检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

1,926 篇 会议
236 册 图书
24 篇 期刊文献

馆藏范围

2,185 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

1,605 篇 工学
- 1,333 篇 计算机科学与技术...
- 466 篇 软件工程
- 250 篇 电气工程
- 235 篇 机械工程
- 186 篇 光学工程
- 178 篇 信息与通信工程
- 119 篇 控制科学与工程
- 96 篇 生物工程
- 62 篇 生物医学工程（可授...
- 41 篇 仪器科学与技术
- 38 篇 电子科学与技术（可...
- 30 篇 化学工程与技术
- 21 篇 安全科学与工程
- 18 篇 材料科学与工程（可...
- 15 篇 交通运输工程
- 13 篇 建筑学
444 篇 理学
- 257 篇 物理学
- 202 篇 数学
- 110 篇 生物学
- 57 篇 统计学（可授理学、...
- 22 篇 化学
228 篇 医学
- 200 篇 临床医学
- 26 篇 基础医学(可授医学...
- 22 篇 特种医学
137 篇 管理学
- 83 篇 图书情报与档案管...
- 60 篇 管理科学与工程(可...
- 19 篇 工商管理
27 篇 艺术学
- 27 篇 设计学（可授艺术学...
16 篇 农学
- 15 篇 作物学
15 篇 法学
- 13 篇 社会学
9 篇 教育学
7 篇 经济学
5 篇 文学
5 篇 军事学

主题

320 篇 computer vision
286 篇 pattern recognit...
166 篇 artificial intel...
119 篇 feature extracti...
117 篇 computer imaging...
101 篇 image processing...
82 篇 face recognition
68 篇 training
61 篇 object detection
60 篇 image segmentati...
57 篇 computer applica...
54 篇 deep learning
51 篇 robustness
47 篇 computer graphic...
46 篇 cameras
45 篇 visualization
43 篇 semantics
38 篇 object recogniti...
37 篇 shape
36 篇 information syst...

机构

89 篇 univ chinese aca...
67 篇 chinese acad sci...
59 篇 national laborat...
56 篇 chinese acad sci...
50 篇 univ chinese aca...
36 篇 chinese univ hon...
36 篇 university of ch...
31 篇 institute of aut...
27 篇 chinese acad sci...
25 篇 school of artifi...
23 篇 univ sci & techn...
22 篇 chinese academy ...
18 篇 chinese acad sci...
17 篇 chinese univ hon...
16 篇 chinese acad sci...
16 篇 univ chinese aca...
15 篇 national laborat...
15 篇 computer vision ...
14 篇 tsinghua univers...
14 篇 department of in...

作者

32 篇 wang xiaogang
29 篇 lu hanqing
28 篇 tan tieniu
28 篇 wang jinqiao
23 篇 li stan z.
22 篇 pal umapada
21 篇 huang kaiqi
21 篇 lei zhen
21 篇 qiao yu
19 篇 tieniu tan
19 篇 hu weiming
18 篇 tang xiaoou
17 篇 xilin chen
15 篇 wang liang
15 篇 chen xilin
15 篇 cheng jian
14 篇 liu jing
14 篇 tang ming
13 篇 xiaoou tang
13 篇 shiguang shan

语言

2,165 篇 英文
19 篇 中文
7 篇 其他
1 篇 土耳其文

检索条件"任意字段=7th Chinese Conference on Pattern Recognition and Computer Vision"

共 2186 条记录，以下是81-90 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Cascade Large Language Model via In-Context Learning for Depression Detection on chinese Social Media 7th

Cascade Large Language Model via In-Context Learning for Dep...

引用

7th chinese conference on pattern recognition and computer vision

作者： Zheng, Tong Guo, Yanrong Hong, Richang Anhui Univ AHU IAI AI Joint Lab Hefei 230601 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Peoples R China

ISBN: (纸本)9789819784868;9789819784875

Depression is a common mental illness in modern society. However, incorrect and missed diagnoses of this illness still widely exist, making timely and accurate depression detection based on artificial intelligence (AI) technology urgent. Despite recent progress in depression detection, current models often struggle to analyze large amounts of data containing ambiguous content. the emergence of large language models (LLMs) sheds light on solving this issue. this paper proposes a Cascade Large Language Model via In-Context Learning (CLLM-ICL) for detecting depression. CLLM-ICL is a cascade model that includes an in-context learning prompt for GPT-3.5-Turbo-1106 and a small-sized linear neural network, subtly combining the strengths of the small-sized model and LLM in our application. We conduct experiments on the Weibo user depression detection dataset (WU3D) to evaluate the effectiveness of our model. the results show that our cascade model achieves better accuracy and F1-score than other recently proposed models.

关键词： Cascade model Depression detection Large language model In-context learning Online social network

来源：评论

学校读者我要写书评

暂无评论

Empathizing Before Generation: A Double-Layered Framework for Emotional Support LLM 7th

Empathizing Before Generation: A Double-Layered Framework fo...

引用

7th chinese conference on pattern recognition and computer vision

作者： Zhu, Jiahao Jiang, Zijian Zhou, Boyu Su, Jionglong Zhang, Naming Li, Zhihao Shantou Univ Shantou Peoples R China Chinese Univ Hong Kong Ma Liu Shui Hong Kong Peoples R China Mind Win Heart Robot Co Ltd Shenzhen Peoples R China Xian Jiaotong Liverpool Univ Suzhou Peoples R China

ISBN: (纸本)9789819784899;9789819784905

Large Language Models (LLMs) have found extensive use across different applications due to its diverse capabilities and proficiency in executing instructions. In the case of chatbots, they are frequently required to show empathy when used in the context of emotional support. However, to date their performance is still not satisfactory due to the lack of deep understanding of user related issues. Hence, we introduce the Empathizing Before Generation (EBG), a two-step learning framework that allows LLMs to analyze the chain of thought (COT) prior to generating a response. this model also enables the inference of the 24 emotions conveyed in the user's text as well as facilitates the generation of empathetic, high-quality and appropriate responses. We create a COT version of the dataset for sentiment inference by utilizing a publicly accessible sentiment dialogue. this dataset is then used as support for the training of two layers of EBG. Experiments indicate that models integrated with the EBG outperform other models in demonstrating empathy, with 98.2% and 92.8% accuracy in emotional attributes and labels respectively. Additionally, there is a notable enhancement in the model's capacity to comprehend COT instructions, infer emotions, and generate answers that are more satisfactory than other models.

关键词： Large language model Emotional support Chain of thought

来源：评论

学校读者我要写书评

暂无评论

ST-SBV: Spatial-Temporal Self-Blended Videos for Deepfake Detection 7th

ST-SBV: Spatial-Temporal Self-Blended Videos for Deepfake De...

引用

7th chinese conference on pattern recognition and computer vision

作者： Guan, Weinan Wang, Wei Peng, Bo Dong, Jing Tan, Tieniu Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China NLPR CASIA Beijing Peoples R China

ISBN: (纸本)9789819786190;9789819786206

the generalization ability to unseen forgery data is a critical concern in deepfake detection tasks. In this paper, we propose a novel spatial-temporal generation method for synthesizing forgery data, called spatial-temporal self-blended video (ST-SBV). ST-SBV is specifically designed to train deepfake detectors in a self-supervised manner. We utilize various image augmentation techniques on a genuine video to create pseudo source and target videos. Subsequently, ST-SBV is generated by blending them using face masks, following the generation process of a deepfake video. the key idea behind ST-SBV is to imitate the spatial and temporal artifacts in deepfake videos that are inherent and generalizable across deepfake generation methods. this encourages detectors to learn more general and effective representations. Specially, compared to existing data synthesis methods, ST-SBV addresses the gap in investigating spatial-temporal inconsistencies in forgery videos. Extensive experiments demonstrate that our method significantly improves the generalization performance of deepfake detectors on unknown forgery data. In particular, on the challenging datasets DFDCP and DFDC, our method outperforms the baselines by 5.20% and 4.94%, respectively.

关键词： Deepfake detection Synthetic forgery data

来源：评论

学校读者我要写书评

暂无评论

Balancing Complementarity and Consistency via Delayed Activation in Incomplete Multi-view Clustering 7th

Balancing Complementarity and Consistency via Delayed Activa...

引用

7th chinese conference on pattern recognition and computer vision

作者： Li, Bo Xu, Zhiwei Yun, Jing Wang, Jiatai Inner Mongolia Univ Technol Coll Data Sci & Applicat Hohhot Peoples R China Haihe Lab ITAI Tianjin Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9789819787913;9789819787920

this paper study one challenging issue in incomplete multi-view clustering, where valuable complementary information from other views is always ignored. To be specific, we propose a framework that effectively balances Complementarity and Consistency information in Incomplete Multi-view Clustering (CoCo-IMC). Specifically, we design a dual network of delayed activation, which achieves a balance of complementarity and consistency among different views. the delayed activation could enriches the complementarity information that was ignored during consistency learning. then, we recover the incomplete information and enhance the consistency learning by minimizing the conditional entropy and maximizing the mutual information across different views. this could be the first theoretical attempt to incorporate delayed activation into incomplete data recovery and the balance of complementarity and consistency. We have proved the effectiveness of CoCo-IMC in extensive comparative experiments with 12 state-of-the-art baselines on four publicly available datasets.

关键词： Incomplete Multi-view Clustering Contrastive Learning Complementarity Consistency Balance

来源：评论

学校读者我要写书评

暂无评论

Concentrating Estimation Attention: Human Prior Constrained Methods for Robust Classification 7th

Concentrating Estimation Attention: Human Prior Constrained ...

引用

7th chinese conference on pattern recognition and computer vision

作者： Cao, Zhe Yang, Shuo Liu, Yumeng Xu, Xiaofeng Pei, Hongbin Huang, Yan Yu, Yushu Zhang, Ruiheng Beijing Inst Technol Beijing Peoples R China Univ Hong Kong Hongkong Peoples R China Chinese Acad Sci Inst Software Beijing Peoples R China Anhui Polytech Univ Hefei Peoples R China Xi An Jiao Tong Univ Xian Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China

ISBN: (纸本)9789819784981;9789819784998

In the realm of label noise learning, a potent strategy involves the application of noise transition matrices to foster robust learning processes. Most current research has gained significant success utilizing parameter estimation approaches to generate these matrices when facing instance-dependent noise. Nonetheless, a key drawback of this approach is the diffuse focus of the transition matrix, which can be indiscriminately distracting, thereby ignoring specific locations that are vulnerable to noise flipping. To address this gap, we introduce the Human Attention Constrained Estimation (HACE). this innovative method capitalizes on human cognitive precedents to derive an inter-class affinity matrix. It further refines the estimation of the noise transition matrix by employing our novel Matrix Structure Similarity (MSS) Loss, enabling the matrix estimation module to selectively concentrate on areas frequently affected by noisy flips. this targeted approach addresses the label noise conundrum more effectively and narrows the operational scope significantly. Experiments on three synthetic datasets and a real-world dataset corroborate the robustness and efficiency of our proposed method.

关键词： Robust learning Transition matrix estimation Human prior Focus attention Classification

来源：评论

学校读者我要写书评

暂无评论

Adapt and Refine: A Few-Shot Class-Incremental Learner via Pre-Trained Models 7th

Adapt and Refine: A Few-Shot Class-Incremental Learner via P...

引用

7th chinese conference on pattern recognition and computer vision

作者： Qiang, Sunyuan Xiong, Zhu Liang, Yanyan Wan, Jun Zhang, Du Macau Univ Sci & Technol Macau Peoples R China Chinese Acad Sci Inst Automat MAIS Beijing Peoples R China Univ Chinese Acad Sci SAI Beijing Peoples R China

ISBN: (纸本)9789819784868;9789819784875

the intricate and ever-changing nature of the real world imposes greater demands on neural networks, necessitating the rapid assimilation of fleeting new concepts as they arise. Consequently, a novel learning paradigm has emerged, namely, few-shot class-incremental learning (FSCIL), which aims to continuously update knowledge of novel categories with insufficient instances while avoiding catastrophic forgetting of previous knowledge. However, recent FSCIL methods encountered significant performance limitations due to the low-quality latent representation spaces obtained from base session. To this end, this paper introduces a novel FSCIL method, Adapt and REfine (ARE). Specifically, ARE initially strengthens the latent space through the powerful representational capabilities of pre-trained models (PTMs). Subsequently, we further adapt and refine the feature space and prototypes to promote the enhancement of FSCIL performance. Extensive experiments on benchmarks such as CIFAR100, mini-ImageNet, and CUB200 validate the effectiveness of the proposed method.

关键词： Few-shot class-incremental learning (FSCIL) center dot Catastrophic forgetting Pre-trained models (PTMs)

来源：评论

学校读者我要写书评

暂无评论

A Robust and Real-Time RGB-D SLAM Method with Dynamic Point recognition and Depth Segmentation Optimization 7th

A Robust and Real-Time RGB-D SLAM Method with Dynamic Point ...

引用

7th chinese conference on pattern recognition and computer vision

作者： Chen, Shuaixin Gan, Baolin Zhang, Congxuan Chen, Zhen Lu, Ke Lu, Feng Nanchang Hangkong Univ Nanchang 330063 Jiangxi Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9789819786916;9789819786923

Simultaneous localization and mapping (SLAM), as one of the core prerequisite technologies for intelligent mobile robots, has attracted much attention in recent years. However, the applicability of SLAM algorithms in practical scenarios is limited due to the strict assumptions of static environments. Although many recent SLAM systems have been devoted to introducing semantic segmentation or object detection schemes to identify dynamic regions, these methods fail to detect regions with unknown semantics and are highly time-consuming. To address the abovementioned problems, we propose a robust and real-time RGB-D SLAM method with dynamic point recognition and depth segmentation optimization. Specifically, we first plan a self-adaptive feature point tracking scheme based on sparse optical flows, which accelerates the feature point tracking process and avoids local optima. then, we design a dynamic feature point recognition model that uses motion information and spatial distribution patterns to distinguish between dynamic and static point clusters. Finally, we exploit a depth segmentation optimization scheme to recover the misclassified feature points, which further improves the SLAM performance of the model. the experimental results obtained from a comparison between our method and several state-of-the-art (SOTA) models demonstrate that the proposed method achieves the best performance among the geometry-based methods and performs competitively relative to the deep learning-based models, especially in highly dynamic environments.

关键词： SLAM dynamic environments optical flow spatial distribution pattern depth segmentation

来源：评论

学校读者我要写书评

暂无评论

DRC-NET: Density Reweighted Convolution Network for Edge Curve Extraction 7th

DRC-NET: Density Reweighted Convolution Network for Edge Cur...

引用

7th chinese conference on pattern recognition and computer vision

作者： Ning, Xiaojuan Shi, Qishuai Liu, Ynexuan Jin, Haiyan Wang, Yinghui Zhang, Xiaopeng Guo, Jianwei Xian Univ Technol Inst Comp Sci & Engn Xian Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi Jiangsu Peoples R China Chinese Acad Sci Inst Automat MAIS Beijing Peoples R China

ISBN: (纸本)9789819784899;9789819784905

Parametric edge curve extraction from 3D point clouds is a challenging task in computer vision due to the diverse, complex, sparse, and noisy characteristics of the data. In this paper, we propose a novel edge curve extraction network, named DRC-Net, which leverages Density Reweighted Convolution to address the challenge of incomplete edge curve structure encountered in prior research. Inspired by PIE-Net, we employ density-reweighted convolution instead of the conventional feature extraction module and deconvolution to supplant the feature propagation module, thereby preserving both translation and displacement invariance. Our DRC-Net is architected to simultaneously execute feature point classification and edge curve extraction, utilizing a standard encoder-decoder network structure. We evaluate the performance of DRC-Net on the ABC dataset, the largest publicly available dataset of CAD models, through robustness testing and comparison with EC-Net and PIE-Net. Experimental results demonstrate that the proposed method can effectively improve the accuracy in feature points classification and edge extraction while producing relatively complete and smooth edge curves.

关键词： D point cloud Edge curve extraction Feature point classification Density reweighted convolution

来源：评论

学校读者我要写书评

暂无评论

Spatio-temporal Perceiving Network Based vision Transformer for 6-Hour Precipitation Prediction Using Multi-meteorological Factors 7th

Spatio-temporal Perceiving Network Based Vision Transformer ...

引用

7th chinese conference on pattern recognition and computer vision

作者： Hu, Jing Zheng, Peng Zhang, Honghu Wu, Xi Chengdu Univ Informat Technol Chengdu 610225 Peoples R China

ISBN: (纸本)9789819784899;9789819784905

Precipitation is crucial for the future development of mankind. However, accurately predicting it remains a formidable challenge. Due to the low efficiency of traditional Numerical Weather Prediction (NWP), deep-learning based methods are highly preferred. However, most deep learning methods focus on predicting the spatio-temporal behavior of the single precipitation variable, often ignoring the interplay between various meteorological factors and precipitation. Furthermore, they tend to underestimate it. therefore, this paper proposes a new neural network model called Spatio-temporal Perceiving Network Based vision Transformer (ST-ViT), which integrates spatio-temporal and channel perception mechanisms to build the relationship between precipitation and meteorological elements. Additionally, an adaptive differential loss function is proposed to accurately capture precipitation intensity. We evaluated the ST-ViT on ERA5 from Southeast Asia for 6h prediction. the quantitative results demonstrate that our method achieved superior accuracy and lower errors compared to other deep learning methods. Specifically, it shows great potential to alleviate the situation of underestimated precipitation from the reconstructed predicted image.

关键词： Precipitation prediction Multiple meteorological factors Deep learning vision transformer

来源：评论

学校读者我要写书评

暂无评论

BIVL-Net: Bidirectional vision-Language Guidance for Visual Question Answering 7th

BIVL-Net: Bidirectional Vision-Language Guidance for Visual ...

引用

7th chinese conference on pattern recognition and computer vision

作者： Han, Cong Zhang, Feifei Tianjin Univ Technol Tianjin Peoples R China

ISBN: (纸本)9789819785018;9789819785025

Visual Question Answering models have been shown to suffer from language bias, where the model relies too much on surface correlations or specific language patterns between the questions and the answers when answering a question, and ignores image information. Previous works to reduce this bias have used question-only models or data augmentation, but they have overlooked the effective utilization of correlated information between text and vision. In this paper, we propose a solution to address these challenges by introducing a novel Bidirectional vision-Language Guidance approach called BIVL-Net. BIVL-Net redirects the focus of the two modalities by introducing image categories as additional information and treating them as inter-modal association objects. Our proposed framework comprises two key components: the Textual Information Enhancement Module (TE) and the Question-Guided Visual Enhancement Module (VE). In the TE module, we adjust the model's focus on textual features to mitigate biases introduced by the textual modality. VE module optimizes the final image feature representations by using the objects obtained from the text side as prompts to guide the visual side to focus on the objects related to the questions. Finally, to improve the model's discrimination of question types, we propose a relational optimization loss lrl that is effective for both in-distribution(ID) and out-of-distribution (OOD) data. We demonstrate the effectiveness of the proposed method on the OOD dataset VQA-CP v2 and the ID dataset VQA v2. In addition, this paper is also the most robust in terms of differences between ID and OOD data.

关键词： Visual Question Answering Language Bias Bidirectional Guidance

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共219页 << < 5 6 7 8 9 10 11 12 13 14 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：