the community. © 2022 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Incomplete intuitionistic fuzzy behavioral group decision-making based on multigranulation probabilistic rough sets and MULTIMOORA for water quality inspection

引用

Journal of intelligent and Fuzzy Systems 2023年第3期44卷 4537-4556页

作者： Bai, Wenhui Zhang, Chao Zhai, Yanhui Sangaiah, Arun Kumar School of Computer and Information Technology Key Lab. of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Water quality inspection (WQI) is one of the primary ways to ensure the safe utilization of water resources, and complicated data modeling, fusion and analysis play a significant role in seeking the resource with the best water quality. Nevertheless, the challenges of missing data, relatively large differences in decision results and bounded rationality owned by decision-makers (DMs) in terms of WQI still exist nowadays. Thus, from the aspect of stable and behavioral decision-making in multi-granularity incomplete intuitionistic fuzzy information systems (MG-IIFISs), the paper investigates a comprehensive multi-attribute group decision-making (MAGDM) approach for the application of WQI. First, the concept of MG-IIFISs is built by modeling MAGDM problems with intuitionistic fuzzy numbers (IFNs), then a new transformation scheme is constructed for transforming MG-IIFISs into multi-granularity intuitionistic fuzzy information systems (MG-IFISs) based on the similarity principle. Second, three types of multigranulation intuitionistic fuzzy probabilistic rough sets (MG IF PRSs) are developed by referring to the MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form) method. Afterwards, attribute weights are objectively calculated based on the best-worst method (BWM), and a new stable and behavioral MAGDM approach is constructed by means of the TODIM (an acronym in Portuguese for interactive multi-criteria decision-making) method. At last, a case study in the setting of WQI is conducted with the support of a UCI data set, and sensitivity analysis, comparative analysis and experimental analysis are investigated to display the validity of the proposed approach. In general, the proposed approach improves the stability of decision results via MULTIMOORA and BWM, and also fully considers the bounded rationality of DMs' psychological behaviors from the aspect of the TODIM method, which has certain advantages in the community of MAGDM studie

关键词： Water quality

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：