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

3,317 篇 会议
3 篇 期刊文献

馆藏范围

3,320 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,915 篇 工学
- 1,820 篇 计算机科学与技术...
- 376 篇 软件工程
- 142 篇 机械工程
- 136 篇 光学工程
- 42 篇 生物工程
- 28 篇 信息与通信工程
- 11 篇 控制科学与工程
- 9 篇 电气工程
- 9 篇 电子科学与技术（可...
- 9 篇 化学工程与技术
- 9 篇 交通运输工程
- 8 篇 生物医学工程（可授...
- 7 篇 安全科学与工程
- 4 篇 材料科学与工程（可...
- 4 篇 建筑学
- 3 篇 土木工程
- 3 篇 农业工程
174 篇 理学
- 136 篇 物理学
- 43 篇 生物学
- 29 篇 数学
- 16 篇 统计学（可授理学、...
- 10 篇 化学
29 篇 医学
- 28 篇 临床医学
- 3 篇 基础医学(可授医学...
15 篇 管理学
- 8 篇 管理科学与工程(可...
- 7 篇 图书情报与档案管...
- 3 篇 工商管理
5 篇 法学
- 3 篇 社会学
- 2 篇 法学
2 篇 教育学
- 2 篇 教育学
2 篇 农学
1 篇 经济学

主题

1,179 篇 computer vision
801 篇 conferences
570 篇 training
484 篇 pattern recognit...
330 篇 computational mo...
279 篇 computer archite...
255 篇 visualization
180 篇 feature extracti...
160 篇 neural networks
153 篇 semantics
152 篇 task analysis
145 篇 cameras
143 篇 deep learning
140 篇 three-dimensiona...
139 篇 benchmark testin...
126 篇 low-level vision
117 篇 vision
115 篇 language
113 篇 image segmentati...
112 篇 estimation

机构

35 篇 tsinghua univ pe...
34 篇 univ sci & techn...
28 篇 shanghai ai lab ...
26 篇 chinese univ hon...
25 篇 university of sc...
25 篇 tsinghua univers...
24 篇 peng cheng labor...
23 篇 swiss fed inst t...
23 篇 swiss fed inst t...
23 篇 peng cheng lab p...
22 篇 zhejiang univ pe...
22 篇 carnegie mellon ...
20 篇 univ chinese aca...
20 篇 university of ch...
18 篇 harbin inst tech...
18 篇 shanghai jiao to...
18 篇 sensetime res pe...
16 篇 hong kong univ s...
15 篇 fudan univ peopl...
14 篇 national key lab...

作者

60 篇 timofte radu
26 篇 radu timofte
19 篇 van gool luc
16 篇 lei lei
15 篇 loy chen change
15 篇 qiao yu
14 篇 fan haoqiang
13 篇 zhang yulun
13 篇 liu shuaicheng
13 篇 luc van gool
12 篇 chen chen
12 篇 chen wei-ting
12 篇 li chongyi
11 篇 wangmeng zuo
11 篇 pan jinshan
11 篇 marcos v. conde
10 篇 wei dong
10 篇 zhou shangchen
10 篇 suiyi zhao
10 篇 yu lei

语言

3,318 篇 英文
2 篇 其他

检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是2051-2060 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：

Are NeRFs ready for autonomous driving? Towards closing the real-to-simulation gap

Are NeRFs ready for autonomous driving? Towards closing the ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Carl Lindström Georg Hess Adam Lilja Maryam Fatemi Lars Hammarstrand Christoffer Petersson Lennart Svensson Zenseact Chalmers University of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural Radiance Fields (NeRFs) have emerged as promising tools for advancing autonomous driving (AD) research, offering scalable closed-loop simulation and data augmentation capabilities. However, to trust the results achieved in simulation, one needs to ensure that AD systems perceive real and rendered data in the same way. Although the performance of rendering methods is increasing, many scenarios will remain inherently challenging to reconstruct faithfully. To this end, we propose a novel perspective for addressing the real-to-simulated data gap. Rather than solely focusing on improving rendering fidelity, we explore simple yet effective methods to enhance perception model robustness to NeRF artifacts without compromising performance on real data. Moreover, we conduct the first large-scale investigation into the real-to-simulated data gap in an AD setting using a state-of-the-art neural rendering technique. Specifically, we evaluate object detectors and an online mapping model on real and simulated data, and study the effects of different fine-tuning strategies. Our results show notable improvements in model robustness to simulated data, even improving real-world performance in some cases. Last, we delve into the correlation between the real-to-simulated gap and image reconstruction metrics, identifying FID and LPIPS as strong indicators.

关键词： Measurement Object detection Neural radiance field Rendering (computer graphics) Data models Robustness pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Multi-modal Arousal and Valence Estimation under Noisy Conditions

Multi-modal Arousal and Valence Estimation under Noisy Condi...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Denis Dresvyanskiy Maxim Markitantov Jiawei Yu Heysem Kaya Alexey Karpov Ulm University Germany ITMO University Russia St. Petersburg Federal Research Center of the Russian Academy of Sciences St. Petersburg Russia Department of Information and Computing Sciences Utrecht University The Netherlands

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Automatic emotion recognition has gained significant attention over the past two decades due to the central role that emotions play in human communication. While multi-modal systems demonstrate high performances on laboratory-controlled data, their validity on non-lab-controlled, namely ‘in-the-wild’ data, remains a challenge. This work investigates audio-visual deep learning approaches for emotion recognition in-the-wild, with a particular focus on the effectiveness of architectures based on fine-tuned Convolutional Neural Networks (CNN) and Public Dimensional Emotion Model (PDEM) for video and audio modality, respectively. We explore and compare various temporal modeling techniques (e.g., transformer architectures) and fusion strategies by leveraging the embeddings from developed multi-stage trained modality-specific Deep Neural Networks (DNN). The results are reported on the Af-fWild2 dataset following the Affective Behavior Analysis in-the-Wild 2024 (ABAW’24) challenge protocol. Our investigation highlights the complexities of robust multi-modal emotion recognition in an unconstrained environment, providing insights into the usage of various deep learning architectures for tackling this challenging task.

关键词： Deep learning Emotion recognition Protocols conferences Estimation computer architecture Transformers

来源：评论

学校读者我要写书评

暂无评论

Learnable Global Spatio-Temporal Adaptive Aggregation for Bracketing Image Restoration and Enhancement

Learnable Global Spatio-Temporal Adaptive Aggregation for Br...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xinwei Dai Yuanbo Zhou Xintao Qiu Hui Tang Wei Deng Qingquan Gao Tong Tong Fuzhou University Imperial Vision Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Employing specific networks to address different types of degradation often proved to be complex and time-consuming in practical applications. The Bracket Image Restoration and Enhancement (BIRE) aimed to address various image restoration tasks in a unified manner by restoring clear single-frame images from multiple-frame shots, including denoising, deblurring, enhancing high dynamic range (HDR), and achieving super-resolution under various degradation conditions. In this paper, we propose LGSTANet, an efficient aggregation restoration network for BIRE. Specifically, inspired by video restoration methods, we adopt an efficient architecture comprising alignment, aggregation, and reconstruction. Additionally, we introduce a Learnable Global Spatio-Temporal Adaptive (LGSTA) aggregation module to effectively aggregate inter-frame complementary information. Furthermore, we propose an adaptive restoration modulator to address specific degradation disturbances of various types, thereby achieving high-quality restoration outcomes. Extensive experiments demonstrate the effectiveness of our method. LGSTANet outperforms other state-of-the-art methods in Bracket Image Restoration and Enhancement and achieves competitive results in the NTIRE2024 BIRE challenge.

关键词： Degradation Aggregates Perturbation methods Superresolution Noise reduction Modulation computer architecture

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yawei Li Yulun Zhang Radu Timofte Luc Van Gool Lei Yu Youwei Li Xinpeng Li Ting Jiang Qi Wu Mingyan Han Wenjie Lin Chengzhi Jiang Jinting Luo Haoqiang Fan Shuaicheng Liu Yucong Wang Minjie Cai Mingxi Li Yuhang Zhang Xian-Jun Fan Yankai Sheng Yanyu Mao Nihao Zhang Qian Wang Mingjun Zheng Long Sun Jinshan Pan Jiangxin Dong Jinhui Tang Zhongbao Yang Yan Wang Erlin Pan Qixuan Cai Xinan Dai Magauiya Zhussip Nikolay Kalyazin Dmitry Vyal Xueyi Zou Youliang Yan Heaseo Chung Jin Zhang Gaocheng Yu Feng Zhang Hongbin Wang Bohao Liao Zhibo Du Yu-Liang Wu Gege Shi Long Peng Yang Wang Yang Cao Zhengjun Zha Zhi-Kai Huang Yi-Chung Chen Yuan-Chun Chiang Hao-Hsiang Yang Wei-Ting Chen Hua-En Chang I-Hsiang Chen Chia-Hsuan Hsieh Sy-Yen Kuo Xin Liu Jiahao Pan Hongyuan Yu Weichen Yu Lin Ge Jiahua Dong Yajun Zou Zhuoyuan Wu Binnan Han Xiaolin Zhang Heng Zhang Xuanwu Yin Kunlong Zuo Weijian Deng Hongjie Yuan Zengtong Lu Mingyu Ouyang Wenzhuo Ma Nian Liu Hanyou Zheng Yuantong Zhang Junxi Zhang Zhenzhong Chen Garas Gendy Nabil Sabor Jingchao Hou Guanghui He Yurui Zhu Xi Wang Xueyang Fu Zheng-Jun Zha Daheng Yin Mengyang Liu Baijun Chen Ao Li Lei Luo Kangjun Jin Ce Zhu Xiaoming Zhang Chengxing Xie Linze Li Haiteng Meng Tianlin Zhang Tianrui Li Xiaole Zhao Zhao Zhang Baiang Li Huan Zheng Suiyi Zhao Yangcheng Gao Jiahuan Ren Kang Hu Jingpeng Shi Zhijian Wu Dingjiang Huang Jinchen Zhu Hui Li Qianru Xv Tianle Liu Shizhuang Weng Gang Wu Junpeng Jiang Xianming Liu Junjun Jiang Mingjian Zhang Jing Hu Chengxu Wu Qinrui Fan Chengming Feng Ziwei Luo Shu Hu Siwei Lyu Xi Wu Xin Wang Computer Vision Lab ETH Zurich

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Recognize Anything: A Strong Image Tagging Model

Recognize Anything: A Strong Image Tagging Model

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Youcai Zhang Xinyu Huang Jinyu Ma Zhaoyang Li Zhaochuan Luo Yanchun Xie Yuzhuo Qin Tong Luo Yaqian Li Shilong Liu Yandong Guo Lei Zhang OPPO Research Institute International Digital Economy Academy (IDEA) AI2Robotics

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present the Recognize Anything Model (RAM): a strong foundation model for image tagging. RAM makes a substantial step for foundation models in computer vision, demonstrating the zero-shot ability to recognize any common category with high accuracy. By leveraging large-scale image-text pairs for training instead of manual annotations, RAM introduces a new paradigm for image *** development of RAM comprises four key steps. Firstly, annotation-free image tags are obtained at scale through automatic text semantic parsing. Subsequently, a preliminary model is trained for automatic annotation by unifying the captioning and tagging tasks, supervised by the original texts and parsed tags, respectively. Thirdly, a data engine is employed to generate additional annotations and clean incorrect ones. Lastly, the model is retrained with the processed data and fine-tuned using a smaller but higher-quality *** evaluate the tagging capability of RAM on numerous benchmarks and observe an impressive zero-shot performance, which significantly outperforms CLIP and BLIP. Remarkably, RAM even surpasses fully supervised models and exhibits a competitive performance compared with the Google tagging API. We have released RAM at https://***/ to foster the advancement of foundation models in computer vision.

关键词： Training computer vision Image recognition Annotations Computational modeling Random access memory Image annotation

来源：评论

学校读者我要写书评

暂无评论

ALINA: Advanced Line Identification and Notation Algorithm

ALINA: Advanced Line Identification and Notation Algorithm

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Mohammed Abdul Hafeez Khan Parth Ganeriwala Siddhartha Bhattacharyya Natasha Neogi Raja Muthalagu Florida Institute of Technology Melbourne Florida NASA Langley Research Center Hampton Virginia BITS Pilani Dubai Campus Dubai UAE

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Labels are the cornerstone of supervised machine learning algorithms. Most visual recognition methods are fully supervised, using bounding boxes or pixel-wise segmentations for object localization. Traditional labeling methods, such as crowd-sourcing, are prohibitive due to cost, data privacy, amount of time, and potential errors on large datasets. To address these issues, we propose a novel annotation framework, Advanced Line Identification and Notation Algorithm (ALINA), which can be used for labeling taxiway datasets that consist of different camera perspectives and variable weather attributes (sunny and cloudy). Additionally, the CIRCular threshoLd pixEl Discovery And Traversal (CIRCLEDAT) algorithm has been proposed, which is an integral step in determining the pixels corresponding to taxiway line markings. Once the pixels are identified, ALINA generates corresponding pixel coordinate annotations on the frame. Using this approach, 60,249 frames from the taxiway dataset, AssistTaxi have been labeled. To evaluate the performance, a context-based edge map (CBEM) set was generated manually based on edge features and connectivity. The detection rate after testing the annotated labels with the CBEM set was recorded as 98.45%, attesting its dependability and effectiveness.

关键词： Visualization Costs Annotations Image edge detection Roads Manuals pattern recognition

来源：评论

学校读者我要写书评

暂无评论

DOAD: Decoupled One Stage Action Detection Network

DOAD: Decoupled One Stage Action Detection Network

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Shuning Chang Pichao Wang Fan Wang Jiashi Feng Mike Zheng Shou Showlab National University of Singapore Alibaba Group National University of Singapore

ISBN: (数字)9798350302493

ISBN: (纸本)9798350302509

Localizing people and recognizing their actions from videos is a challenging task towards high-level video understanding. Existing methods are mostly two-stage based, with one stage for person bounding box generation and the other stage for action recognition. However, such two-stage methods are generally with low efficiency. We observe that directly unifying detection and action recognition normally suffers from (i) inferior learning due to different desired properties of context representation for detection and action recognition; (ii) optimization difficulty with insufficient training data. In this work, we present a decoupled onestage network dubbed DOAD, to mitigate above issues and improve the efficiency for spatio-temporal action detection. To achieve it, we decouple detection and action recognition into two branches. Specifically, one branch focuses on detection representation for actor detection, and the other one for action recognition. For the action branch, we design a transformer-based module (TransPC) to model pairwise relationships between people and context. Different from commonly used vector-based dot product in self-attention, it is built upon a novel matrix-based key and value for Hadamard attention to model person-context information. It not only exploits relationships between person pairs but also takes into account context and relative position information. The results on AVA and UCF101-24 datasets show that our method is competitive with two-stage state-of-theart methods with significant efficiency improvement.

关键词： computer vision conferences Training data Transformers Feature extraction pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

Classifier Guided Cluster Density Reduction for Dataset Selection

Classifier Guided Cluster Density Reduction for Dataset Sele...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Cheng Chang Keyu Long Zijian Li Himanshu Rai Layer 6 AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we address the challenge of selecting an optimal dataset from a source pool with annotations to enhance performance on a target dataset derived from a different source. This is important in scenarios where it is hard to afford on-the-fly dataset annotation and is also the theme of the second Visual Data Understanding (VDU) Challenge. Our solution, the Classifier Guided Cluster Density Reduction (CCDR) framework, operates in two stages. Initially, we employ a filtering technique to identify images that align with the target dataset’s distribution. Subsequently, we implement a graph-based cluster density reduction method, steered by a classifier that approximates the distance between the target distribution and source distribution. This classifier is trained to distinguish between images that resemble the target dataset and those that do not, facilitating the pruning process shown in Figure 1. Our approach maintains a balance between selecting pertinent images that match the target distribution and eliminating redundant ones that do not contribute to the enhancement of the detection model. We demonstrate the superiority of our method over various baselines in object detection tasks, particularly in optimizing the training set distribution on the region100 dataset. We have released our code here: https://***/himsR/DataCVChallenge-2024/tree/main

关键词： Training Visualization computer vision Codes Annotations Filtering conferences

来源：评论

学校读者我要写书评

暂无评论

Adapting the Segment Anything Model During Usage in Novel Situations

Adapting the Segment Anything Model During Usage in Novel Si...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Robin Schön Julian Lorenz Katja Ludwig Rainer Lienhart Universität Augsburg Augsburg

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The interactive segmentation task consists in the creation of object segmentation masks based on user interactions. The most common way to guide a model towards producing a correct segmentation consists in clicks on the object and background. The recently published Segment Anything Model (SAM) supports a generalized version of the interactive segmentation problem and has been trained on an object segmentation dataset which contains 1.1B masks. Though being trained extensively and with the explicit purpose of serving as a foundation model, we show significant limitations of SAM when being applied for interactive segmentation on novel domains or object types. On the used datasets, SAM displays a failure rate FR 30 @90 of up to 72.6%. Since we still want such foundation models to be immediately applicable, we present a framework that can adapt SAM during immediate usage. For this we will leverage the user interactions and masks, which are constructed during the interactive segmentation process. We use this information to generate pseudo-labels, which we use to compute a loss function and optimize a part of the SAM model. The presented method causes a relative reduction of up to 48.1% in the FR 20 @85 and 46.6% in the FR 30 @90 metrics.

关键词： Measurement Adaptation models Image segmentation computer vision Computational modeling conferences Object segmentation

来源：评论

学校读者我要写书评

暂无评论

What does CLIP know about peeling a banana?

What does CLIP know about peeling a banana?

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Claudia Cuttano Gabriele Rosi Gabriele Trivigno Giuseppe Averta Politecnico di Torino Focoos AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Humans show an innate capability to identify tools to support specific actions. The association between objects parts and the actions they facilitate is usually named affordance. Being able to segment objects parts depending on the tasks they afford is crucial to enable intelligent robots to use objects of daily living. Traditional supervised learning methods for affordance segmentation require costly pixel-level annotations, while weakly supervised approaches, though less demanding, still rely on object-interaction examples and support a closed set of actions. These limitations hinder scalability, may introduce biases, and usually restrict models to a limited set of predefined actions. This paper proposes Affordance-CLIP, to overcome these limitations by leveraging the implicit affordance knowledge embedded within large pre-trained vision-Language models like CLIP. We experimentally demonstrate that CLIP, although not explicitly trained for affordances detection, retains valuable information for the task. Our AffordanceCLIP achieves competitive zero-shot performance compared to methods with specialized training, while offering several advantages: i) it works with any action prompt, not just a predefined set; ii) it requires training only a small number of additional parameters compared to existing solutions and iii) eliminates the need for direct supervision on action-object pairs, opening new perspectives for functionality-based reasoning of models.

关键词： Training computer vision Affordances Scalability conferences Computational modeling Supervised learning

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332页 << < 202 203 204 205 206 207 208 209 210 211 > >>

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：