检索结果-内蒙古大学图书馆

A Clustering-Based Color Reordering Method for Reversible Data Hiding in Palette Images

IET Image Processing 2025年第1期19卷

作者： Deng, Jianxuan Chen, Yi Wang, Hongxia Guo, Chun Cui, Yunhe Shen, Guowei State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guiyang China School of Cyber Science and Engineering Sichuan University Chengdu China

A recent research work pointed out that the reversible data hiding algorithms proposed for gray-scale images can be implemented on the reconstructed palette images to improve embedding capacity and visual quality by reordering the color table. However, the reordering effect has a significant impact on performance improvement. Therefore, we propose a clustering-based color reordering method for reversible data hiding in palette images to improve the reordering effect and further enhance the performance. In this method, we first design a centroid initialization method to select the initial centroids and then exploit the K-means algorithm to generate (Formula presented.) clusters for the colors in the original color table. In the following, our proposed method, respectively, reorders the colors of these clusters by a greedy strategy and concatenates them into the reordered color table. Based on the relationship between the original and the reordered color tables, a novel index matrix can be reconstructed. Finally, state-of-the-art reversible data hiding algorithms can be implemented on the reconstructed index matrix for performance improvement. Since our proposed method improves the reordering effect, enhances the correlation of the reconstructed index matrix, and reduces the length of the encoded location map, the maximal embedding capacities and the visual quality under the fixed embedding capacities are improved. We conducted experiments on two image datasets and six standard images to verify that the performance improvement of our proposed reordering method is better than that of the state-of-the-art methods. © 2025 The Author(s). IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Simpseudo: Enhancing Whole Slide Imaging Classification with Similarity and Prototype-Based Example-Level Pseudo-Supervision

SSRN

引用

SSRN 2025年

作者： Yu, Hong Diao, Yujie Liu, Xueyang Wang, Hexin Zhu, Jialin Gao, Jie Tianjin Central Hospital of Gynecology Obstetrics Tianjin300100 China College of Intelligence and Computing Tianjin University Tianjin300350 China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin300350 China Tianjin Key Laboratory of Advanced Networking Tianjin300350 China Department of Diagnostic and Therapeutic Ultrasonography Tianjin Medical University Cancer Institute and Hospital Tianjin300060 China

Whole Slide Imaging (WSI) analysis, pivotal in digital pathology, faces significant challenges due to its ultra-high resolution (often exceeding 10k * 10k pixels) and the scarcity of pixel-level annotations. While Multiple Instance Learning (MIL) mitigates these issues by leveraging image-level labels, existing methods relying on Top-K strategies for pseudo-label generation only annotate a subset of high-confidence instances, leaving the majority without supervision and limiting classifier optimization. To address this critical gap, we propose SimPseudo, a novel similarity comparison-based instance pseudo-label generator that adaptively assigns high-quality soft pseudo-labels to all instances in WSI bags. Our framework integrates three key innovations: (1) A Top-N1 similarity strategy that combines MIL classifier confidence with pre-trained negative prototypes to assign soft pseudo-labels to low-confidence instances through feature similarity comparisons;(2) A negative example prototype generation mechanism that generates pseudo-labels for negative instances, further enhancing the model's ability to handle diverse example types;(3) A pseudo-label correction module that refines soft labels via classifier-derived probabilities, enabling error suppression and one-hot encoding. SimPseudo is designed to be model-agnostic, making it compatible with diverse MIL classifiers. Extensive experiments on Camelyon16 and Camelyon17 demonstrate that SimPseudo brings consistent improvements across various evaluation metrics, outperforming existing Top-K baselines. The framework provides precise instance-level localization of pathological regions and establishes a new paradigm for comprehensive instance supervision in WSI analysis without requiring additional annotations. © 2025, The Authors. All rights reserved.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Replace2Self: Self-Supervised Denoising based on Voxel Replacing and Image Mixing for Diffusion MRI

引用

IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Wu, Linhai Wang, Lihui Deng, Zeyu Zhu, Yuemin Wei, Hongjiang Guizhou University Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology 550025 China Univ Lyon INSA Lyon CNRS Inserm CREATIS UMR 5220 LyonF-69621 France Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200030 China

Low signal to noise ratio (SNR) remains one of the limitations of diffusion weighted (DW) imaging. How to suppress the influence of noise on the subsequent analysis about the tissue microstructure is still challenging. This work proposed a novel self-supervised learning model, Replace2Self, to effectively reduce spatial correlated noise in DW images. Specifically, a voxel replacement strategy based on similar block matching in Q-space was proposed to destroy the correlations of noise in DW image along one diffusion gradient direction. To alleviate the signal gap caused by the voxel replacement, an image mixing strategy based on complementary mask was designed to generate two different noisy DW images. After that, these two noisy DW images were taken as input, and the non-correlated noisy DWimage after voxel replacement was taken as learning target, a denoising network was trained for denoising. To promote the denoising performance, a complementary mask mixing consistency loss and an inverse replacement regularization loss were also proposed. Through the comparisons against several existing DW image denoising methods on extensive simulation data with different noise distributions, noise levels and b-values, as well as the acquisition datasets and the ablation experiments, we verified the effectiveness of the proposed method. Regardless of the noise distribution and noise level, the proposed method achieved the highest PSNR, which was at least 1.9% higher than the suboptimal method when the noise level reaches 10%. Furthermore, our method has superior generalization ability due to the use of the proposed strategies. © 2025 IEEE. All rights reserved.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

CroPrompt: Cross-task Interactive Prompting for Zero-shot Sp...

引用

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

作者： Libo Qin Fuxuan Wei Qiguang Chen Jingxuan Zhou Shijue Huang Jiasheng Si Wenpeng Lu Wanxiang Che School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences)

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU.

关键词： Large language models Signal processing Benchmark testing Multitasking Filling Acoustics Speech processing Information exchange Standards

来源：评论

学校读者我要写书评

暂无评论

MVideoRec: Micro Video Recommendations through Modality Decomposition and Contrastive Learning

引用

ACM Transactions on Information Systems 2025年第3期43卷 1-27页

作者： Li Yu Jianyong Hu Qihan Du Xi Niu School of Information Renmin University of China Beijing China and Suzhou Key Laboratory of Artificial Intelligence and Social Governance Technology Suzhou China School of Information Renmin University of China Beijing China College of Computing and Informatics The University of North Carolina at Charlotte Charlotte NC USA

Personalized micro video recommendation aims to recommend the micro videos tailored to user preference based on the user’s interaction history with the micro videos, which has drawn increasing attention from both the academic and industrial communities. Existing solutions primarily concentrate on video-level interactions between users and micro videos to model their preferences, and cannot distinguish the finer-grained users’ interactions with various modalities. Ignoring modality-level interactions prevents the full understanding of the user’s true and subtle preferences on micro videos. To this end, in this article, we propose a Contrastive Multimodal Interaction Graph Learning (MVideoRec) model to automatically and explicitly learn the modality-level interaction between users and micro videos for recommendations. Specifically, we designed a graph structure learning module with a sparsification strategy to infer modality-level interaction graph, which will be dynamically and iteratively updated based on the node representations obtained from the node representation learning module. Furthermore, to address the lack of ground truth labels, we propose to generate teacher view from video-level interaction graph and student view from modality-level interaction graph, as well as construct intra-modality and inter-modality contrastive pairwise instances to provide self-supervised signals. Extensive experiments on three real-world micro video datasets validate the effectiveness of MVideoRec.

关键词： micro video recommendations multiple modalities graph neural networks (GNNs) contrastive learning

来源：评论

学校读者我要写书评

暂无评论

A Fusion Tuning Method for Named Entity Recognition 12th

A Fusion Tuning Method for Named Entity Recognition

引用

12th CCF Conference on BigData, BigData 2024

作者： Wang, Jitian Chen, Yanping Zou, Anqi Qin, Yongbin Huang, Ruizhang Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang China College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (纸本)9789819610235

In named entity recognition, the main methods for constructing deep neural networks are fine-tuning and prompt tuning. Fine-tuning is a commonly used paradigm to optimize neural networks by using task-specific objective functions to make them more adapted to solve downstream tasks. In which pre-trained language models are used as external resources to provide contextual representations. Prompt tuning is a recent paradigm in which the downstream tasks are reformulated to look more like those solved during the original PLM training with the help of a textual prompt. It is effective to utilize potential knowledge of PLMs. In this paper, we propose the fusion approach, which sufficiently fuses the semantics obtained by extracting fine-tuning and prompt-tuning. In our approach, the model makes decisions based on the information obtained from the reconstructive fusion of the fine-tuned semantics and the semantics of the pre-trained language model. The representation obtained by fine-tuning and the representation obtained by prompt tuning are reconstructed into a multidimensional semantics using a gate fusion module (GFM). In particularly, our model has two gate components, one gate to extract the semantics of fine-tuning and the other gate to extract the semantics of prompt tuning, and finally the reconstructed semantic representation is obtained. This takes full advantage of the potential knowledge of PLM. Our model is evaluated on the CONLL2003, ACE2005 and GENIA corpora. It achieves performance close to the state-of-the-art in F1 scores. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

MHCD: Multi-Hierarchy Interactive Constraint-Aware cognitive Diagnosis Framework

引用

Expert Systems with Applications 2025年 283卷

作者： Sun, Xinjie Zhang, Kai Shen, Shuanghong Wang, Fei Gong, Weiyin Yang, Lina Ma, Yuling Huo, Xingying Liu, Qi School of Computer Science Liupanshui Normal University Liupanshui China School of Computer Science and Technology University of Science and Technology of China Hefei China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei China State Key Laboratory of Cognitive Intelligence Hefei China School of Computing National University of Singapore Singapore Singapore Shandong Jianzhu University Jinan China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

cognitive Diagnosis (CD) is a fundamental aspect of educational data mining that focuses on assessing students’ mastery of specific knowledge domains by analyzing their response patterns. However, traditional CD frameworks often fail to reasonably model students’ true knowledge state due to insufficient constraints for automatic inference, particularly overlooking the hierarchical relationships among explicit behavioral records. This limitation undermines both the rationality and interpretability of diagnostic outcomes. To address these challenges, we propose an innovative Multi-Hierarchy Interactive Constraint-aware cognitive Diagnosis (MHCD) framework. This framework first identifies and constructs the hierarchical relationships of explicit records in real-world educational contexts. It then delves into feature interactions across multiple hierarchies and achieves more accurate and reasonable knowledge state modeling through an iterative constraint-aware strategy. Finally, it integrates multi-hierarchy interaction-aware features with personalized features, significantly enhancing the expressive power of both individual and hierarchical attributes. Experimental results demonstrate that MHCD achieves notable improvements in diagnostic accuracy, rationality, and interpretability, while seamlessly integrating into existing cognitive diagnosis frameworks to further enhance their overall performance. © 2025

关键词： Text mining

来源：评论

学校读者我要写书评

暂无评论

SSAM: a span spatial attention model for recognizing named entities

引用

Scientific Reports 2025年第1期15卷 1-13页

作者： Wang, Kai Wen, Kunjian Chen, Yanping Qin, Yongbin Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China ByteDance Ltd. Beijing China

Mapping a sentence into a two-dimensional (2D) representation can flatten nested semantic structures and build multi-granular span dependencies in named entity recognition. Existing approaches to recognizing named entities often classify each entity span independently, which ignores the spatial structures between neighboring spans. To address this issue, we propose a Span Spatial Attention Model (SSAM) that consists of a token encoder, a span generation module, and a 2D spatial attention network. The SSAM employs a two-channel span generation strategy to capture multi-granular features. Unlike traditional attention implemented on a sequential sentence representation, spatial attention is applied to a 2D sentence representation, enabling the model to learn the spatial structures of the sentence. This allows the SSAM to adaptively encode important features and suppress non-essential information in the 2D sentence representation. Experimental results on the GENIA, ACE2005, and ACE2004 datasets demonstrate that our proposed model achieves state-of-the-art performance, with F1-scores of 81.82%, 89.04%, and 89.24%, respectively. The code is available at https://***/Gzuwkj/SpatialAttentionForNer. © The Author(s) 2025.

关键词： Named entity recognition Natural language processing Spatial attention

来源：评论

学校读者我要写书评

暂无评论

Neural Rendering and Flow-assisted Unsupervised Multi-view Stereo for Real-time Monocular Tracking and Scene Perception

引用

IEEE Transactions on Automation Science and Engineering 2025年

作者： Tong, Kevin W. Cai, Yandong Jie, Yu-Wen Duan, Ya Hou, Yuhong Wu, Edmond Q. Nanjing University of Posts and Telecommunications College of Automation College of Artificial Intelligence Nanjing210023 China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Dalian116024 China Shanxi University School of Automation and Software Engineering Shanxi 237016 China Flight Test Technology and Engineering Center of the Chinese Flight Test Establishment Xian710089 China China Civil Aviation Shanghai Aircraft Airworthiness Certification Center Shanghai200335 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China

The existing camera tracking and perception methods mainly rely on sparse SLAM, which limits the dense perception ability of the scene and affects the reliability of auxiliary decision-making. Different from this, this work proposes a real-time tracking and unsupervised dense sensing framework. Firstly, the dense depth value of the scene is predicted by unsupervised multi-view stereo to remove the dependence on labeled data. Then, the quality of synthetic pseudo-reference image is quantified according to the predicted depth map and used as a weighted guidance to train the unsupervised model, thus reducing the ambiguity of feature matching in areas such as specular reflection. Moreover, the sparse optical flow of the keyframes is solved by real-time and robust ORB feature matching operator, which assists the high-precision training of unsupervised depth inference model. To increase the prediction accuracy of occluded area, a novel rendering consistency loss via neural radiance fields is designed to constrain the geometric characteristics of object surface. Finally, dense direct image alignment is performed from a global model to improve the tracking robustness, which is incrementally constructed from dense depth prediction. Extensive experiments on synthetic datasets and real datasets validate the effectiveness and practicability of the proposed work, which is an effective supplement to the existing SLAM work. © 2025 IEEE.

关键词： Stereo image processing

来源：评论

学校读者我要写书评

暂无评论

Modeling Fine-Grained Relations in Dynamic Space-Time Graphs for Video-Based Facial Expression Recognition

引用

IEEE Transactions on Affective computing 2025年

作者： Huang, Changqin Jiang, Fan Han, Zhongmei Huang, Xiaodi Wang, Shijin Zhu, Yanlai Jiang, Yunliang Hu, Bin Zhejiang Normal University Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Jinhua321004 China Charles Sturt University School of Computing Mathematics and Engineering AlburyNSW2640 Australia State Key Laboratory of Cognitive Intelligence Anhui Hefei230088 China Hangzhou Hikvision Digital Technology Co. Ltd Education Business Department Hangzhou310051 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China Beijing Institute of Technology School of Medical Technology Beijing10081 China

Facial expressions in videos inherently mirror the dynamic nature of real-world facial events. Consequently, facial expression recognition (FER) should employ a dynamic graph-based representation to effectively capture the relational structure of facial expressions rather than relying on conventional grid or sequence methods. However, existing graph-based approaches have their limitations. Frame-level graph methods provide a coarse representation of the facial graph across time and space, while landmark-based graph methods need to introduce additional facial landmarks, resulting in a static graph structure. To address these challenges, we propose spatial-temporal relation-aware dynamic graph convolutional networks (ST-RDGCN). This fine-grained relation modeling approach enables the dynamic modeling of evolving facial expressions in videos through dynamic space-time graphs, eliminating the need for facial landmarks. ST-RDGCN encompasses three graph construction paradigms: dynamic independent space graph, dynamic joint space-time graph, and dynamic cross space-time graph. Furthermore, we propose a relation-aware space-time graph convolution (RSTG-Conv) operator to learn informative spatiotemporal correlations in dynamic space-time graphs. In extensive experimental evaluations, our ST-RDGCN demonstrates state-of-the-art performance on the five popular video-based FER datasets, achieving overall accuracy scores of 99.69%, 91.67%, 56.51%, 69.37%, and 49.03% on the CK+, Oulu-CASIA, AFEW, DFEW, and FERV39 k datasets, respectively. In particular, our ST-RDGCN outperforms the current best method by 3.6% in UAR on the most challenging FERV39 k dataset. Furthermore, our analysis reveals that the dynamic cross space-time graph scheme is the most effective among the three dynamic graph construction schemes. © 2010-2012 IEEE.

关键词： Graphic methods

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：