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

29,388 篇 会议
1,371 册 图书
224 篇 期刊文献

馆藏范围

30,981 篇 电子文献
2 种 纸本馆藏

日期分布

学科分类号

17,275 篇 工学
- 13,621 篇 计算机科学与技术...
- 5,199 篇 软件工程
- 2,971 篇 机械工程
- 2,644 篇 光学工程
- 1,410 篇 控制科学与工程
- 1,408 篇 电气工程
- 1,333 篇 信息与通信工程
- 656 篇 生物工程
- 576 篇 仪器科学与技术
- 513 篇 生物医学工程（可授...
- 465 篇 电子科学与技术（可...
- 251 篇 化学工程与技术
- 212 篇 安全科学与工程
- 140 篇 交通运输工程
- 132 篇 建筑学
- 121 篇 材料科学与工程（可...
- 119 篇 土木工程
5,056 篇 理学
- 3,128 篇 物理学
- 2,405 篇 数学
- 824 篇 生物学
- 802 篇 统计学（可授理学、...
- 299 篇 系统科学
- 228 篇 化学
3,831 篇 医学
- 3,799 篇 临床医学
- 186 篇 基础医学(可授医学...
- 140 篇 药学(可授医学、理...
1,060 篇 管理学
- 617 篇 图书情报与档案管...
- 468 篇 管理科学与工程(可...
- 145 篇 工商管理
373 篇 艺术学
- 373 篇 设计学（可授艺术学...
116 篇 法学
81 篇 农学
48 篇 教育学
43 篇 经济学
18 篇 军事学
8 篇 文学

主题

12,593 篇 computer vision
5,697 篇 pattern recognit...
3,181 篇 training
2,263 篇 cameras
2,178 篇 computational mo...
2,116 篇 feature extracti...
2,049 篇 image segmentati...
1,970 篇 visualization
1,967 篇 shape
1,642 篇 robustness
1,491 篇 layout
1,476 篇 three-dimensiona...
1,444 篇 computer science
1,338 篇 computer archite...
1,296 篇 object detection
1,220 篇 semantics
1,142 篇 face recognition
1,107 篇 conferences
1,077 篇 benchmark testin...
1,056 篇 humans

机构

137 篇 univ sci & techn...
134 篇 tsinghua univers...
134 篇 univ chinese aca...
118 篇 chinese univ hon...
101 篇 microsoft resear...
97 篇 zhejiang univers...
94 篇 national laborat...
93 篇 shanghai jiao to...
93 篇 zhejiang univ pe...
85 篇 university of sc...
79 篇 shanghai ai lab ...
78 篇 swiss fed inst t...
65 篇 microsoft res as...
62 篇 adobe research
62 篇 computer vision ...
61 篇 peking univ peop...
58 篇 univ oxford oxfo...
57 篇 google mountain ...
57 篇 hong kong univ s...
56 篇 google res mount...

作者

107 篇 umapada pal
81 篇 van gool luc
68 篇 zhang lei
59 篇 timofte radu
41 篇 yang yi
37 篇 loy chen change
37 篇 hanqing lu
33 篇 liu yang
33 篇 xiaoou tang
32 篇 nassir navab
32 篇 wang liang
30 篇 tian qi
29 篇 h. bischof
29 篇 jan-michael frah...
29 篇 vittorio murino
29 篇 darrell trevor
27 篇 li xin
27 篇 vasconcelos nuno
27 篇 murino vittorio
27 篇 chen chen

语言

30,846 篇 英文
89 篇 中文
67 篇 其他
6 篇 土耳其文
2 篇 日文
2 篇 俄文
1 篇 西班牙文

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

共 30983 条记录，以下是4701-4710 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：

AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval

AxIoU: An Axiomatically Justified Measure for Video Moment R...

引用

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

作者： Togashi, Riku Otani, Mayu Nakashima, Yuta Rahtu, Esa Heikkila, Janne Sakai, Tetsuya Waseda Univ Cyberagent Inc Tokyo Japan Cyberagent Inc Tokyo Japan Osaka Univ Suita Osaka Japan Tampere Univ Tampere Finland Univ Oulu Oulu Finland Waseda Univ Tokyo Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Evaluation measures have a crucial impact on the direction of research. Therefore, it is of utmost importance to develop appropriate and reliable evaluation measures for new applications where conventional measures are not well suited. Video Moment Retrieval (VMR) is one such application, and the current practice is to use R@ K, theta for evaluating VMR systems. However, this measure has two disadvantages. First, it is rank-insensitive: It ignores the rank positions of successfully localised moments in the top-K ranked list by treating the list as a set. Second, it binarizes the Intersection over Union (IoU) of each retrieved video moment using the threshold theta and thereby ignoring fine-grained localisation quality of ranked moments. We propose an alternative measure for evaluating VMR, called Average Max IoU (AxIoU), which is free from the above two problems. We show that AxIoU satisfies two important axioms for VMR evaluation, namely, Invariance against Redundant Moments and Monotonicity with respect to the Best Moment, and also that R@ K, theta satisfies the first axiom only. We also empirically examine how AxIoU agrees with R@ K, theta, as well as its stability with respect to change in the test data and human-annotated temporal boundaries.

关键词： computer vision Current measurement Computational modeling Stability analysis pattern recognition Reliability

来源：评论

学校读者我要写书评

暂无评论

Potential Risk Localization via Weak Labeling out of Blind Spot

Potential Risk Localization via Weak Labeling out of Blind S...

引用

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

作者： Shimomura, Kota Hirakawa, Tsubasa Yamashita, Takayoshi Fujiyoshi, Hironobu Chubu Univ Kasugai Aichi Japan

ISBN: (纸本)9798350365474

Achieving fully autonomous driving requires not only understanding the current surrounding conditions but also predicting how objects that could lead to potential risks may change in the future. Predicting potential risk regions, especially where pedestrians or vehicles might suddenly appear, is crucial for safe autonomous driving and accident avoidance. Constructing datasets annotated with potential risk regions is costly. Therefore, conventional methods have proposed blind spot estimation using depth maps or segmentation masks through automatic labeling. However, these methods are limited in applicability due to their reliance on camera parameters or point clouds. In this study, we propose a method to automatically generate labels from depth maps and segmentation masks and estimate potential risk regions in 2D. Our automatic labeling algorithm relies solely on images, making it applicable to all onboard camera datasets. To demonstrate the effectiveness of our approach, we define regions where pedestrians or vehicles might emerge from blind spots as potential risk regions and annotate them to create a new dataset extended with potential risk region annotations. Experiments using the Cityscapes Dataset show that weakly training with labels generated by our proposed method achieves equal or superior accuracy compared with supervised training with manually annotated ground truth (GT). Furthermore, experiments using the Mapillary Vistas Dataset and BDD100K Dataset demonstrate the versatility of our approach.

关键词： Advanced Driver-Assistance Systems Autonomous Driving computer vision Deep Learning

来源：评论

学校读者我要写书评

暂无评论

HoHoNet: 360 Indoor Holistic Understanding with Latent Horizontal Features

HoHoNet: 360 Indoor Holistic Understanding with Latent Horiz...

引用

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

作者： Sun, Cheng Sun, Min Chen, Hwann-Tzong Natl Tsing Hua Univ Hsinchu Taiwan ASUS AICS Dept Singapore Singapore Joint Res Ctr AI Technol & All Vista Healthcare Taipei Taiwan Aeolus Robot San Francisco CA USA

ISBN: (纸本)9781665445092

We present HoHoNet, a versatile and efficient framework for holistic understanding of an indoor 360-degree panorama using a Latent Horizontal Feature (LHFeat). The compact LHFeat flattens the features along the vertical direction and has shown success in modeling per-column modality for room layout reconstruction. HoHoNet advances in two important aspects. First, the deep architecture is redesigned to run faster with improved accuracy. Second, we propose a novel horizon-to-dense module, which relaxes the per-column output shape constraint, allowing per-pixel dense prediction from LHFeat. HoHoNet is fast: It runs at 52 FPS and 110 FPS with ResNet-50 and ResNet-34 backbones respectively, for modeling dense modalities from a high-resolution 512 x 1024 panorama. HoHoNet is also accurate. On the tasks of layout estimation and semantic segmentation, HoHoNet achieves results on par with current state-of-the-art. On dense depth estimation, HoHoNet outperforms all the prior arts by a large margin.

关键词： Image segmentation computer vision Shape Layout Semantics Estimation Deep architecture

来源：评论

学校读者我要写书评

暂无评论

WarpingGAN: Warping Multiple Uniform Priors for Adversarial 3D Point Cloud Generation

WarpingGAN: Warping Multiple Uniform Priors for Adversarial ...

引用

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

作者： Tang, Yingzhi Qian, Yue Zhang, Qijian Zeng, Yiming Hou, Junhui Zhe, Xuefei City Univ Hong Kong Hong Kong Peoples R China Tencent AI Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose WarpingGAN, an effective and efficient 3D point cloud generation network. Unlike existing methods that generate point clouds by directly learning the mapping functions between latent codes and 3D shapes, Warping-GAN learns a unified local-warping function to warp multiple identical pre-defined priors (i.e., sets of points uniformly distributed on regular 3D grids) into 3D shapes driven by local structure-aware semantics. In addition, we also ingeniously utilize the principle of the discriminator and tailor a stitching loss to eliminate the gaps between different partitions of a generated shape corresponding to different priors for boosting quality. Owing to the novel generating mechanism, WarpingGAN, a single lightweight network after one-time training, is capable of efficiently generating uniformly distributed 3D point clouds with various resolutions. Extensive experimental results demonstrate the superiority of our WarpingGAN over state-of-the-art methods in terms of quantitative metrics, visual quality, and efficiency.

关键词： Point cloud compression Measurement Training Visualization computer vision Three-dimensional displays Codes

来源：评论

学校读者我要写书评

暂无评论

Identifying Ambiguous Similarity Conditions via Semantic Matching

Identifying Ambiguous Similarity Conditions via Semantic Mat...

引用

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

作者： Ye, Han-Jia Shi, Yi Zhan, De-Chuan Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Rich semantics inside an image result in its ambiguous relationship with others, i.e., two images could be similar in one condition but dissimilar in another. Given triplets like "aircraft" is similar to "bird" than "train", Weakly Supervised Conditional Similarity Learning (WS-CSL) learns multiple embeddings to match semantic conditions without explicit condition labels such as "can fly". However, similarity relationships in a triplet are uncertain except providing a condition. For example, the previous comparison becomes invalid once the conditional label changes to "is vehicle". To this end, we introduce a novel evaluation criterion by predicting the comparison's correctness after assigning the learned embeddings to their optimal conditions, which measures how much WS-CSL could cover latent semantics as the supervised model. Furthermore, we propose the Distance Induced Semantic COndition VERification Network (DiscovERNET), which characterizes the instance-instance and triplets-condition relations in a "decompose-and-fuse" manner. To make the learned embeddings cover all semantics, DISCOVERNET utilizes a set module or an additional regularizer over the correspondence between a triplet and a condition. DISCOVERNET achieves state-of-the-art performance on benchmarks like UT-Zappos-50k and Celeb-A w.r.t. different criteria.

关键词： computer vision Semantics Benchmark testing Predictive models Birds pattern recognition Aircraft

来源：评论

学校读者我要写书评

暂无评论

Embedding Arithmetic of Multimodal Queries for Image Retrieval

Embedding Arithmetic of Multimodal Queries for Image Retriev...

引用

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

作者： Couairon, Guillaume Douze, Matthijs Cord, Matthieu Schwenk, Holger Meta AI Menlo Pk CA 94025 USA Sorbonne Univ Paris France Valeo St Ouen Sur Seine France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Latent text representations exhibit geometric regularities, such as the famous analogy: queen is to king what woman is to man. Such structured semantic relations were not demonstrated on image representations. Recent works aiming at bridging this semantic gap embed images and text into a multimodal space, enabling the transfer of text-defined transformations to the image modality. We introduce the SIMAT dataset to evaluate the task of Image Retrieval with Multimodal queries. SIMAT contains 6k images and 18k textual transformation queries that aim at either replacing scene elements or changing pairwise relationships between scene elements. The goal is to retrieve an image consistent with the (source image, text transformation) query. We use an image/text matching oracle (OSCAR) to assess whether the image transformation is successful. The SIMAT dataset will be publicly available. We use SIMAT to evaluate the geometric properties of multimodal embedding spaces trained with an image/text matching objective, like CLIP. We show that vanilla CLIP embeddings are not very well suited to transform images with delta vectors, but that a simple finetuning on the COCO dataset can bring dramatic improvements. We also study whether it is beneficial to leverage pretrained universal sentence encoders (FastText, LASER and LaBSE).

关键词： computer vision conferences Semantics Image retrieval Lasers Transforms Image representation

来源：评论

学校读者我要写书评

暂无评论

FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learning

FReTAL: Generalizing Deepfake Detection using Knowledge Dist...

引用

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

作者： Kim, Minha Tariq, Shahroz Woo, Simon S. Sungkyunkwan Univ Coll Comp & Informat Seoul South Korea Sungkyunkwan Univ Dept Appl Data Sci Seoul South Korea

ISBN: (纸本)9781665448994

As GAN-based video and image manipulation technologies become more sophisticated and easily accessible, there is an urgent need for effective deepfake detection technologies. Moreover, various deepfake generation techniques have emerged over the past few years. While many deepfake detection methods have been proposed, their performance suffers from new types of deepfake methods on which they are not sufficiently trained. To detect new types of deepfakes, the model should learn from additional data without losing its prior knowledge about deepfakes (catastrophic forgetting), especially when new deepfakes are significantly different. In this work, we employ the Representation Learning (ReL) and Knowledge Distillation (KD) paradigms to introduce a transfer learning-based Feature Representation Transfer Adaptation Learning (FReTAL) method. We use FReTAL to perform domain adaptation tasks on new deepfake datasets, while minimizing the catastrophic forgetting. Our student model can quickly adapt to new types of deepfake by distilling knowledge from a pre-trained teacher model and applying transfer learning without using source domain data during domain adaptation. Through experiments on FaceForensics++ datasets, we demonstrate that FReTAL outperforms all baselines on the domain adaptation task with up to 86.97% accuracy on low-quality deepfakes.

关键词： Adaptation models computer vision conferences Computational modeling Transfer learning Data models pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Style Neophile: Constantly Seeking Novel Styles for Domain Generalization

Style Neophile: Constantly Seeking Novel Styles for Domain G...

引用

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

作者： Kang, Juwon Lee, Sohyun Kim, Namyup Kwak, Suha POSTECH Dept CSE Pohang South Korea POSTECH Grad Sch AI Pohang South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper studies domain generalization via domain-invariant representation learning. Existing methods in this direction suppose that a domain can be characterized by styles of its images, and train a network using style-augmented data so that the network is not biased to particular style distributions. However, these methods are restricted to a finite set of styles since they obtain styles for augmentation from a fixed set of external images or by interpolating those of training data. To address this limitation and maximize the benefit of style augmentation, we propose a new method that synthesizes novel styles constantly during training. Our method manages multiple queues to store styles that have been observed so far, and synthesizes novel styles whose distribution is distinct from the distribution of styles in the queues. The style synthesis process is formulated as a monotone submodular optimization, thus can be conducted efficiently by a greedy algorithm. Extensive experiments on four public benchmarks demonstrate that the proposed method is capable of achieving state-of-the-art domain generalization performance.

关键词： Greedy algorithms Training Representation learning computer vision Computational modeling Training data Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

Clipped Hyperbolic Classifiers Are Super-Hyperbolic Classifiers

Clipped Hyperbolic Classifiers Are Super-Hyperbolic Classifi...

引用

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

作者： Guo, Yunhui Wang, Xudong Chen, Yubei Yu, Stella X. UC Berkeley ICSI Berkeley CA 94720 USA Facebook AI Res New York NY USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Hyperbolic space can naturally embed hierarchies, unlike Euclidean space. Hyperbolic Neural Networks (HNNs) exploit such representational power by lifting Euclidean features into hyperbolic space for classification, outperforming Euclidean neural networks (ENNs) on datasets with known semantic hierarchies. However, HNNs underperform ENNs on standard benchmarks without clear hierarchies, greatly restricting HNNs' applicability in practice. Our key insight is that HNNs' poorer general classification performance results from vanishing gradients during backpropagation, caused by their hybrid architecture connecting Euclidean features to a hyperbolic classifier. We propose an effective solution by simply clipping the Euclidean feature magnitude while training HNNs. Our experiments demonstrate that clipped HNNs become super-hyperbolic classifiers: They are not only consistently better than HNNs which already outperform ENNs on hierarchical data, but also on-par with ENNs on MNIST, CIFAR10, CIFAR100 and ImageNet benchmarks, with better adversarial robustness and out-of-distribution detection.

关键词： Training Representation learning computer vision Neural networks Semantics Benchmark testing Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Edges to Shapes to Concepts: Adversarial Augmentation for Robust vision

Edges to Shapes to Concepts: Adversarial Augmentation for Ro...

引用

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

作者： Tripathi, Aditay Singh, Rishubh Chakraborty, Anirban Shenoy, Pradeep Indian Inst Sci CDS Bangalore Karnataka India Google Res India Bangalore Karnataka India

ISBN: (纸本)9798350301298

Recent work has shown that deep vision models tend to be overly dependent on low-level or "texture" features, leading to poor generalization. Various data augmentation strategies have been proposed to overcome this so-called texture bias in DNNs. We propose a simple, lightweight adversarial augmentation technique that explicitly incentivizes the network to learn holistic shapes for accurate prediction in an object classification setting. Our augmentations superpose edgemaps from one image onto another image with shuffled patches, using a randomly determined mixing proportion, with the image label of the edgemap image. To classify these augmented images, the model needs to not only detect and focus on edges but distinguish between relevant and spurious edges. We show that our augmentations significantly improve classification accuracy and robustness measures on a range of datasets and neural architectures. As an example, for ViT-S, We obtain absolute gains on classification accuracy gains up to 6%. We also obtain gains of up to 28% and 8.5% on natural adversarial and out-of-distribution datasets like ImageNet-A (for ViTB) and ImageNet-R (for ViT-S), respectively. Analysis using a range of probe datasets shows substantially increased shape sensitivity in our trained models, explaining the observed improvement in robustness and classification accuracy.

关键词： Deep learning architectures and techniques

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 << < 467 468 469 470 471 472 473 474 475 476 > >>

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：