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

8,901 篇 会议
43 篇 期刊文献
3 册 图书

馆藏范围

8,946 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

4,560 篇 工学
- 4,020 篇 计算机科学与技术...
- 2,178 篇 软件工程
- 1,241 篇 光学工程
- 555 篇 控制科学与工程
- 431 篇 信息与通信工程
- 430 篇 机械工程
- 294 篇 电气工程
- 287 篇 仪器科学与技术
- 179 篇 生物工程
- 159 篇 生物医学工程（可授...
- 119 篇 电子科学与技术（可...
- 61 篇 安全科学与工程
- 58 篇 建筑学
- 58 篇 化学工程与技术
- 52 篇 土木工程
- 49 篇 交通运输工程
- 40 篇 力学（可授工学、理...
2,065 篇 理学
- 1,382 篇 物理学
- 1,198 篇 数学
- 420 篇 统计学（可授理学、...
- 238 篇 生物学
- 54 篇 化学
- 36 篇 系统科学
263 篇 管理学
- 180 篇 图书情报与档案管...
- 89 篇 管理科学与工程(可...
- 47 篇 工商管理
223 篇 医学
- 222 篇 临床医学
- 39 篇 基础医学(可授医学...
205 篇 艺术学
- 205 篇 设计学（可授艺术学...
45 篇 法学
- 43 篇 社会学
21 篇 农学
14 篇 教育学
9 篇 经济学
6 篇 军事学

主题

3,412 篇 computer vision
1,205 篇 pattern recognit...
946 篇 cameras
908 篇 conferences
765 篇 computer science
674 篇 image segmentati...
618 篇 layout
598 篇 training
548 篇 shape
518 篇 robustness
452 篇 feature extracti...
448 篇 humans
445 篇 face recognition
405 篇 computational mo...
402 篇 object detection
365 篇 visualization
356 篇 computer archite...
336 篇 application soft...
304 篇 lighting
259 篇 image reconstruc...

机构

41 篇 microsoft resear...
30 篇 department of co...
25 篇 department of co...
23 篇 institute for co...
22 篇 department of co...
22 篇 school of comput...
20 篇 university of sc...
20 篇 swiss fed inst t...
19 篇 tsinghua univers...
19 篇 institute of com...
18 篇 swiss fed inst t...
17 篇 the robotics ins...
17 篇 carnegie mellon ...
17 篇 computer vision ...
17 篇 department of co...
16 篇 institute of inf...
16 篇 school of comput...
15 篇 school of comput...
15 篇 carnegie mellon ...
14 篇 national laborat...

作者

57 篇 timofte radu
25 篇 huang thomas s.
24 篇 van gool luc
23 篇 s.k. nayar
22 篇 nayar shree k.
22 篇 t. kanade
21 篇 jain anil k.
20 篇 luc van gool
19 篇 t.s. huang
18 篇 xiaoou tang
18 篇 murino vittorio
18 篇 horst bischof
17 篇 a.k. jain
17 篇 t. darrell
16 篇 g. healey
16 篇 bowyer kevin w.
16 篇 bischof horst
15 篇 m.j. black
15 篇 li stan z.
15 篇 m. shah

语言

8,917 篇 英文
21 篇 其他
8 篇 中文
1 篇 土耳其文

检索条件"任意字段=IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops"

共 8947 条记录，以下是221-230 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：

SymDNN: Simple & Effective Adversarial Robustness for Embedded Systems

SymDNN: Simple & Effective Adversarial Robustness for Embedd...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Dey, Swarnava Dasgupta, Pallab Chakrabarti, Partha P. Indian Inst Technol Kharagpur Kharagpur 721302 W Bengal India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose SymDNN, a Deep Neural Network (DNN) inference scheme, to segment an input image into small patches, replace those patches with representative symbols, and use the reconstructed image for CNN inference. This approach of deconstruction of images, and the reconstruction from cluster centroids trained on clean images, enhances robustness against adversarial attacks. The input transform used in SymDNN is learned from very large datasets, making it difficult to approximate for adaptive adversarial attacks. For example, SymDNN achieves 23% and 42% robust accuracy at L-infinity attack strengths of 8/255 and 4/255 respectively, against BPDA under a complete white box setting, where most input processing based defenses break completely. SymDNN is not a future-proof adversarial defense that can defend any attack, but it is one of the few readily usable defenses in resource-limited embedded systems that defends against a wide range of attacks.

关键词： computer vision Adaptation models Visualization Embedded systems Computational modeling Symbols Transforms

来源：评论

学校读者我要写书评

暂无评论

Federated Learning-based Driver Activity recognition for Edge Devices

Federated Learning-based Driver Activity Recognition for Edg...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Doshi, Keval Yilmaz, Yasin Univ S Florida 4202 E Fowler Ave Tampa FL 33620 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Video action recognition has been an active area of research for the past several years. However, the majority of research is concentrated on recognizing a diverse range of activities in distinct environments. On the other hand, Driver Activity recognition (DAR) is significantly more difficult since there is a much finer distinction between various actions. Moreover, training robust DAR models requires diverse training data from multiple sources, which might not be feasible for a centralized setup due to privacy and security concerns. Furthermore, it is critical to develop efficient models due to limited computational resources available on vehicular edge devices. Federated Learning (FL), which allows data parties to collaborate on machine learning models while preserving data privacy and reducing communication requirements, can be used to overcome these challenges. Despite significant progress on various computer vision tasks, FL for DAR has been largely unexplored. In this work, we propose an FL-based DAR model and extensively benchmark the model performance on two datasets under various practical setups. Our results indicate that the proposed approach performs competitively under the centralized (non-FL) and decentralized (FL) settings.

关键词： Performance evaluation Data privacy computer vision Computational modeling Activity recognition Collaborative work Data models

来源：评论

学校读者我要写书评

暂无评论

Low-Rank Adaptation vs. Fine-Tuning for Handwritten Text recognition

Low-Rank Adaptation vs. Fine-Tuning for Handwritten Text Rec...

引用

2025 ieee/CVF Winter conference on Applications of computer vision workshops, WACVW 2025

作者： Huttner, Lukas Mayr, Martin Gorges, Thomas Wu, Fei Seuret, Mathias Maier, Andreas Christlein, Vincent Friedrich-Alexander Universität Erlangen-Nürnberg Pattern Recognition Lab Erlangen91058 Germany

ISBN: (纸本)9798331536626

The continuous expansion of neural network sizes is a notable trend in machine learning, with transformer models exceeding 20 billion parameters in computer vision. This growth comes with rising demands for computational resources and large-scale datasets. Efficient techniques for transfer learning thus become an attractive option in setups with limited data, as in handwriting recognition. Recently, parameter-efficient fine-tuning (PEFT) methods, such as low-rank adaptation (LoRA) and weight-decomposed low-rank adaptation (DoRA), have gained wide-spread interest. In this paper, we explore tradeoffs in parameter-efficient transfer learning using the synthetically pretrained Transformer-Based Optical Character recognition (TrOCR) model for handwritten text recognition with LoRA and DoRA. Additionally, we analyze the performance of full fine-tuning with a limited number of samples, scaling from a few-shot learning scenario up to using the whole dataset. We conduct experiments on the popular IAM Handwriting database as well as the historical READ 2016 dataset. We find that (a) LoRA/DoRA does not outperform full fine-tuning as opposed to a recent paper and (b) LoRA/DoRA is not substantially faster than full fine-tuning of TrOCR. © 2025 ieee.

关键词： Optical character recognition

来源：评论

学校读者我要写书评

暂无评论

Disentangled Loss for Low-Bit Quantization-Aware Training

Disentangled Loss for Low-Bit Quantization-Aware Training

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Allenet, Thibault Briand, David Bichler, Olivier Sentieys, Olivier CEA LIST Saclay France Univ Rennes INRIA Rennes France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Quantization-Aware Training (QAT) has recently showed a lot of potential for low-bit settings in the context of image classification. Approaches based on QAT are using the Cross Entropy Loss function which is the reference loss function in this domain. We investigate quantization-aware training with disentangled loss functions. We qualify a loss to disentangle as it encourages the network output space to be easily discriminated with linear functions. We introduce a new method, Disentangled Loss Quantization Aware Training, as our tool to empirically demonstrate that the quantization procedure benefits from those loss functions. Results show that the proposed method substantially reduces the loss in top-1 accuracy for low-bit quantization on CIFAR10, CIFAR100 and ImageNet. Our best result brings the top-1 Accuracy of a Resnet-18 from 63.1% to 64.0% with binary weights and 2-bit activations when trained on ImageNet.

关键词： Training Visualization computer vision Quantization (signal) conferences Entropy pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Edge-enhanced Feature Distillation Network for Efficient Super-Resolution

Edge-enhanced Feature Distillation Network for Efficient Sup...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Yan Nankai Univ Nankai Baidu Joint Lab Tianjin Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With the recently massive development in convolution neural networks, numerous lightweight CNN-based image super-resolution methods have been proposed for practical deployments on edge devices. However, most existing methods focus on one specific aspect: network or loss design, which leads to the difficulty of minimizing the model size. To address the issue, we conclude block devising, architecture searching, and loss design to obtain a more efficient SR structure. In this paper, we proposed an edge-enhanced feature distillation network, named EFDN, to preserve the high-frequency information under constrained resources. In detail, we build an edge-enhanced convolution block based on the existing reparameterization methods. Meanwhile, we propose edge-enhanced gradient loss to calibrate the reparameterized path training. Experimental results show that our edge-enhanced strategies preserve the edge and significantly improve the final restoration quality. Code is available at https://***/icandle/EFDN.

关键词： Training computer vision Convolution Image edge detection conferences Superresolution Neural networks

来源：评论

学校读者我要写书评

暂无评论

Out-Of-Distribution Detection In Unsupervised Continual Learning

Out-Of-Distribution Detection In Unsupervised Continual Lear...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： He, Jiangpeng Zhu, Fengqing Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Unsupervised continual learning aims to learn new tasks incrementally without requiring human annotations. However, most existing methods, especially those targeted on image classification, only work in a simplified scenario by assuming all new data belong to new tasks, which is not realistic if the class labels are not provided. Therefore, to perform unsupervised continual learning in real life applications, an out-of-distribution detector is required at beginning to identify whether each new data corresponds to a new task or already learned tasks, which still remains under-explored yet. In this work, we formulate the problem for Out-of-distribution Detection in Unsupervised Continual Learning (OOD-UCL) with the corresponding evaluation protocol. In addition, we propose a novel OOD detection method by correcting the output bias at first and then enhancing the output confidence for in-distribution data based on task discriminativeness, which can be applied directly without modifying the learning procedures and objectives of continual learning. Our method is evaluated on CIFAR-100 dataset by following the proposed evaluation protocol and we show improved performance compared with existing OOD detection methods under the unsupervised continual learning scenario.

关键词： computer vision Protocols Annotations conferences Detectors pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

Multi-Camera Vehicle Tracking System for AI City Challenge 2022

Multi-Camera Vehicle Tracking System for AI City Challenge 2...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Li, Fei Wang, Zhen Nie, Ding Zhang, Shiyi Jiang, Xingqun Zhao, Xingxing Hu, Peng BOE Technol Grp Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Multi-Target Multi-Camera tracking is a fundamental task for intelligent traffic systems. The track 1 of AI City Challenge 2022 aims at the city-scale multi-camera vehicle tracking task. In this paper we propose an accurate vehicle tracking system composed of 4 parts, including: (1) State-of-the-art detection and re-identification models for vehicle detection and feature extraction. (2) Single camera tracking, where we introduce augmented tracks prediction and multi-level association method on top of tracking-by-detection paradigm.(3) Zone-based singe-camera track-let merging strategy. (4) Multi-camera spatial-temporal matching and clustering strategy. The proposed system achieves promising results and ranks the second place in Track 1 of the AI City Challenge 2022 with a IDF1 score of 0.8437.

关键词： computer vision Vehicle detection conferences Urban areas Merging Predictive models Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs

Searching for Efficient Neural Architectures for On-Device M...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Akin, Berkin Gupta, Suyog Long, Yun Spiridonov, Anton Wang, Zhuo White, Marie Xu, Hao Zhou, Ping Zhou, Yanqi

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

On-device ML accelerators are becoming a standard in modern mobile system-on-chips (SoC). Neural architecture search (NAS) comes to the rescue for efficiently utilizing the high compute throughput offered by these accelerators. However, existing NAS frameworks have several practical limitations in scaling to multiple tasks and different target platforms. In this work, we provide a two-pronged approach to this challenge: (i) a NAS-enabling infrastructure that decouples model cost evaluation, search space design, and the NAS algorithm to rapidly target various on-device ML tasks, and (ii) search spaces crafted from group convolution based inverted bottleneck (IBN) variants that provide flexible quality/performance trade-offs on ML accelerators, complementing the existing full and depthwise convolution based IBNs. Using this approach we target a state-of-the-art mobile platform, Google Tensor SoC, and demonstrate neural architectures1 that improve the quality-performance pareto frontier for various computer vision (classification, detection, segmentation) as well as natural language processing tasks.

关键词： computer vision Tensors Costs Convolution Image edge detection computer architecture Throughput

来源：评论

学校读者我要写书评

暂无评论

M2DAR: Multi-View Multi-Scale Driver Action recognition with vision Transformer

M2DAR: Multi-View Multi-Scale Driver Action Recognition with...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Ma, Yunsheng Yuan, Liangqi Abdelraouf, Amr Han, Kyungtae Gupta, Rohit Li, Zihao Wang, Ziran Purdue University College of Engineering United States Toyota Motor North America InfoTech Labs United States

ISBN: (纸本)9798350302493

Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal. In this paper, we present a multi-view, multi-scale framework for naturalistic driving action recognition and localization in untrimmed videos, namely M2DAR, with a particular focus on detecting distracted driving behaviors. Our system features a weight-sharing, multi-scale Transformer-based action recognition network that learns robust hierarchical representations. Furthermore, we propose a new election algorithm consisting of aggregation, filtering, merging, and selection processes to refine the preliminary results from the action recognition module across multiple views. Extensive experiments conducted on the 7th AI City Challenge Track 3 dataset demonstrate the effectiveness of our approach, where we achieved an overlap score of 0.5921 on the A2 test set. Our source code is available at https://***/PurdueDigitalTwin/M2DAR. © 2023 ieee.

关键词： Statistical tests

来源：评论

学校读者我要写书评

暂无评论

Multi-view Multi-label Canonical Correlation Analysis for Cross-modal Matching and Retrieval

Multi-view Multi-label Canonical Correlation Analysis for Cr...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Sanghavi, Rushil Verma, Yashaswi IIT Jodhpur Jodhpur Rajasthan India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we address the problem of cross-modal retrieval in presence of multi-view and multi-label data. For this, we present Multi-view Multi-label Canonical Correlation Analysis (or MVMLCCA), which is a generalization of CCA for multi-view data that also makes use of high-level semantic information available in the form of multi-label annotations in each view. While CCA relies on explicit pairings/associations of samples between two views (or modalities), MVMLCCA uses the available multi-label annotations to establish correspondence across multiple (two or more) views without the need of explicit pairing of multi-view samples. Extensive experiments on two multi-modal datasets demonstrate that the proposed approach offers much more flexibility than the related approaches without compromising on scalability and cross-modal retrieval performance. Our code and precomputed features are available at https://***/Rushil231100/MVMLCCA.

关键词： computer vision Correlation Codes Annotations Scalability conferences Semantics

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500页 << < 19 20 21 22 23 24 25 26 27 28 > >>

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：