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

20,994 篇 会议
99 册 图书
85 篇 期刊文献
1 篇 学位论文

馆藏范围

21,178 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,603 篇 工学
- 11,179 篇 计算机科学与技术...
- 2,631 篇 机械工程
- 2,542 篇 软件工程
- 990 篇 光学工程
- 849 篇 电气工程
- 676 篇 控制科学与工程
- 487 篇 信息与通信工程
- 242 篇 仪器科学与技术
- 215 篇 测绘科学与技术
- 159 篇 生物医学工程（可授...
- 150 篇 生物工程
- 139 篇 电子科学与技术（可...
- 69 篇 安全科学与工程
- 67 篇 化学工程与技术
- 55 篇 建筑学
- 53 篇 土木工程
- 43 篇 力学（可授工学、理...
- 41 篇 航空宇航科学与技...
3,462 篇 医学
- 3,452 篇 临床医学
- 41 篇 基础医学(可授医学...
2,483 篇 理学
- 1,247 篇 数学
- 1,213 篇 物理学
- 446 篇 统计学（可授理学、...
- 418 篇 生物学
- 269 篇 系统科学
- 67 篇 化学
424 篇 管理学
- 218 篇 管理科学与工程(可...
- 217 篇 图书情报与档案管...
- 43 篇 工商管理
144 篇 艺术学
- 142 篇 设计学（可授艺术学...
41 篇 法学
31 篇 农学
12 篇 经济学
10 篇 教育学
6 篇 文学
3 篇 军事学

主题

8,072 篇 computer vision
2,879 篇 pattern recognit...
2,859 篇 training
1,808 篇 computational mo...
1,718 篇 visualization
1,478 篇 cameras
1,381 篇 shape
1,374 篇 face recognition
1,364 篇 three-dimensiona...
1,342 篇 feature extracti...
1,269 篇 image segmentati...
1,156 篇 robustness
1,109 篇 semantics
982 篇 layout
978 篇 object detection
953 篇 computer archite...
952 篇 benchmark testin...
931 篇 codes
918 篇 object recogniti...
899 篇 computer science

机构

174 篇 univ sci & techn...
154 篇 carnegie mellon ...
149 篇 univ chinese aca...
144 篇 chinese univ hon...
110 篇 microsoft resear...
104 篇 zhejiang univ pe...
98 篇 swiss fed inst t...
93 篇 tsinghua univ pe...
92 篇 tsinghua univers...
90 篇 microsoft res as...
88 篇 shanghai ai lab ...
83 篇 zhejiang univers...
76 篇 alibaba grp peop...
74 篇 hong kong univ s...
73 篇 university of sc...
72 篇 peking univ peop...
68 篇 shanghai jiao to...
68 篇 university of ch...
66 篇 google res mount...
66 篇 univ oxford oxfo...

作者

83 篇 van gool luc
71 篇 zhang lei
60 篇 timofte radu
49 篇 yang yi
49 篇 luc van gool
48 篇 xiaoou tang
43 篇 darrell trevor
43 篇 tian qi
42 篇 loy chen change
42 篇 sun jian
41 篇 qi tian
37 篇 vasconcelos nuno
37 篇 liu yang
37 篇 chen xilin
37 篇 li fei-fei
36 篇 liu xiaoming
36 篇 shan shiguang
36 篇 li stan z.
36 篇 torralba antonio
33 篇 zhou jie

语言

21,137 篇 英文
31 篇 中文
5 篇 土耳其文
4 篇 其他
2 篇 日文

检索条件"任意字段=2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011"

共 21179 条记录，以下是151-160 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：

SkipPLUS: Skip the First Few Layers to Better Explain vision Transformers

SkipPLUS: Skip the First Few Layers to Better Explain Vision...

引用

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

作者： Mehri, Faridoun Fayyaz, Mohsen Baghshah, Mahdieh Soleymani Pilehvar, Mohammad Taher Sharif Univ Technol Tehran Iran Univ Tehran Tehran Iran Cardiff Univ Cardiff Wales

ISBN: (纸本)9798350365474

Despite their remarkable performance, the explainability of vision Transformers (ViTs) remains a challenge. While forward attention-based token attribution techniques have become popular in text processing, their suitability for ViTs hasn't been extensively explored. In this paper, we compare these methods against state-of-the-art input attribution methods from the vision literature, revealing their limitations due to improper aggregation of information across layers. To address this, we introduce two general techniques, PLUS and SkipPLUS, that can be composed with any input attribution method to more effectively aggregate information across layers while handling noisy layers. Through comprehensive and quantitative evaluations of faithfulness and human interpretability on a variety of ViT architectures and datasets, we demonstrate the effectiveness of PLUS and SkipPLUS, establishing a new state-of-the-art in white-box token attribution. We conclude with a comparative analysis highlighting the strengths and weaknesses of the best versions of all the studied methods. The code used in this paper is freely available at https://***/NightMachinery/SkipPLUS-cvpr-2024.

关键词： Explainable AI Forward Attention-Based Token Attribution Interpretability Neural Network Visualization vision Transformers White-Box Input Attribution Methods xAI

来源：评论

学校读者我要写书评

暂无评论

Task-aligned Part-aware Panoptic Segmentation through Joint Object-Part Representations

Task-aligned Part-aware Panoptic Segmentation through Joint ...

引用

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

作者： de Geus, Daan Dubbelman, Gijs Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9798350353013;9798350353006

Part-aware panoptic segmentation (PPS) requires (a) that each foreground object and background region in an image is segmented and classified, and (b) that all parts within foreground objects are segmented, classified and linked to their parent object. Existing methods approach PPS by separately conducting object-level and part-level segmentation. However, their part-level predictions are not linked to individual parent objects. Therefore, their learning objective is not aligned with the PPS task objective, which harms the PPS performance. To solve this, and make more accurate PPS predictions, we propose Task-Aligned Part-aware Panoptic Segmentation (TAPPS). This method uses a set of shared queries to jointly predict (a) object-level segments, and (b) the part-level segments within those same objects. As a result, TAPPS learns to predict part-level segments that are linked to individual parent objects, aligning the learning objective with the task objective, and allowing TAPPS to leverage joint object-part representations. With experiments, we show that TAPPS considerably outperforms methods that predict objects and parts separately, and achieves new state-of-the-art PPS results.

关键词： computer vision image segmentation panoptic segmentation part segmentation part-aware panoptic segmentation scene understanding semantic segmentation

来源：评论

学校读者我要写书评

暂无评论

Instance-based Max-margin for Practical Few-shot recognition

Instance-based Max-margin for Practical Few-shot Recognition

引用

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

作者： Fu, Minghao Zhu, Ke Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China Nanjing Univ Sch Artificial Intelligence Nanjing Peoples R China

ISBN: (纸本)9798350353006

In order to mimic the human few-shot learning (FSL) ability better and to make FSL closer to real-world applications, this paper proposes a practical FSL (pFSL) setting. pFSL is based on unsupervised pre-trained models (analogous to human prior knowledge) and recognizes many novel classes simultaneously. Compared to traditional FSL, pFSL is simpler in its formulation, easier to evaluate, more challenging and more practical. To cope with the rarity of training examples, this paper proposes IbM2, an instance-based max-margin method not only for the new pFSL setting, but also works well in traditional FSL scenarios. Based on the Gaussian Annulus Theorem, IbM2 converts random noise applied to the instances into a mechanism to achieve maximum margin in the many-way pFSL (or traditional FSL) recognition task. Experiments with various self-supervised pre-training methods and diverse many- or few-way FSL tasks show that IbM2 almost always leads to improvements compared to its respective baseline methods, and in most cases the improvements are significant. With both the new pFSL setting and novel IbM2 method, this paper shows that practical few-shot learning is both viable and promising.

关键词： Few-shot Learning Transfer Learning

来源：评论

学校读者我要写书评

暂无评论

Action-slot: Visual Action-centric Representations for Multi-label Atomic Activity recognition in Traffic Scenes

Action-slot: Visual Action-centric Representations for Multi...

引用

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

作者： Kung, Chi-Hsi Lu, Shu-Wei Tsai, Yi-Hsuan Chen, Yi-Ting Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan Google Mountain View CA USA

ISBN: (纸本)9798350353006

In this paper, we study multi-label atomic activity recognition. Despite the notable progress in action recognition, it is still challenging to recognize atomic activities due to a deficiency in holistic understanding of both multiple road users' motions and their contextual information. In this paper, we introduce Action-slot, a slot attention-based approach that learns visual action-centric representations, capturing both motion and contextual information. Our key idea is to design action slots that are capable of paying attention to regions where atomic activities occur, without the need for explicit perception guidance. To further enhance slot attention, we introduce a background slot that competes with action slots, aiding the training process in avoiding unnecessary focus on background regions devoid of activities. Yet, the imbalanced class distribution in the existing dataset hampers the assessment of rare activities. To address the limitation, we collect a synthetic dataset called TACO, which is four times larger than OATS and features a balanced distribution of atomic activities. To validate the effectiveness of our method, we conduct comprehensive experiments and ablation studies against various action recognition baselines. We also show that the performance of multi-label atomic activity recognition on real-world datasets can be improved by pretraining representations on TACO. Our source code, dataset, and visualization videos are available at https://***/Action-slot/

关键词： Action recognition Autonomous Driving Slot Attention Traffic Activity Video Understanding

来源：评论

学校读者我要写书评

暂无评论

BoQ: A Place is Worth a Bag of Learnable Queries

BoQ: A Place is Worth a Bag of Learnable Queries

引用

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

作者： Ali-Bey, Amar Chaib-Draa, Brahim Giguere, Philippe Univ Laval Dept Comp Sci & Software Engn Quebec City PQ Canada

ISBN: (纸本)9798350353006

In visual place recognition, accurately identifying and matching images of locations under varying environmental conditions and viewpoints remains a significant challenge. In this paper, we introduce a new technique, called Bag-of-Queries (BoQ), which learns a set of global queries, designed to capture universal place-specific attributes. Unlike existing techniques that employ self-attention and generate the queries directly from the input, BoQ employ distinct learnable global queries, which probe the input features via cross-attention, ensuring consistent information aggregation. In addition, this technique provides an interpretable attention mechanism and integrates with both CNN and vision Transformer backbones. The performance of BoQ is demonstrated through extensive experiments on 14 large-scale benchmarks. It consistently outperforms current state-of-the-art techniques including NetVLAD, MixVPR and EigenPlaces. Moreover, despite being a global retrieval technique (one-stage), BoQ surpasses two-stage retrieval methods, such as Patch-NetVLAD, TransVPR and R2Former, all while being orders of magnitude faster and more efficient. The code and model weights are publicly available at https://***/amaralibey/Bag-of-Queries.

关键词： Image Geo-localization Image Retrieval Image-based Localization NetVLAD Place recognition Visual Localization Visual Place recognition VPR

来源：评论

学校读者我要写书评

暂无评论

GreedyViG: Dynamic Axial Graph Construction for Efficient vision GNNs

GreedyViG: Dynamic Axial Graph Construction for Efficient Vi...

引用

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

作者： Munir, Mustafa Avery, William Rahman, Md Mostafijur Marculescu, Radu Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9798350353013;9798350353006

vision graph neural networks (ViG) offer a new avenue for exploration in computer vision. A major bottleneck in ViGs is the inefficient k-nearest neighbor (KNN) operation used for graph construction. To solve this issue, we propose a new method for designing ViGs, Dynamic Axial Graph Construction (DAGC), which is more efficient than KNN as it limits the number of considered graph connections made within an image. Additionally, we propose a novel CNN-GNN architecture, GreedyViG, which uses DAGC. Extensive experiments show that GreedyViG beats existing ViG, CNN, and ViT architectures in terms of accuracy, GMACs, and parameters on image classification, object detection, instance segmentation, and semantic segmentation tasks. Our smallest model, GreedyViG-S, achieves 81.1% top-1 accuracy on ImageNet-1K, 2.9% higher than vision GNN and 2.2% higher than vision HyperGraph Neural Network (ViHGNN), with less GMACs and a similar number of parameters. Our largest model, GreedyViG-B obtains 83.9% top-1 accuracy, 0.2% higher than vision GNN, with a 66.6% decrease in parameters and a 69% decrease in GMACs. GreedyViG-B also obtains the same accuracy as ViHGNN with a 67.3% decrease in parameters and a 71.3% decrease in GMACs. Our work shows that hybrid CNN-GNN architectures not only provide a new avenue for de-signing efficient models, but that they can also exceed the performance of current state-of-the-art models(1).

关键词： Deep Learning Efficient computer vision Graph Neural Networks

来源：评论

学校读者我要写书评

暂无评论

A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neural Network

A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neu...

引用

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

作者： Ma, Ruichen Qiao, Guanchao Liu, Yian Meng, Liwei Ning, Ning Liu, Yang Hu, Shaogang Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Binary neural networks utilize 1-bit quantized weights and activations to reduce both the model's storage demands and computational burden. However, advanced binary architectures still incorporate millions of inefficient and non-hardware-friendly full-precision multiplication operations. A&B BNN is proposed to directly remove part of the multiplication operations in a traditional BNN and replace the rest with an equal number of bit operations, introducing the mask layer and the quantized RPReLU structure based on the normalizer-free network architecture. The mask layer can be removed during inference by leveraging the intrinsic characteristics of BNN with straightforward mathematical transformations to avoid the associated multiplication operations. The quantized RPReLU structure enables more efficient bit operations by constraining its slope to be integer powers of 2. Experimental results achieved 92.30%, 69.35%, and 66.89% on the CIFAR-10, CIFAR-100, and ImageNet datasets, respectively, which are competitive with the state-of-the-art. Ablation studies have verified the efficacy of the quantized RPReLU structure, leading to a 1.14% enhancement on the ImageNet compared to using a fixed slope RLeakyReLU. The proposed add&bit-operation-only BNN offers an innovative approach for hardware-friendly network architecture.

关键词： Binary neural networks Hardware-friendly structure Multiplication-free structure pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Video recognition in Portrait Mode

Video Recognition in Portrait Mode

引用

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

作者： Han, Mingfei Yang, Linjie Jin, Xiaojie Feng, Jiashi Chang, Xiaojun Wang, Heng Bytedance Beijing Peoples R China UTS ReLER Lab AAII Sydney NSW Australia CSIRO Data61 Canberra ACT Australia MBZUAI Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350353006

The creation of new datasets often presents new challenges for video recognition and can inspire novel ideas while addressing these challenges. While existing datasets mainly comprise landscape mode videos, our paper seeks to introduce portrait mode videos to the research community and highlight the unique challenges associated with this video format. With the growing popularity of smartphones and social media applications, recognizing portrait mode videos is becoming increasingly important. To this end, we have developed the first dataset dedicated to portrait mode video recognition, namely PortraitMode-400. The taxonomy of PortraitMode-400 was constructed in a data-driven manner, comprising 400 fine-grained categories, and rigorous quality assurance was implemented to ensure the accuracy of human annotations. In addition to the new dataset, we conducted a comprehensive analysis of the impact of video format (portrait mode versus landscape mode) on recognition accuracy and spatial bias due to the different formats. Furthermore, we designed extensive experiments to explore key aspects of portrait mode video recognition, including the choice of data augmentation, evaluation procedure, the importance of temporal information, and the role of audio modality. Building on the insights from our experimental results and the introduction of PortraitMode-400, our paper aims to inspire further research efforts in this emerging research direction.

关键词： benchmark portrait-mode videos social media videos video recognition

来源：评论

学校读者我要写书评

暂无评论

Learning to Predict Activity Progress by Self-Supervised Video Alignment

Learning to Predict Activity Progress by Self-Supervised Vid...

引用

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

作者： Donahue, Gerard Elhamifar, Ehsan Northwestern Univ Boston MA 02115 USA

ISBN: (纸本)9798350353006

In this paper, we tackle the problem of self-supervised video alignment and activity progress prediction using in-the-wild videos. Our proposed self-supervised representation learning method carefully addresses different action orderings, redundant actions, and background frames to generate improved video representations compared to previous methods. Our model generalizes temporal cycleconsistency learning to allow for more flexibility in determining cycle-consistent neighbors. More specifically, to handle repeated actions, we propose a multi-neighbor cycle consistency and a multi-cycle-back regression loss by finding multiple soft nearest neighbors using a Gaussian Mixture Model. To handle background and redundant frames, we introduce a context-dependent drop function in our framework, discouraging the alignment of droppable frames. On the other hand, to learn from videos of multiple activities jointly, we propose a multi-head crosstask network, allowing us to embed a video and estimate progress without knowing its activity label. Experiments on multiple datasets show that our method outperforms the state-of-the-art for video alignment and progress prediction. (1)

关键词： computer vision in the wild procedural learning progress progress prediction representation learning self-supervised self-supervised representation learning unconstrained unconstrained videos Video Alignment video understanding

来源：评论

学校读者我要写书评

暂无评论

CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor

CLIP as RNN: Segment Countless Visual Concepts without Train...

引用

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

作者： Sun, Shuyang Li, Runjia Torr, Philip Gu, Xiuye Li, Siyang Univ Oxford Oxford England Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350353006

Existing open-vocabulary image segmentation methods require a fine-tuning step on mask labels and/or image-text datasets. Mask labels are labor-intensive, which limits the number of categories in segmentation datasets. Consequently, the vocabulary capacity of pre-trained VLMs is severely reduced after fine-tuning. However, without fine-tuning, VLMs trained under weak image-text supervision tend to make suboptimal mask predictions. To alleviate these issues, we introduce a novel recurrent framework that progressively filters out irrelevant texts and enhances mask quality without training efforts. The recurrent unit is a two-stage segmenter built upon a frozen VLM. Thus, our model retains the VLM's broad vocabulary space and equips it with segmentation ability. Experiments show that our method outperforms not only the training-free counterparts, but also those fine-tuned with millions of data samples, and sets the new state-of-the-art records for both zero-shot semantic and referring segmentation. Concretely, we improve the current record by 28.8, 16.0, and 6.9 mIoU on Pascal VOC, COCO Object, and Pascal Context.

关键词： image segmentation open-vocabulary referring segmentation training-free methods vision-language models

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 << < 12 13 14 15 16 17 18 19 20 21 > >>

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：