检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Wang, Chenglong Yi, Jiangyan Zhang, Xiaohui Tao, Jianhua Xu, Le Fu, Ruibo University of Science and Technology of China Hefei China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences China School of Computer and Information Technology Beijing Jiaotong University China Department of Automation Tsinghua University China

Self-supervised speech models are a rapidly developing research topic in fake audio detection. Many pre-trained models can serve as feature extractors, learning richer and higher-level speech features. However,when fine-tuning pre-trained models, there is often a challenge of excessively long training times and high memory consumption, and complete fine-tuning is also very expensive. To alleviate this problem, we apply low-rank adaptation(LoRA) to the wav2vec2 model, freezing the pre-trained model weights and injecting a trainable rank-decomposition matrix into each layer of the transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared with fine-tuning with Adam on the wav2vec2 model containing 317M training parameters, LoRA achieved similar performance by reducing the number of trainable parameters by 198 times. Copyright © 2023, The Authors. All rights reserved.

关键词： Fake detection

来源：评论

学校读者我要写书评

暂无评论

Typical curve with G1 constraints for curve completion

引用

Visual Computing for Industry,Biomedicine,and Art 2021年第1期4卷 283-294页

作者： Chuan He Gang Zhao Aizeng Wang Fei Hou Zhanchuan Cai Shaolin Li School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China State Key Laboratory of Lunar and Planetary Sciences Macao University of Science and TechnologyMacao 999078China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100049China Faculty of Information Technology Macao University of Science and TechnologyMacao 999078China

This paper presents a novel algorithm for planar G1 interpolation using typical curves with monotonic *** G1 interpolation problem is converted into a system of nonlinear equations and sufficient conditions are provided to check whether there is a *** proposed algorithm was applied to a curve completion *** main advantages of the proposed method are its simple construction,compatibility with NURBS,and monotonic curvature.

关键词： Typical curves Monotonic curvature G1 interpolation Curve completion Euler spiral

来源：评论

学校读者我要写书评

暂无评论

mvEchoSeg: One-shot In-context Learning for Multi-view Echocardiography Segmentation

mvEchoSeg: One-shot In-context Learning for Multi-view Echoc...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yuting Guo Ya Duan Wenfeng Song Nannan Li Aili Li Shuai Li State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China School of Computer Science Beijing Information Science and Technology University Beijing China School of Information Science and Technology Dalian Maritime University Dalian China China-Japan Friendship Hospital Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Echocardiography is the clinical standard for evaluation of cardiac morphology, and function, and providing hemodynamic parameters in patients with known or suspected heart disease. Due to the diversity and complexity of diagnostic tasks, comprehensive interpretation of echocardiography often requires multi-view imaging for integrated metric analysis. Deep learning methods have become the mainstream approach for echocardiography segmentation. However, achieving segmentation of multi-view echocardiography still requires a substantial amount of annotation. To remedy this, we propose a one-shot in-context learning network mvEchoSeg for multi-view echocardiography segmentation. This network requires only one annotated image for each view. Specifically, we propose a Task Prompt Identifier (TPI) module to identify the task type of the image and allocate the most precise task prompt for it, with minimal adaptation of CLIP and few-shot strategies. Additionally, we leverage a unified In-Context Model (ICM) capable of performing a diverse set of echocardiography segmentation tasks automatically. Using fine-tuning and low-rank adapters improved the performance of the pre-trained model, achieving significant results with minimal training cost. Furthermore, we collect a multi-view echocardiography dataset (MVECD) with 8 views to evaluate our method. The results show an improvement of more than 10% in the DICE score compared to the SOTA foundational medical image segmentation models. To our knowledge, this is the first exploration of a one-shot model for multi-view echocardiography segmentation. Our codes and models are available at https://***/stellating/mvEchoSeg.

关键词： Training Measurement Image segmentation Adaptation models Echocardiography Biological system modeling Morphology Hemodynamics Standards Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

HCF-Net: Hierarchical Context Fusion Network for Infrared Small Object Detection

arXiv

引用

arXiv 2024年

作者： Xu, Shibiao Zheng, ShuChen Xu, Wenhao Xu, Rongtao Wang, Changwei Zhang, Jiguang Teng, Xiaoqiang Li, Ao Guo, Li Artificial Intelligence Beijing University of Posts and Telecommunications China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science China School of Computer Science and Technology Harbin University of Science and Technology China

Infrared small object detection is an important computer vision task involving the recognition and localization of tiny objects in infrared images, which usually contain only a few pixels. However, it encounters difficulties due to the diminutive size of the objects and the generally complex backgrounds in infrared images. In this paper, we propose a deep learning method, HCF-Net, that significantly improves infrared small object detection performance through multiple practical modules. Specifically, it includes the parallelized patch-aware attention (PPA) module, dimension-aware selective integration (DASI) module, and multi-dilated channel refiner (MDCR) module. The PPA module uses a multi-branch feature extraction strategy to capture feature information at different scales and levels. The DASI module enables adaptive channel selection and fusion. The MDCR module captures spatial features of different receptive field ranges through multiple depth-separable convolutional layers. Extensive experimental results on the SIRST infrared single-frame image dataset show that the proposed HCF-Net performs well, surpassing other traditional and deep learning models. Code is available at https://***/zhengshuchen/HCFNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Context Convolution Dehazing Network With Channel Attention 5

Context Convolution Dehazing Network With Channel Attention

引用

5th Asian Conference on Artificial Intelligence Technology, ACAIT 2021

作者： Li, Yuanyuan Meng, Jun Zhu, Zhiqin Huang, Xinghua Qi, Guanqiu Luo, Yaqin Chongqing University of Posts and Telecommunications Chongqin Key Laboratory of Image Cognition Chongqing400065 China Chongqing University College of Automation Chongqing400044 China Buffalo State College Computer Information Systems Department BuffaloNY14222 United States

ISBN: (纸本)9781665426305

Fog and haze weather conditions lead to deterioration of image visual quality. Under these conditions, advanced image processing tasks such as object detection and image segmentation are difficult to perform. To solve related problems, in this paper we propose an end-to-end context dilated convolution dehazing network with channel attention to return to a clear image from a haze image. This model uses context dilated module extracts the multi-scale scene information in the haze image, which can better maintain the original color of the image while removing the haze. The channel attention enables the model to separate the importance of features and boosts the model's power to adapt to different input scenarios. In the training phase, the model uses contrastive learning to distinguish the potential difference between the haze picture and the clear picture, helping the model to better renewal the clear image. The results of contrastive tests with existing methods indicate the proposed method has excellent dehazing performance. © 2021 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Abstraction-Based Synthesis of Controllers for Approximate Opacity

Abstraction-Based Synthesis of Controllers for Approximate O...

引用

IEEE Conference on Decision and Control

作者： Junyao Hou Siyuan Liu Xiang Yin Majid Zamani Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Computer Science University of Colorado Boulder CO USA Department of Computer Science LMU Munich Munich Germany

Opacity is an important information-flow security property which characterizes the plausible deniability of certain “secret behaviors” in dynamical systems. In this paper, we study the problem of synthesizing controllers enforcing a notion of opacity over discrete-time control systems with continuous state sets. In this paper, we develop an abstraction-based approach to tackle the controller synthesis problem. Specifically, we adopt a notion of approximate opacity which is suitable for continuous-space control systems. We propose a notion of approximate initial-state opacity preserving alternating simulation relation which characterizes the closeness between two systems in terms of opacity preservation. We show that, based on this new notion of system relation, one can synthesize an opacity-enforcing controller for the abstract system which is finite and then refine it back to enforce opacity over the original control system. Finally, we present a method for constructing opacity-preserving finite abstractions for discrete-time control systems under some stability properties. Our results are illustrated on a two-room temperature control problem.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Task-aware Attentional Dynamic Alignment for Few-Shot Compressed Video Classification

引用

IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Luo, Wenyang Liu, Yufan Li, Bing Hu, Weiming Maybank, Stephen Chinese Academy of Sciences Institute of Automation State Key Laboratory of Multimodal Artificial Intelligence Systems China University of Chinese Academy of Sciences School of Artificial Intelligence China PeopleAI Inc China ShanghaiTech University School of Information Science and Technology China University of London School of Computer Science and Mathematics Birkbeck College LondonWC1E 7HX United Kingdom

We present a novel Task-aware Attentional Dynamic Alignment (TADA) framework for visual-based few-shot video classification (FSVC) that addresses two key challenges in this field: efficiency and nuanced spatio-temporal reasoning. Existing methods are often hindered by computationally expensive video decoding processes and neglect the temporal order of videos. In contrast, our method harnesses compressed domain data to extract rich spatio-temporal cues at a fraction of the cost of traditional video processing methods. Specifically, we propose an embedding module to extract informative features from compressed domain data while minimizing computational overheads. Furthermore, to exploit the temporal order of frames, we develop a prototypical ADA module to align and classify videos with an explicit temporal order constraint. Our framework also incorporates a contextual mixer to enrich video embeddings with task-specific context. Extensive experiments on multiple datasets demonstrate that TADA achieves state-of-the-art performance and outperforms existing methods in accuracy and efficiency. © 2025 IEEE.

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

Hybrid Data-Free Knowledge Distillation

arXiv

引用

arXiv 2024年

作者： Tang, Jialiang Chen, Shuo Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Center for Advanced Intelligence Project RIKEN Japan Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generation-based methods train student networks by collecting massive real examples and generating synthetic examples, respectively. However, they inevitably become weak in practical scenarios due to the difficulties in gathering or emulating sufficient real-world data. To solve this problem, we propose a novel method called Hybrid Data-Free Distillation (HiDFD), which leverages only a small amount of collected data as well as generates sufficient examples for training student networks. Our HiDFD comprises two primary modules, i.e., the teacher-guided generation and student distillation. The teacher-guided generation module guides a Generative Adversarial Network (GAN) by the teacher network to produce high-quality synthetic examples from very few real-world collected examples. Specifically, we design a feature integration mechanism to prevent the GAN from overfitting and facilitate the reliable representation learning from the teacher network. Meanwhile, we drive a category frequency smoothing technique via the teacher network to balance the generative training of each category. In the student distillation module, we explore a data inflation strategy to properly utilize a blend of real and synthetic data to train the student network via a classifier-sharing-based feature alignment technique. Intensive experiments across multiple benchmarks demonstrate that our HiDFD can achieve state-of-the-art performance using 120 times less collected data than existing methods. Code is available at https://***/tangjialiang97/HiDFD. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Mixture of Experts for Intelligent Networks: A Large Language Model-enabled Approach

Mixture of Experts for Intelligent Networks: A Large Languag...

引用

International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Hongyang Du Guangyuan Liu Yijing Lin Dusit Niyato Jiawen Kang Zehui Xiong Dong In Kim School of Computer Science and Engineering Nanyang Technological University Singapore Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of Automation Guangdong University of Technology and Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation resource and energy consumption and can lead to inconsistent outcomes. To address this issue, we propose a novel approach utilizing a Mixture of Experts (MoE) framework, augmented with Large Language Models (LLMs), to analyze user objectives and constraints effectively, select specialized DRL experts, and weigh each decision from the participating experts. Specifically, we develop a gate network to oversee the expert models, allowing a collective of experts to tackle a wide array of new tasks. Furthermore, we innovatively substitute the traditional gate network with an LLM, leveraging its advanced reasoning capabilities to manage expert model selection for joint decisions. Our proposed method reduces the need to train new DRL models for each unique optimization problem, decreasing energy consumption and AI model implementation costs. The LLMenabled MoE approach is validated through a general maze navigation task and a specific network service provider utility maximization task, demonstrating its effectiveness and practical applicability in optimizing complex networking systems.

关键词： Wireless communication Energy consumption Navigation Computational modeling Logic gates Probability Throughput

来源：评论

学校读者我要写书评

暂无评论

Learn from Noise: Detecting Deepfakes via Regional Noise Consistency

Learn from Noise: Detecting Deepfakes via Regional Noise Con...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Weiming Bai Yufan Liu Zhipeng Zhang Xinyi Zhang Bo Wang Chengwei Peng Weiming Hu Bing Li State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences DiDiChuxing National Computer Network Emergency Response Technical Team/Coordination Center of China School of Information Science and Technology ShanghaiTech University Shanghai PeopleAI Inc.

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Face forgery detection becomes increasingly crucial due to the serious security issues caused by face manipulation techniques. Various methods primarily concentrate on the features specific to certain generation techniques, potentially resulting in overfitting to the distinctive fingerprint characteristics of those manipulation techniques, thus undermining their generalizability. In contrast, our investigation reveals a prevalent phenomenon wherein regional noise consistency is disrupted during the integration of synthesized faces into source images, regardless of specific manipulation techniques. Motivated by this observation, we introduce the Regional Noise Consistency Learning Framework (RNCL), a novel approach designed to discern manipulated faces. Central to RNCL are two pivotal modules: Noise Consistency Enhancement (NCE) and Pyramidal Noise Consistency Learning (PNCL). The NCE module facilitates channel-wise and spatial-wise feature enhancement by exploiting noise inconsistencies between facial and non-facial regions. Complementarily, the PNCL module constructs a noise consistency pyramid to analyze enhanced features across multiple scales, enabling adaptive multi-scale feature integration. Leveraging the NCE and PNCL modules, our framework effectively transforms noise information into useful forgery cues, significantly enhancing forgery detection performance. Experimental results demonstrate that our method achieves state-of-the-art performance on standard benchmarks. The code will be publicly available.

关键词： Visualization Noise Neural networks Transforms Fingerprint recognition Excavation Forgery

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：