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

632 篇 会议
597 篇 期刊文献
2 册 图书

馆藏范围

1,231 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

851 篇 工学
- 630 篇 计算机科学与技术...
- 543 篇 软件工程
- 209 篇 信息与通信工程
- 139 篇 生物工程
- 116 篇 控制科学与工程
- 113 篇 光学工程
- 86 篇 生物医学工程（可授...
- 70 篇 电气工程
- 58 篇 电子科学与技术（可...
- 51 篇 化学工程与技术
- 48 篇 机械工程
- 27 篇 仪器科学与技术
- 25 篇 安全科学与工程
- 22 篇 交通运输工程
- 22 篇 网络空间安全
- 20 篇 土木工程
481 篇 理学
- 234 篇 数学
- 155 篇 生物学
- 143 篇 物理学
- 74 篇 统计学（可授理学、...
- 53 篇 化学
- 27 篇 系统科学
261 篇 管理学
- 162 篇 图书情报与档案管...
- 110 篇 管理科学与工程(可...
- 42 篇 工商管理
72 篇 医学
- 58 篇 临床医学
- 45 篇 基础医学(可授医学...
- 28 篇 公共卫生与预防医...
- 23 篇 药学(可授医学、理...
33 篇 法学
- 33 篇 社会学
13 篇 经济学
10 篇 农学
6 篇 教育学
3 篇 艺术学
2 篇 军事学
1 篇 文学

主题

64 篇 semantics
47 篇 training
46 篇 feature extracti...
30 篇 object detection
27 篇 convolution
24 篇 visualization
23 篇 deep learning
22 篇 machine learning
22 篇 computer vision
20 篇 deep neural netw...
20 篇 task analysis
19 篇 image segmentati...
19 篇 computational mo...
18 篇 signal processin...
16 篇 predictive model...
16 篇 accuracy
15 篇 contrastive lear...
15 篇 benchmarking
14 篇 robustness
13 篇 pattern recognit...

机构

155 篇 shanghai key lab...
110 篇 school of comput...
93 篇 shanghai key lab...
78 篇 shanghai key lab...
54 篇 school of comput...
48 篇 academy for engi...
45 篇 school of comput...
44 篇 institute of sci...
35 篇 shanghai key lab...
31 篇 shanghai key lab...
27 篇 shanghai center ...
22 篇 fudan university
22 篇 school of data s...
22 篇 shanghai enginee...
21 篇 university of ch...
21 篇 school of comput...
20 篇 shanghai key lab...
18 篇 department of co...
18 篇 school of inform...
17 篇 shanghai collabo...

作者

54 篇 feng rui
52 篇 jiang yu-gang
51 篇 zhang wenqiang
49 篇 zhang junping
41 篇 fu yanwei
41 篇 li wei
41 篇 zhang yuejie
38 篇 zhou shuigeng
38 篇 shuigeng zhou
37 篇 xue xiangyang
37 篇 shan hongming
32 篇 huang xuanjing
29 篇 rui feng
28 篇 junping zhang
28 篇 jihong guan
24 篇 hongming shan
24 篇 qiu xipeng
22 篇 chen jingjing
22 篇 wu zuxuan
21 篇 zhang tao

语言

1,116 篇 英文
91 篇 其他
28 篇 中文

检索条件"机构=Shanghai Key Lab of Intelligent Information Processing School of Computer Science and Technology"

共 1231 条记录，以下是21-30 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：

DeTrack: In-model Latent Denoising Learning for Visual Object Tracking 38

DeTrack: In-model Latent Denoising Learning for Visual Objec...

引用

38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Zhou, Xinyu Li, Jinglun Hong, Lingyi Jiang, Kaixun Guo, Pinxue Ge, Weifeng Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering and Technology Fudan University Shanghai China

Previous visual object tracking methods employ image-feature regression models or coordinate autoregression models for bounding box prediction. Image-feature regression methods heavily depend on matching results and do not utilize positional prior, while the autoregressive approach can only be trained using bounding boxes available in the training set, potentially resulting in suboptimal performance during testing with unseen data. Inspired by the diffusion model, denoising learning enhances the model's robustness to unseen data. Therefore, We introduce noise to bounding boxes, generating noisy boxes for training, thus enhancing model robustness on testing data. We propose a new paradigm to formulate the visual object tracking problem as a denoising learning process. However, tracking algorithms are usually asked to run in real-time, directly applying the diffusion model to object tracking would severely impair tracking speed. Therefore, we decompose the denoising learning process into every denoising block within a model, not by running the model multiple times, and thus we summarize the proposed paradigm as an in-model latent denoising learning process. Specifically, we propose a denoising Vision Transformer (ViT), which is composed of multiple denoising blocks. In the denoising block, template and search embeddings are projected into every denoising block as conditions. A denoising block is responsible for removing the noise in a predicted bounding box, and multiple stacked denoising blocks cooperate to accomplish the whole denoising process. Subsequently, we utilize image features and trajectory information to refine the denoised bounding box. Besides, we also utilize trajectory memory and visual memory to improve tracking stability. Experimental results validate the effectiveness of our approach, achieving competitive performance on several challenging datasets. The proposed in-model latent denoising tracker achieve real-time speed, rendering denoising learning

关键词：

来源：评论

学校读者我要写书评

暂无评论

Inference-Time Decontamination: Reusing Leaked Benchmarks for Large Language Model Evaluation

Inference-Time Decontamination: Reusing Leaked Benchmarks fo...

引用

2024 Conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： Zhu, Qin Cheng, Qinyuan Peng, Runyu Li, Xiaonan Liu, Tengxiao Peng, Ru Qiu, Xipeng Huang, Xuanjing School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China College of Computer Science and Technology Zhejiang University China

ISBN: (纸本)9798891761681

The training process of large language models (LLMs) often involves varying degrees of test data contamination (Yang et al., 2023b).Although current LLMs are achieving increasingly better performance on various benchmarks, their performance in practical applications does not always match their benchmark *** of benchmarks can prevent the accurate assessment of LLMs' true ***, constructing new benchmarks is costly, labor-intensive and still carries the risk of ***, in this paper, we ask the question "Can we reuse these leaked benchmarks for LLM evaluation?" We propose Inference-Time Decontamination (ITD) to address this issue by detecting and rewriting leaked samples without altering their *** can mitigate performance inflation caused by memorizing leaked *** proof-of-concept experiments demonstrate that ITD reduces inflated accuracy by 22.9% on GSM8K and 19.0% on *** MMLU, using Inference-time Decontamination can lead to a decrease in the results of Phi3 and Mistral by 6.7% and 3.6% *** hope that ITD can provide more truthful evaluation results for large language models. © 2024 Association for Computational Linguistics.

关键词： Decontamination

来源：评论

学校读者我要写书评

暂无评论

Gaitcotr: Improved Spatial-Temporal Representation for Gait Recognition with a Hybrid Convolution-Transformer Framework 48

Gaitcotr: Improved Spatial-Temporal Representation for Gait ...

引用

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

作者： Li, Jingqi Zhang, Yuzhen Shan, Hongming Zhang, Junping School of Computer Science Shanghai Key Lab of Intelligent Information Processing China Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Shanghai200433 China

ISBN: (纸本)9781728163277

This work presents a novel hybrid convolution-transformer framework for gait recognition, termed GaitCoTr. The developed framework captures the appearance and short-term temporal features by convolution and extracts the long-term temporal features by transformer architecture, achieving a comprehensive spatial-temporal representation of gait. To unleash the potential of this hybrid framework for extracting richness and generalized temporal features, we propose a new variant of transformer tailored for gait, including temporally shifted tokenization, length-flexible position embedding, and inter-frame encoder. In addition, we introduce an auxiliary task - view label prediction - aiming to disentangle view from ID information. Extensive experimental results on two well-known gait benchmark datasets, CASIA-B and GREW, demonstrate the superior performance of the proposed Gait-CoTr. © 2023 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Motion Matters: A Novel Motion Modeling for Cross-View Gait Feature Learning 48

Motion Matters: A Novel Motion Modeling for Cross-View Gait ...

引用

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

作者： Li, Jingqi Gao, Jiaqi Zhang, Yuzhen Shan, Hongming Zhang, Junping School of Computer Science Shanghai Key Lab of Intelligent Information Processing China Institute of Science and Technology for Brain-inspired Intelligence Fudan University Shanghai200433 China

ISBN: (纸本)9781728163277

As a unique biometric that can be perceived at a distance, gait has broad applications in person authentication, social security and so on. Existing gait recognition methods suffer from changes in viewpoint and clothing and barely consider extracting diverse motion features, a fundamental characteristic in gaits, from gait sequences. This paper proposes a novel motion modeling method to extract the discriminative and robust representation. Specifically, we first extract the motion features from the encoded motion sequences in the shallow layer. Then we continuously enhance the motion feature in deep layers. This motion modeling approach is independent of mainstream work in building network architectures. As a result, one can apply this motion modeling method to any backbone to improve gait recognition performance. In this paper, we combine motion modeling with one commonly used backbone (GaitGL) as GaitGL-M to illustrate motion modeling. Extensive experimental results on two commonly-used crossview gait datasets demonstrate the superior performance of GaitGL-M over existing state-of-the-art methods. © 2023 IEEE.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

Cross-Head Supervision for Crowd Counting with Noisy Annotations 48

Cross-Head Supervision for Crowd Counting with Noisy Annotat...

引用

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

作者： Dai, Mingliang Huang, Zhizhong Gao, Jiaqi Shan, Hongming Zhang, Junping School of Computer Science Shanghai Key Lab of Intelligent Information Processing China Fudan University Institute of Science and Technology for Brain-inspired Intelligence Shanghai200433 China

ISBN: (纸本)9781728163277

Noisy annotations such as missing annotations and location shifts often exist in crowd counting datasets due to multi-scale head sizes, high occlusion, etc. These noisy annotations severely affect the model training, especially for density map-based methods. To alleviate the negative impact of noisy annotations, we propose a novel crowd counting model with one convolution head and one transformer head, in which these two heads can supervise each other in noisy areas, called Cross-Head Supervision. The resultant model, CHS-Net, can synergize different types of inductive biases for better counting. In addition, we develop a progressive cross-head supervision learning strategy to stabilize the training process and provide more reliable supervision. Extensive experimental results on Shang-haiTech and QNRF datasets demonstrate superior performance over state-of-the-art methods. Code is available at https://***/RaccoonDML/CHSNet. © 2023 IEEE.

关键词： Crowd counting noisy annotations

来源：评论

学校读者我要写书评

暂无评论

Mixture of Experts for Audio-Visual Learning 38

Mixture of Experts for Audio-Visual Learning

引用

38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Cheng, Ying Li, Yang He, Junjie Feng, Rui School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China

With the rapid development of multimedia technology, audio-visual learning has emerged as a promising research topic within the field of multimodal analysis. In this paper, we explore parameter-efficient transfer learning for audio-visual learning and propose the Audio-Visual Mixture of Experts (AVMoE) to inject adapters into pre-trained models flexibly. Specifically, we introduce unimodal and cross-modal adapters as multiple experts to specialize in intra-modal and inter-modal information, respectively, and employ a lightweight router to dynamically allocate the weights of each expert according to the specific demands of each task. Extensive experiments demonstrate that our proposed approach AVMoE achieves superior performance across multiple audio-visual tasks, including AVE, AVVP, AVS, and AVQA. Furthermore, visual-only experimental results also indicate that our approach can tackle challenging scenes where modality information is missing. The source code is available at https://***/yingchengy/AVMOE. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SPIKING CONVOLUTIONAL NEURAL NETWORKS FOR TEXT CLASSIFICATION 11

SPIKING CONVOLUTIONAL NEURAL NETWORKS FOR TEXT CLASSIFICATIO...

引用

11th International Conference on Learning Representations, ICLR 2023

作者： Lv, Changze Xu, Jianhan Zheng, Xiaoqing School of Computer Science Fudan University Shanghai 200433 China Shanghai Key Laboratory of Intelligent Information Processing

Spiking neural networks (SNNs) offer a promising pathway to implement deep neural networks (DNNs) in a more energy-efficient manner since their neurons are sparsely activated and inferences are event-driven. However, there have been very few works that have demonstrated the efficacy of SNNs in language tasks partially because it is non-trivial to represent words in the forms of spikes and to deal with variable-length texts by SNNs. This work presents a "conversion + fine-tuning" two-step method for training SNNs for text classification and proposes a simple but effective way to encode pre-trained word embeddings as spike trains. We show empirically that after fine-tuning with surrogate gradients, the converted SNNs achieve comparable results to their DNN counterparts with much less energy consumption across multiple datasets for both English and Chinese. We also show that such SNNs are more robust to adversarial attacks than DNNs. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

A Power Consumption Forecasting Method Based on Knowledge Embedding Under the Influence of the COVID-19 Pandemic 8th

A Power Consumption Forecasting Method Based on Knowledge E...

引用

8th China National Conference on Big Data and Social Computing, BDSC 2023

作者： Zhang, Qifan Cao, Xiu Shanghai Key Lab of Intelligent Information Processing Department of Computer Science Fudan University Shanghai200433 China

ISBN: (纸本)9789819939244

This paper addresses the new challenges of power consumption forecasting under the COVID-19 pandemic by proposing a knowledge embedding-based method (PEC-19). This method constructs a multi-source enterprise knowledge graph by analyzing and combining the specific impact of COVID-19 on power consumption, and designs the Connection Count Calculation of PEC-19 (3CP) algorithm and Similarity Degree Calculation of PEC-19 (SDCP) algorithm based on the graph to measure the similarity between different users affected by the COVID-19 pandemic and enable knowledge transfer and embedding among them. Moreover, the PEC-19 framework introduces a SEIR-based caṇse number prediction module and a comprehensive feature extraction module to account for the trend of the COVID-19 pandemic and increase the variety of time series features. Compared with existing baseline methods, the PEC-19 framework can significantly improve the power consumption forecasting performance in both univariate and multivariate time series forecasting tasks, and each module contributes differently to the proposed method. The framework also has some applicability and provides a new solution for various public health emergencies, which helps to reduce power operation cost, coordinate supply and demand, and conduct accurate scheduling. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Sketch-based Point Cloud Generation with Diffusion Model and Pre-training Enhancement

Sketch-based Point Cloud Generation with Diffusion Model and...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Chen, Yangdong Chen, Mohan Zhang, Yuejie Feng, Rui Zhang, Tao Gao, Shang School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics Shanghai China School of Information Technology Deakin University Victoria Australia

ISBN: (纸本)9798350368741

Diffusion models, known for their success in various generative tasks like image generation and super-resolution, are applied in this study for point cloud generation, a field that has not been extensively explored due to the complexity of point clouds. We propose a novel method using a diffusion model to generate high-quality 3D point clouds from 2D sketches. This method employs a self-supervised contrastive learning scheme to align sketch and point cloud modalities. Additionally, it incorporates a specific partition mixing strategy to integrate edge information during pre-training. Evaluated on two benchmark datasets, our method outperforms existing state-of-the-art approaches, showcasing the potential of diffusion models in point cloud generation and setting a new direction for future research. © 2025 IEEE.

关键词： Diffusion Models Point Cloud Generation Self-supervised Contrastive Learning Sketch

来源：评论

学校读者我要写书评

暂无评论

A Holistic Evaluation of Piano Sound Quality 10th

A Holistic Evaluation of Piano Sound Quality

引用

10th Conference on Sound and Music technology, CSMT 2023

作者： Zhou, Monan Wu, Shangda Ji, Shaohua Li, Zijin Li, Wei Central Conservatory of Music Beijing China School of Computer Science and Technology Fudan University Shanghai200438 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9789819779611

This paper aims to develop a holistic evaluation method for piano sound quality to assist in purchasing decisions. Unlike previous studies that focused on the effect of piano performance techniques on sound quality, this study evaluates the inherent sound quality of different pianos. To derive quality evaluation systems, the study uses subjective questionnaires based on a piano sound quality dataset. The method selects the optimal piano classification models by comparing the fine-tuning results of different pre-training models of Convolutional Neural Networks (CNN). To improve the interpretability of the models, the study applies Equivalent Rectangular Bandwidth (ERB) analysis. The results reveal that musically trained individuals are better able to distinguish between the sound quality differences of different pianos. The best fine-tuned CNN pre-trained backbone achieves a high accuracy of 98.3% as the piano classifier. However, the dataset is limited, and the audio is sliced to increase its quantity, resulting in a lack of diversity and balance, so we use focal loss to reduce the impact of data imbalance. To optimize the method, the dataset will be expanded, or few-shot learning techniques will be employed in future research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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124页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：