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

3,319 篇 英文
1 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2561-2570 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：

ZBS: Zero-Shot Background Subtraction via Instance-Level Background Modeling and Foreground Selection

ZBS: Zero-Shot Background Subtraction via Instance-Level Bac...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yongqi An Xu Zhao Tao Yu Haiyun Gu Chaoyang Zhao Ming Tang Jinqiao Wang National Laboratory of Pattern Recognition Institute of Automation CAS Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

Background subtraction (BGS) aims to extract all moving objects in the video frames to obtain binary foreground segmentation masks. Deep learning has been widely used in this field. Compared with supervised-based BGS methods, unsupervised methods have better generalization. However, previous unsupervised deep learning BGS algorithms perform poorly in sophisticated scenarios such as shadows or night lights, and they cannot detect objects outside the pre-defined categories. In this work, we propose an unsuper-vised BGS algorithm based on zero-shot object detection called Zero-shot Background Subtraction (ZBS). The proposed method fully utilizes the advantages of zero-shot object detection to build the open-vocabulary instance-level background model. Based on it, the foreground can be effectively extracted by comparing the detection results of new frames with the background model. ZBS performs well for sophisticated scenarios, and it has rich and extensible categories. Furthermore, our method can easily generalize to other tasks, such as abandoned object detection in unseen environments. We experimentally show that ZBS surpasses state-of-the-art unsupervised BGS methods by 4.70% F-Measure on the CDnet 2014 dataset. The code is released at https://***/CASIA-IVA-Lab/ZBS.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Adversarial Fooling Beyond "Flipping the Label"

Adversarial Fooling Beyond "Flipping the Label"

引用

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

作者： Mopuri, Konda Reddy Shaj, Vaisakh Babu, R. Venkatesh Indian Inst Sci Video Analyt Lab Benguluru India

ISBN: (纸本)9781728193601

Recent advancements in CNNs have shown remarkable achievements in various CV/AI applications. Though CNNs show near human or better than human performance in many critical tasks, they are quite vulnerable to adversarial attacks. These attacks are potentially dangerous in real-life deployments. Though there have been many adversarial attacks proposed in recent years, there is no proper way of quantifying the effectiveness of these attacks. As of today, mere fooling rate is used for measuring the susceptibility of the models, or the effectiveness of adversarial attacks. Fooling rate just considers label flipping and does not consider the cost of such flipping, for instance, in some deployments, flipping between two species of dogs may not be as severe as confusing a dog category with that of a vehicle. Therefore, the metric to quantify the vulnerability of the models should capture the severity of the flipping as well. In this work we first bring out the drawbacks of the existing evaluation and propose novel metrics to capture various aspects of the fooling. Further, for the first time, we present a comprehensive analysis of several important adversarial attacks over a set of distinct CNN architectures. We believe that the presented analysis brings valuable insights about the current adversarial attacks and the CNN models.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Accelerating vision-Language Pretraining with Free Language Modeling

Accelerating Vision-Language Pretraining with Free Language ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Teng Wang Yixiao Ge Feng Zheng Ran Cheng Ying Shan Xiaohu Qie Ping Luo Southern University of Science and Technology The University of Hong Kong ARC Lab Peng Cheng Laboratory Tencent PCG Shanghai AI Laboratory

The state of the arts in vision-language pretraining (VLP) achieves exemplary performance but suffers from high training costs resulting from slow convergence and long training time, especially on large-scale web datasets. An essential obstacle to training efficiency lies in the entangled prediction rate (percentage of tokens for reconstruction) and corruption rate (percentage of corrupted tokens) in masked language modeling (MLM), that is, a proper corruption rate is achieved at the cost of a large portion of output tokens being excluded from prediction loss. To accelerate the convergence of VLP, we propose a new pretraining task, namely, free language modeling (FLM), that enables a 100% prediction rate with arbitrary corruption rates. FLM successfully frees the prediction rate from the tie-up with the corruption rate while allowing the corruption spans to be customized for each token to be predicted. FLM-trained models are encouraged to learn better and faster given the same GPU time by exploiting bidirectional contexts more flexibly. Extensive experiments show FLM could achieve an impressive 2.5 × pretraining time reduction in comparison to the MLM-based methods, while keeping competitive performance on both vision-language understanding and generation tasks. Code will be public at https://***/TencentARC/FLM.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DAA: A Delta Age AdaIN operation for age estimation via binary code transformer

DAA: A Delta Age AdaIN operation for age estimation via bina...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Ping Chen Xingpeng Zhang Ye Li Ju Tao Bin Xiao Bing Wang Zongjie Jiang Jiayu Intelligent Technology Co. Ltd. (Affiliated With Great Wall Motor Company Limited) School of Computer Science Southwest Petroleum University Chengdu China

Naked eye recognition of age is usually based on comparison with the age of others. However, this idea is ignored by computer tasks because it is difficult to obtain representative contrast images of each age. Inspired by the transfer learning, we designed the Delta Age AdaIN (DAA) operation to obtain the feature difference with each age, which obtains the style map of each age through the learned values representing the mean and standard deviation. We let the input of transfer learning as the binary code of age natural number to obtain continuous age feature information. The learned two groups of values in Binary code mapping are corresponding to the mean and standard deviation of the comparison ages. In summary, our method consists of four parts: FaceEncoder, DAA operation, Binary code mapping, and AgeDecoder modules. After getting the delta age via AgeDecoder, we take the average value of all comparison ages and delta ages as the predicted age. Compared with state-of-the-art methods, our method achieves better performance with fewer parameters on multiple facial age datasets. Code is available at https://***/redcping/Delta_Age_AdaIN

关键词：

来源：评论

学校读者我要写书评

暂无评论

OpenGait: Revisiting Gait recognition Toward Better Practicality

OpenGait: Revisiting Gait Recognition Toward Better Practica...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Chao Fan Junhao Liang Chuanfu Shen Saihui Hou Yongzhen Huang Shiqi Yu Department of Computer Science and Engineering Southern University of Science and Technology Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong School of Artificial Intelligence Beijing Normal University WATRIX.AI

Gait recognition is one of the most critical long-distance identification technologies and increasingly gains popularity in both research and industry communities. Despite the significant progress made in indoor datasets, much evidence shows that gait recognition techniques perform poorly in the wild. More importantly, we also find that some conclusions drawn from indoor datasets cannot be generalized to real applications. Therefore, the primary goal of this paper is to present a comprehensive benchmark study for better practicality rather than only a particular model for better performance. To this end, we first develop a flexible and efficient gait recognition codebase named OpenGait. Based on OpenGait, we deeply revisit the recent development of gait recognition by re-conducting the ablative experiments. Encouragingly, we detect some unperfect parts of certain prior woks, as well as new insights. Inspired by these discoveries, we develop a structurally simple, empirically powerful, and practically robust baseline model, Gait-Base. Experimentally, we comprehensively compare Gait-Base with many current gait recognition methods on multiple public datasets, and the results reflect that GaitBase achieves significantly strong performance in most cases regardless of indoor or outdoor situations. Code is available at https://***/ShiqiYu/OpenGait.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Tri-Perspective View for vision-Based 3D Semantic Occupancy Prediction

Tri-Perspective View for Vision-Based 3D Semantic Occupancy ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yuanhui Huang Wenzhao Zheng Yunpeng Zhang Jie Zhou Jiwen Lu Beijing National Research Center for Information Science and Technology China Department of Automation Tsinghua University China

Modern methods for vision-centric autonomous driving perception widely adopt the bird's-eye-view (BEV) representation to describe a 3D scene. Despite its better efficiency than voxel representation, it has difficulty describing the fine-grained 3D structure of a scene with a single plane. To address this, we propose a tri-perspective view (TPV) representation which accompanies BEV with two additional perpendicular planes. We model each point in the 3D space by summing its projected features on the three planes. To lift image features to the 3D TPV space, we further propose a transformer-based TPV encoder (TPVFormer) to obtain the TPV features effectively. We employ the attention mechanism to aggregate the image features corresponding to each query in each TPV plane. Experiments show that our model trained with sparse supervision effectively predicts the semantic occupancy for all voxels. We demonstrate for the first time that using only camera inputs can achieve comparable performance with LiDAR-based methods on the LiDAR segmentation task on nuScenes. Code: https://***/wzzheng/TPVFormer.

关键词：

来源：评论

学校读者我要写书评

暂无评论

BiFormer: vision Transformer with Bi-Level Routing Attention

BiFormer: Vision Transformer with Bi-Level Routing Attention

引用

conference on computer vision and pattern recognition (CVPR)

作者： Lei Zhu Xinjiang Wang Zhanghan Ke Wayne Zhang Rynson Lau City University of Hong Kong SenseTime Research

As the core building block of vision transformers, attention is a powerful tool to capture long-range dependency. However, such power comes at a cost: it incurs a huge computation burden and heavy memory footprint as pairwise token interaction across all spatial locations is computed. A series of works attempt to alleviate this problem by introducing handcrafted and content-agnostic sparsity into attention, such as restricting the attention operation to be inside local windows, axial stripes, or dilated windows. In contrast to these approaches, we propose a novel dynamic sparse attention via bi-level routing to enable a more flexible allocation of computations with content awareness. Specifically, for a query, irrelevant key-value pairs are first filtered out at a coarse region level, and then fine-grained token-to-token attention is applied in the union of remaining candidate regions (i.e., routed regions). We provide a simple yet effective implementation of the proposed bilevel routing attention, which utilizes the sparsity to save both computation and memory while involving only GPU-friendly dense matrix multiplications. Built with the proposed bi-level routing attention, a new general vision transformer, named BiFormer, is then presented. As BiFormer attends to a small subset of relevant tokens in a query adaptive manner without distraction from other irrelevant ones, it enjoys both good performance and high computational efficiency, especially in dense prediction tasks. Empirical results across several computer vision tasks such as image classification, object detection, and semantic segmentation verify the effectiveness of our design. Code is available at https://***/rayleizhu/BiFormer.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Dynamic Conceptional Contrastive Learning for Generalized Category Discovery

Dynamic Conceptional Contrastive Learning for Generalized Ca...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Nan Pu Zhun Zhong Nicu Sebe The Department of Information Engineering and Computer Science University of Trento Trento Italy

Generalized category discovery (GCD) is a recently proposed open-world problem, which aims to automatically cluster partially labeled data. The main challenge is that the unlabeled data contain instances that are not only from known categories of the labeled data but also from novel categories. This leads traditional novel category discovery (NCD) methods to be incapacitated for GCD, due to their assumption of unlabeled data are only from novel categories. One effective way for GCD is applying self-supervised learning to learn discriminate representation for unlabeled data. However, this manner largely ignores underlying relationships between instances of the same concepts (e.g., class, super-class, and sub-class), which results in inferior representation learning. In this paper, we propose a Dynamic Conceptional Contrastive Learning (DCCL)framework, which can effectively improve clustering accuracy by alternately estimating underlying visual conceptions and learning conceptional representation. In addition, we design a dynamic conception generation and update mechanism, which is able to ensure consistent conception learning and thus further facilitate the optimization of DCCL. Extensive experiments show that DCCL achieves new state-of-the-art performances on six generic and fine-grained visual recognition datasets, especially on fine-grained ones. For example, our method significantly surpasses the best competitor by 16.2% on the new classes for the CUB-200 dataset. Code is available at https://***/TPCD/DCCL

关键词：

来源：评论

学校读者我要写书评

暂无评论

GeoVLN: Learning Geometry-Enhanced Visual Representation with Slot Attention for vision-and-Language Navigation

GeoVLN: Learning Geometry-Enhanced Visual Representation wit...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jingyang Huo Qiang Sun Boyan Jiang Haitao Lin Yanwei Fu Shanghai Key Lab of Intelligent Information Processing School of Data Science Fudan University Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence Zhejiang Normal University Jinhua China

Most existing works solving Room-to-Room VLN problem only utilize RGB images and do not consider local context around candidate views, which lack sufficient visual cues about surrounding environment. Moreover, natural language contains complex semantic information thus its correlations with visual inputs are hard to model merely with cross attention. In this paper, we propose GeoVLN, which learns Geometry-enhanced visual representation based on slot attention for robust Visual-and-Language Navigation. The RGB images are compensated with the corresponding depth maps and normal maps predicted by Omnidata as visual inputs. Technically, we introduce a two-stage module that combine local slot attention and CLIP model to produce geometry-enhanced representation from such input. We employ V&L BERT to learn a cross-modal representation that incorporate both language and vision informations. Additionally, a novel multiway attention module is designed, encouraging different phrases of input instruction to exploit the most related features from visual input. Extensive experiments demonstrate the effectiveness of our newly designed modules and show the compelling performance of the proposed method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Meta-Personalizing vision-Language Models to Find Named Instances in Video

Meta-Personalizing Vision-Language Models to Find Named Inst...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Chun-Hsiao Yeh Bryan Russell Josef Sivic Fabian Caba Heilbron Simon Jenni University of California Berkeley Adobe Research Czech Institute of Informatics Robotics and Cybernetics at the Czech Technical University in Prague (CIIRC CTU)

Large-scale vision-language models (VLM) have shown impressive results for language-guided search applications. While these models allow category-level queries, they currently struggle with personalized searches for moments in a video where a specific object instance such as “My dog Biscuit” appears. We present the following three contributions to address this problem. First, we describe a method to meta-personalize a pre-trained VLM, i.e., learning how to learn to personalize a VLM at test time to search in video. Our method extends the VLM's token vocabulary by learning novel word embeddings specific to each instance. To capture only instance-specific features, we represent each instance embedding as a combination of shared and learned global category features. Second, we propose to learn such personalization without explicit human supervision. Our approach automatically identifies moments of named visual instances in video using transcripts and vision-language similarity in the VLM's embedding space. Finally, we introduce This-Is-My, a personal video instance retrieval benchmark. We evaluate our approach on This-Is-My and Deep-Fashion2 and show that we obtain a 15% relative improvement over the state of the art on the latter dataset.

关键词：

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 << < 253 254 255 256 257 258 259 260 261 262 > >>

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：