检索结果-内蒙古大学图书馆

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Hou, Junlin Xiao, Fan Xu, Jilan Feng, Rui Zhang, Yuejie Zou, Haidong Lu, Lina Xue, Wenwen Fudan University School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Fudan University Academy for Engineering and Technology Shanghai China Shanghai Eye Diseases Prevention & Treatment Center Shanghai China

ISBN: (纸本)9781728163277

Explicit information of lesions can provide visual instructions for diabetic retinopathy (DR) grading on fundus images. However, pixel-level lesion annotations are extremely difficult and time-consuming to acquire. In this work, we propose a novel weakly-supervised lesion-aware network for DR grading, which enhances the discriminative features with lesion priors by only image-level supervision. Specifically, we design a lesion attention module that generates lesion activation maps by introducing an auxiliary task of binary DR identification. Lesion activation maps are utilized to assist the network to focus on the most relevant regions for boosting DR grading performance. Besides, we particularly devise an adaptive joint loss to balance the DR identification and DR grading tasks dynamically. Extensive results on the public DR dataset demonstrate the superiority and generality of our proposed lesion-aware network. The interpretability of generated lesion activation maps is also verified by the comparison with ground truth segmentation masks. © 2023 IEEE.

关键词： Activation map Diabetic retinopathy grading Fundus image Lesion priors Weakly-supervised

来源：评论

学校读者我要写书评

暂无评论

Discovering Reliable Information Extraction Patterns with Pre-Trained Model for Text with Writing Style

Discovering Reliable Information Extraction Patterns with Pr...

引用

2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bu, Chenyang Liu, Jiacheng Liu, Jiaxuan Ji, Shengwei Yang, Hongbin School of Computer Science and Information Engineering Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Anhui Hefei230027 China School of Artificial Intelligence and Big Data Hefei University China

ISBN: (纸本)9798350337020

Large-scale pre-trained models such as GPT and BERT have demonstrated remarkable performance in information extraction tasks. However, their black-box nature poses challenges for reliability and interpretability. In contrast, rule- based extraction methods have better interpretability, but typically require domain experts to manually establish rules, limiting their generalization ability. In industry, there is often a demand for reliable knowledge extraction to reduce the time spent on manual verification of each piece of knowledge. In this paper, we explore the idea of combining GPT and symbolic-based methods to automatically discover reliable extraction patterns in text with a particular writing style. This method leverages the characteristics of high information density and similar writing patterns in text with a specific writing style to generate verifiable and reliable patterns. We conduct experiments on two datasets with a specific writing style to demonstrate its effectiveness, validating the idea of combining large models for reliable information extraction pattern discovery in the tested datasets. © 2023 IEEE.

关键词： Information extraction Reliable pattern Writing style data

来源：评论

学校读者我要写书评

暂无评论

MM-Tracker: Motion Mamba with Margin Loss for UAV-platform Multiple Object Tracking

arXiv

引用

arXiv 2024年

作者： Yao, Mufeng Peng, Jinlong He, Qingdong Peng, Bo Chen, Hao Chi, Mingmin Liu, Chao Benediktsson, Jon Atli School of Computer Science Shanghai Key Laboratory of Data Science Fudan University China Tencent Youtu Lab China Shanghai Ocean University China University of Iceland Iceland

Multiple object tracking (MOT) from unmanned aerial vehicle (UAV) platforms requires efficient motion modeling. This is because UAV-MOT faces both local object motion and global camera motion. Motion blur also increases the difficulty of detecting large moving objects. Previous UAV motion modeling approaches either focus only on local motion or ignore motion blurring effects, thus limiting their tracking performance and speed. To address these issues, we propose the Motion Mamba Module, which explores both local and global motion features through cross-correlation and bi-directional Mamba Modules for better motion modeling. To address the detection difficulties caused by motion blur, we also design motion margin loss to effectively improve the detection accuracy of motion blurred objects. Based on the Motion Mamba module and motion margin loss, our proposed MM-Tracker surpasses the state-of-the-art in two widely open-source UAV-MOT datasets. Code is available at: https://***/YaoMufeng/MMTracker Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Time-Sensitive Local Differential Privacy-Based Federated Learning for Vehicular Digital Twin Networks 1st

Time-Sensitive Local Differential Privacy-Based Federated Le...

引用

1st International Symposium on Intelligent Computing and Networking, ISICN 2024

作者： Sun, Kangkang Xu, Hansong Zhang, Xiaojin Hua, Kun Li, Jianhua Shanghai Key Laboratory of Integrated Administration Technologies for Information Security School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Electrical Engineering California Polytechnic State University San Luis ObispoCA93407 United States

ISBN: (纸本)9783031674464

The Vehicular Digital Twin Network (VDTN) integrates digital twin (DT) models of connected and autonomous vehicles (CAVs) to facilitate smart sensing, management, and collaboration in the physical realm. VDTN synchronizes extensive data on roads, vehicles, and operators, including sensitive information like user location and driving habits. To address these privacy concerns and enhance model efficiency, we introduce a time-sensitive local differential privacy-based federated learning framework (FedTS) tailored for VDTN. This framework assigns varying privacy levels to DT models during model-sharing intervals, considering the time sensitivity of VDTNs. Comparative analysis with state-of-the-art algorithms, such as differentially-private stochastic gradient descent (DP-SGD), demonstrates significant improvements in both security and model performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Improving viewpoint-independent object-centric representations through active viewpoint selection 24

Improving viewpoint-independent object-centric representatio...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yinxuan Huang Chengmin Gao Bin Li Xiangyang Xue Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University

ISBN: (纸本)9798331314385

Given the complexities inherent in visual scenes, such as object occlusion, a comprehensive understanding often requires observation from multiple viewpoints. Existing multi-viewpoint object-centric learning methods typically employ random or sequential viewpoint selection strategies. While applicable across various scenes, these strategies may not always be ideal, as certain scenes could benefit more from specific viewpoints. To address this limitation, we propose a novel active viewpoint selection strategy. This strategy predicts images from unknown viewpoints based on information from observation images for each scene. It then compares the object-centric representations extracted from both viewpoints and selects the unknown viewpoint with the largest disparity, indicating the greatest gain in information, as the next observation viewpoint. Through experiments on various datasets, we demonstrate the effectiveness of our active viewpoint selection strategy, significantly enhancing segmentation and reconstruction performance compared to random viewpoint selection. Moreover, our method can accurately predict images from unknown viewpoints.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improved Machine Learning Approach for shanghai City's Secondhand Housing Price Prediction

Improved Machine Learning Approach for Shanghai City's Secon...

引用

computer science, computer Engineering, & Applied Computing (CSCE), Congress in

作者： Guang Hu Yue Tang Rui Yi School of Statistics and Information Shanghai University of International Business and Economics School of Computer Science Fudan University Shanghai Key Laboratory of Data Science Shanghai China

This paper presents an improved machine learning approach for prediction of second-hand housing prices in shanghai. It firstly builds the random forest model and the XGboost model with shanghai second-hand housing transaction data, and then analyses the challenges in the improvement of the models' prediction accuracy and generalization performance. In the light of that, it introduces the Lasso model for variable selection, and deletes three variables with insignificant regression coefficients, and then rebuilds the random forest and XGboost prediction models. The experimental results show that the prediction accuracy and the generalization performance of the models is well improved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Image Denoising Method Based on Channel Compression and Split-Attention Mechanism 5

Image Denoising Method Based on Channel Compression and Spli...

引用

5th International Conference on computer Engineering and Intelligent Control, ICCEIC 2024

作者： Sun, Hao Qiao, Xiaoyan School of Computer Science and Technology Shandong Technology and Business University Technology and Evaluation Shandong Engineering Research Center Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Shandong Yantai China

ISBN: (纸本)9798331507992

In the image restoration task, how to make full use of spatial and channel feature information to improve the reconstruction quality of the model without significantly increasing the computational complexity is an important research issue. To address this challenge, this paper proposes a CS module (Channel Compression and Split-Attention) specifically for optimizing image denoising tasks. The module first compresses the features through a preliminary convolution operation, then introduces the split attention convolution mechanism to split the features by channel and dynamically weight them, thereby enhancing the model's adaptive attention to different feature channels and improving feature expression capabilities. Through experiments on multiple image denoising datasets, the results show that the CS module significantly improves the image reconstruction quality, especially in terms of peak signal-to-noise ratio (PSNR). At the same time, the introduction of the CS module does not lead to a significant increase in computational complexity, maintaining a high computational efficiency, which verifies the practicality and superiority of the module in image denoising tasks. © 2024 IEEE.

关键词： Restoration

来源：评论

学校读者我要写书评

暂无评论

Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models

arXiv

引用

arXiv 2025年

作者： Zhang, Siwei Xiong, Yun Tang, Yateng Chen, Xi Jia, Zian Gu, Zehao Xu, Jiarong Zhang, Jiawei Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Wechat Pay Tencent Group China IFM Lab Department of Computer Science University of California Davis United States

Temporal graph neural networks (TGNNs) have shown remarkable performance in temporal graph modeling. However, real-world temporal graphs often possess rich textual information, giving rise to temporal text-attributed graphs (TTAGs). Such combination of dynamic text semantics and evolving graph structures introduces heightened complexity. Existing TGNNs embed texts statically and rely heavily on encoding mechanisms that biasedly prioritize structural information, overlooking the temporal evolution of text semantics and the essential interplay between semantics and structures for synergistic reinforcement. To tackle these issues, we present CROSS, a novel framework that seamlessly extends existing TGNNs for TTAG modeling. The key idea is to employ the advanced large language models (LLMs) to extract the dynamic semantics in text space and then generate expressive representations unifying both semantics and structures. Specifically, we propose a Temporal Semantics Extractor in the CROSS framework, which empowers the LLM to offer the temporal semantic understanding of node’s evolving contexts of textual neighborhoods, facilitating semantic dynamics. Subsequently, we introduce the Semantic-structural Co-encoder, which collaborates with the above Extractor for synthesizing illuminating representations by jointly considering both semantic and structural information while encouraging their mutual reinforcement. Extensive experimental results on four public datasets and one practical industrial dataset demonstrate CROSS’s significant effectiveness and robustness. © 2025, CC BY.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

OnMKD: An Online Mutual Knowledge Distillation Framework for Passage Retrieval 12th

OnMKD: An Online Mutual Knowledge Distillation Framework for...

引用

12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Deng, Jiali Li, Dongyang Zhang, Taolin He, Xiaofeng School of Computer Science and Technology East China Normal University Shanghai China School of Software Engineering East China Normal University Shanghai China NPPA Key Laboratory of Publishing Integration Development ECNUP Shanghai China

ISBN: (纸本)9783031446955

Dense passage retriever recalls a set of relevant passages from a large corpus according to a natural language question. The dual-encoder architecture is prevalent in dense passage retrievers, which is based on large-scale pre-trained language models (PLMs). However, existing PLMs usually have thick structures and bulky parameters, resulting in large memory and time consumption. To overcome the limitation of PLMs, in this paper we apply online distillation to passage retrieval and propose an Online Mutual Knowledge Distillation framework (OnMKD). Specifically, we obtain a lightweight retriever by simultaneously updating two peer networks with the same dual-encoder structure and different initial parameters, named Online Mutual Knowledge Refinement. To further interact with the latent knowledge of intermediate layers, we utilize a novel cross-wise contrastive loss to alternate the representation of questions and passages. Experimental results indicate that our framework outperforms other small baselines with the same number of layers on multiple QA benchmarks. Compared to the heavy PLMs, OnMKD significantly accelerates the inference process and reduces storage requirements with only a slight sacrifice in performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

The GECAM ground search system for gamma-ray transients

引用

science China(Physics,Mechanics & Astronomy) 2025年第3期68卷 161-174页

作者： Ce Cai Yan-Qiu Zhang Shao-Lin Xiong Ping Wang Jian-Hui Li Xiao-Bo Li Cheng-Kui Li Yue Huang Shi-Jie Zheng Li-Ming Song Shuo Xiao Qi-Bin Yi Yi Zhao Sheng-Lun Xie Rui Qiao Yan-Qi Du Zhi-Wei Guo Wang-Chen Xue Chao Zheng Jia-Cong Liu Chen-Wei Wang Wen-Jun Tan Yue Wang Jin-Peng Zhang Chao-Yang Li Guo-Ying Zhao Xiao-Yun Zhao Xiao-Lu Zhang Zhen Zhang Wen-Xi Peng Xiang Ma Jing-Yan Shi Dong-Ya Guo Jin Wang Xin-Qiao Li Xiang-Yang Wen Zheng-Hua An Fan Zhang College of Physics and Hebei Key Laboratory of Photophysics Research and Application Hebei Normal UniversityShijiazhuang050024China Key Laboratory of Particle Astrophysics Institute of High Energy PhysicsChinese Academy of SciencesBeijing100049China University of Chinese Academy of Sciences Beijing100049China School of Physics and Electronic Science Guizhou Normal UniversityGuiyang550001China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal UniversityGuiyang550001China School of Physics and Optoelectronics Xiangtan UniversityXiangtan411105China School of Computer and Information Dezhou UniversityDezhou253023China Institute of Astrophysics Central China Normal UniversityWuhan430079China

In the era of time-domain,multi-messenger astronomy,the detection of transient events on the high-energy electromagnetic sky has become more important than *** gravitational wave high-energy electromagnetic counterpart all-sky monitor(GECAM)is a dedicated mission to monitor gamma-ray transients,launched in December,2020.A real-time on-board trigger and location software,using the traditional signal-to-noise ratio(SNR)method for blind search,is constrained to relatively bright signals due to the limitations in on-board computing resources and the need for real-time *** this work,we developed a ground-based pipeline for GECAM to search for various transients,especially for weak bursts missed by on-board *** pipeline includes both automatic and manual mode,offering options for blind search and targeted *** targeted search is specifically designed to search for interesting weak bursts,such as gravitational wave-associated gamma-ray bursts(GRBs).From the ground search of the data in the first year,GECAM has been triggered by 54 GRBs and other transients,including soft gamma-ray repeaters,X-ray binaries,solar flares,terrestrial gamma-ray *** report the properties of each type of triggers,such as trigger time and light *** this search pipeline and assuming a soft Band spectrum,the GRB detection sensitivity of GECAM is increased to about 1.1×10^(-8)erg cm^(-2)s^(-1)(10-1000 ke V,burst duration of 20 s).These results demonstrate that the GECAM ground search system(both blind search and targeted search)is a versatile pipeline to recover true astrophysical signals which were too weak to be found in the on-board search.

关键词： gamma-ray sources gravitational waves data analysis gamma-ray telescopes

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：