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

3,310 篇 会议
3 篇 期刊文献

馆藏范围

3,313 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,936 篇 工学
- 1,843 篇 计算机科学与技术...
- 201 篇 软件工程
- 147 篇 机械工程
- 134 篇 光学工程
- 41 篇 生物工程
- 28 篇 信息与通信工程
- 18 篇 电气工程
- 13 篇 控制科学与工程
- 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
360 篇 医学
- 359 篇 临床医学
- 3 篇 基础医学(可授医学...
178 篇 理学
- 137 篇 物理学
- 42 篇 生物学
- 30 篇 数学
- 16 篇 统计学（可授理学、...
- 10 篇 化学
- 3 篇 系统科学
14 篇 管理学
- 7 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 农学
1 篇 经济学

主题

1,738 篇 computer vision
900 篇 training
802 篇 conferences
643 篇 pattern recognit...
482 篇 computational mo...
431 篇 computer archite...
430 篇 task analysis
426 篇 visualization
350 篇 feature extracti...
317 篇 semantics
308 篇 three-dimensiona...
243 篇 neural networks
227 篇 benchmark testin...
222 篇 cameras
183 篇 image segmentati...
177 篇 estimation
168 篇 deep learning
156 篇 measurement
154 篇 object detection
151 篇 data models

机构

40 篇 univ sci & techn...
31 篇 peng cheng lab p...
29 篇 swiss fed inst t...
29 篇 sensetime res pe...
27 篇 university of sc...
27 篇 zhejiang univ pe...
26 篇 univ chinese aca...
26 篇 swiss fed inst t...
25 篇 university of ch...
24 篇 tsinghua univ pe...
24 篇 univ chinese aca...
24 篇 nanyang technol ...
24 篇 sun yat sen univ...
22 篇 peng cheng labor...
20 篇 shanghai ai lab ...
20 篇 korea adv inst s...
19 篇 chinese univ hon...
19 篇 yonsei univ
18 篇 peking univ peop...
18 篇 tsinghua univers...

作者

65 篇 timofte radu
21 篇 loy chen change
18 篇 van gool luc
18 篇 radu timofte
16 篇 zha zheng-jun
14 篇 sun jian
12 篇 fan haoqiang
12 篇 chen wei-ting
12 篇 lei lei
12 篇 qiao yu
11 篇 zheng wei-shi
11 篇 zheng-jun zha
11 篇 liu shuaicheng
11 篇 qi tian
11 篇 luc van gool
11 篇 marcos v. conde
11 篇 liu shuai
11 篇 van de weijer jo...
10 篇 danelljan martin
10 篇 kim seon joo

语言

3,311 篇 英文
2 篇 其他

检索条件"任意字段=2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020"

共 3313 条记录，以下是631-640 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：

MAIR: Multi-view Attention Inverse Rendering with 3D Spatially-Varying Lighting Estimation

MAIR: Multi-view Attention Inverse Rendering with 3D Spatial...

引用

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

作者： Choi, JunYong Lee, SeokYeong Park, Haesol Jung, Seung-Won Kim, Ig-Jae Cho, Junghyun Korea Inst Sci & Technol KIST Seoul South Korea Korea Univ Seoul South Korea Univ Sci & Technol KIST Sch AI Robot Koto City Japan Yonsei Univ Yonsei KIST Convergence Res Inst Seoul South Korea

ISBN: (纸本)9798350301298

We propose a scene-level inverse rendering framework that uses multi-view images to decompose the scene into geometry, a SVBRDF, and 3D spatially-varying lighting. Because multi-view images provide a variety of information about the scene, multi-view images in object-level inverse rendering have been taken for granted. However, owing to the absence of multi-view HDR synthetic dataset, scene-level inverse rendering has mainly been studied using single-view image. We were able to successfully perform scene-level inverse rendering using multi-view images by expanding OpenRooms dataset and designing efficient pipelines to handle multi-view images, and splitting spatially-varying lighting. Our experiments show that the proposed method not only achieves better performance than single-view-based methods, but also achieves robust performance on unseen real-world scene. Also, our sophisticated 3D spatially-varying lighting volume allows for photorealistic object insertion in any 3D location.

关键词： vision + graphics

来源：评论

学校读者我要写书评

暂无评论

Consistency and Accuracy of CelebA Attribute Values

Consistency and Accuracy of CelebA Attribute Values

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Wu, Haiyu Bezold, Grace Günther, Manuel Boult, Terrance King, Michael C. Bowyer, Kevin W. University of Notre Dame United States University of Zurich Switzerland University of Colorado Colorado Springs United States Florida Institute of Technology United States

ISBN: (纸本)9798350302493

We report the first systematic analysis of the experimental foundations of facial attribute classification. Two annotators independently assigning attribute values shows that only 12 of 40 common attributes are assigned values with ≥ 95% consistency, and three (high cheekbones, pointed nose, oval face) have essentially random consistency. Of 5,068 duplicate face appearances in CelebA, attributes have contradicting values on from 10 to 860 of the 5,068 duplicates. Manual audit of a subset of CelebA estimates error rates as high as 40% for (no beard=false), even though the labeling consistency experiment indicates that no beard could be assigned with ≥ 95% consistency. Selecting the mouth slightly open (MSO) for deeper analysis, we estimate the error rate for (MSO=true) at about 20% and (MSO=false) at about 2%. A corrected version of the MSO attribute values enables learning a model that achieves higher accuracy than previously reported for MSO. Corrected values for CelebA MSO are available at https://***/HaiyuWu/CelebAMSO. © 2023 ieee.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

VLPD: Context-Aware Pedestrian Detection via vision-Language Semantic Self-Supervision

VLPD: Context-Aware Pedestrian Detection via Vision-Language...

引用

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

作者： Liu, Mengyin Jiang, Jie Zhu, Chao Yin, Xu-Cheng Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China Tencent Data Platform Dept Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Detecting pedestrians accurately in urban scenes is significant for realistic applications like autonomous driving or video surveillance. However, confusing human-like objects often lead to wrong detections, and small scale or heavily occluded pedestrians are easily missed due to their unusual appearances. To address these challenges, only object regions are inadequate, thus how to fully utilize more explicit and semantic contexts becomes a key problem. Meanwhile, previous context-aware pedestrian detectors either only learn latent contexts with visual clues, or need laborious annotations to obtain explicit and semantic contexts. Therefore, we propose in this paper a novel approach via vision-Language semantic self-supervision for context-aware Pedestrian Detection (VLPD) to model explicitly semantic contexts without any extra annotations. Firstly, we propose a self-supervised vision-Language Semantic (VLS) segmentation method, which learns both fully-supervised pedestrian detection and contextual segmentation via self-generated explicit labels of semantic classes by vision-language models. Furthermore, a self-supervised Prototypical Semantic Contrastive (PSC) learning method is proposed to better discriminate pedestrians and other classes, based on more explicit and semantic contexts obtained from VLS. Extensive experiments on popular benchmarks show that our proposed VLPD achieves superior performances over the previous state-of-the-arts, particularly under challenging circumstances like small scale and heavy occlusion. Code is available at https://***/lmy98129/VLPD.

关键词： detection recognition: Categorization retrieval

来源：评论

学校读者我要写书评

暂无评论

CrisisHateMM: Multimodal Analysis of Directed and Undirected Hate Speech in Text-Embedded Images from Russia-Ukraine Conflict

CrisisHateMM: Multimodal Analysis of Directed and Undirected...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Bhandari, Aashish Shah, Siddhant B. Thapa, Surendrabikram Naseem, Usman Nasim, Mehwish Delhi Technological University Department of CSE India Virginia Tech Department of Computer Science United States School of Computer Science The University of Sydney Australia School of Physics Mathematics and Computing The University of Western Australia Australia College of Science and Engineering Flinders University Australia

ISBN: (纸本)9798350302493

Text-embedded images are frequently used on social media to convey opinions and emotions, but they can also be a medium for disseminating hate speech, propaganda, and extremist ideologies. During the Russia-Ukraine war, both sides used text-embedded images extensively to spread propaganda and hate speech. To aid in moderating such content, this paper introduces CrisisHateMM, a novel multimodal dataset of over 4,700 text-embedded images from the Russia-Ukraine conflict, annotated for hate and non-hate speech. The hate speech is annotated for directed and undirected hate speech, with directed hate speech further annotated for individual, community, and organizational targets. We benchmark the dataset using unimodal and multimodal algorithms, providing insights into the effectiveness of different approaches for detecting hate speech in text-embedded images. Our results show that multimodal approaches outperform unimodal approaches in detecting hate speech, highlighting the importance of combining visual and textual features. This work provides a valuable resource for researchers and practitioners in automated content moderation and social media analysis. The CrisisHateMM dataset and codes are made publicly available at https://***/aabhandari/CrisisHateMM. © 2023 ieee.

关键词： Modal analysis

来源：评论

学校读者我要写书评

暂无评论

Neural Dependencies Emerging from Learning Massive Categories

Neural Dependencies Emerging from Learning Massive Categorie...

引用

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

作者： Feng, Ruili Zheng, Kecheng Zhu, Kai Shen, Yujun Zhao, Jian Huang, Yukun Zhao, Deli Zhou, Jingren Jordan, Michael Zha, Zheng-Jun Univ Sci & Technol China Hefei Peoples R China Ant Grp Hangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9798350301298

This work presents two astonishing findings on neural networks learned for large-scale image classification. 1) Given a well-trained model, the logits predicted for some category can be directly obtained by linearly combining the predictions of a few other categories, which we call neural dependency. 2) Neural dependencies exist not only within a single model, but even between two independently learned models, regardless of their architectures. Towards a theoretical analysis of such phenomena, we demonstrate that identifying neural dependencies is equivalent to solving the Covariance Lasso (CovLasso) regression problem proposed in this paper. Through investigating the properties of the problem solution, we confirm that neural dependency is guaranteed by a redundant logit covariance matrix, which condition is easily met given massive categories, and that neural dependency is highly sparse, implying that one category correlates to only a few others. We further empirically show the potential of neural dependencies in understanding internal data correlations, generalizing models to unseen categories, and improving model robustness with a dependency-derived regularizer. Code to reproduce the results in this paper is available at https://***/RuiLiFeng/Neural-Dependencies.

关键词： computer vision theory

来源：评论

学校读者我要写书评

暂无评论

GazeCaps: Gaze Estimation with Self-Attention-Routed Capsules

GazeCaps: Gaze Estimation with Self-Attention-Routed Capsule...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Wang, Hengfei Oh, Jun O Jin Chang, Hyung Na, Jin Hee Tae, Minwoo Zhang, Zhongqun Choi, Sang-Il University of Birmingham United Kingdom Dankook University Korea Republic of VTouch Inc

ISBN: (纸本)9798350302493

Gaze estimation is the task of estimating eye gaze from facial features. People tend to infer gaze by considering different facial properties from the whole image and their relations. However, existing methods rarely consider these various properties. In this paper, we propose a novel GazeCaps framework that represents various facial properties as different capsules. The capsules respond sensitively to transforms of facial properties by vectorial expression, which is effective for gaze estimation in which many facial components are nonlinearly transformed according to the direction of the head in addition to the perspective. Furthermore, we propose a Self-Attention Routing (SAR) module which can dynamically allocate attention to different capsules that contain important information and can be optimized as a single process without iterations. Through rigorous experiments, we confirm that the proposed method achieves state-of-the-art performance on various benchmarks. We also detail the generalization performance of the proposed model through a cross-dataset evaluation. © 2023 ieee.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Advancing Visual Grounding with Scene Knowledge: Benchmark and Method

Advancing Visual Grounding with Scene Knowledge: Benchmark a...

引用

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

作者： Chen, Zhihong Zhang, Ruifei Song, Yibing Wan, Xiang Li, Guanbin Chinese Univ Hong Kong Shenzhen Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China Tencent AI Lab Shenzhen Peoples R China Fudan Univ AI3 Inst Shanghai Peoples R China

ISBN: (纸本)9798350301298

Visual grounding (VG) aims to establish fine-grained alignment between vision and language. Ideally, it can be a testbed for vision-and-language models to evaluate their understanding of the images and texts and their reasoning abilities over their joint space. However, most existing VG datasets are constructed using simple description texts, which do not require sufficient reasoning over the images and texts. This has been demonstrated in a recent study [27], where a simple LSTM-based text encoder without pretraining can achieve state-of-the-art performance on mainstream VG datasets. Therefore, in this paper, we propose a novel benchmark of Scene Knowledge-guided Visual Grounding (SK-VG), where the image content and referring expressions are not sufficient to ground the target objects, forcing the models to have a reasoning ability on the long-form scene knowledge. To perform this task, we propose two approaches to accept the triple-type input, where the former embeds knowledge into the image features before the image-query interaction;the latter leverages linguistic structure to assist in computing the image-text matching. We conduct extensive experiments to analyze the above methods and show that the proposed approaches achieve promising results but still leave room for improvement, including performance and interpretability. The dataset and code are available at https://***/zhjohnchan/SK-VG.

关键词： language reasoning vision

来源：评论

学校读者我要写书评

暂无评论

A Data-Centric Solution to NonHomogeneous Dehazing via vision Transformer

A Data-Centric Solution to NonHomogeneous Dehazing via Visio...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Liu, Yangyi Liu, Huan Li, Liangyan Wu, Zijun Chen, Jun McMaster University Hamilton Canada China Telecom Research Institute Shanghai China

ISBN: (纸本)9798350302493

Recent years have witnessed an increased interest in image dehazing. Many deep learning methods have been proposed to tackle this challenge, and have made significant accomplishments dealing with homogeneous haze. However, these solutions cannot maintain comparable performance when they are applied to images with non-homogeneous haze, e.g., NH-HAZE23 dataset introduced by NTIRE challenge. One of the reasons for such failures is that non-homogeneous haze does not obey one of the assumptions that is required for modeling homogeneous haze. In addition, a large number of pairs of non-homogeneous hazy image and the clean counterpart is required using traditional end-to-end training approaches, while NH-HAZE23 dataset is of limited quantities. Although it is possible to augment the NH-HAZE23 dataset by leveraging other non-homogeneous dehazing datasets, we observe that it is necessary to design a proper data-preprocessing technique that reduces the distribution gaps between the target dataset and the augmented one. This finding indeed aligns with the essence of data-centric AI. With a novel network architecture and a principled data-preprocessing approach that systematically enhances data quality, we present an innovative dehazing method. Specifically, we apply RGB-channel-wise transformations on the augmented datasets, and incorporate the state-of-the-art transformers as the backbone in the two-branch framework. We conduct extensive experiments and ablation studies to demonstrate the effectiveness of our proposed method. The source code is available at https://***/yangyiliu21/ntire2023-ITBdehaze. © 2023 ieee.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

CAT-NeRF: Constancy-Aware Tx2Former for Dynamic Body Modeling

CAT-NeRF: Constancy-Aware Tx2Former for Dynamic Body Modelin...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Zhu, Haidong Zheng, Zhaoheng Zheng, Wanrong Nevatia, Ram University of Southern California United States

ISBN: (纸本)9798350302493

This paper addresses the problem of human rendering in the video with temporal appearance constancy. Reconstructing dynamic body shapes with volumetric neural rendering methods, such as NeRF, requires finding the correspondence of the points in the canonical and observation space, which demands understanding human body shape and motion. Some methods use rigid transformation, such as SE(3), which cannot precisely model each frame's unique motion and muscle movements. Others generate the transformation for each frame with a trainable network, such as neural blend weight field or translation vector field, which does not consider the appearance constancy of general body shape. In this paper, we propose CAT-NeRF for self-awareness of appearance constancy with Tx2Former, a novel way to combine two Transformer layers, to separate appearance constancy and uniqueness. Appearance constancy models the general shape across the video, and uniqueness models the unique patterns for each frame. We further introduce a novel Covariance Loss to limit the correlation between each pair of appearance uniquenesses to ensure the frame-unique pattern is maximally captured in appearance uniqueness. We assess our method on H36M and ZJU-MoCap and show state-of-the-art performance. © 2023 ieee.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Cross-Domain Image Captioning with Discriminative Finetuning

Cross-Domain Image Captioning with Discriminative Finetuning

引用

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

作者： Dessi, Roberto Bevilacqua, Michele Gualdoni, Eleonora Carraz Rakotonirina, Nathanael Franzon, Francesca Baroni, Marco UPF Meta AI Barcelona Spain Samaya AI Mountain View CA USA UPF Barcelona Spain UPF ICREA Barcelona Spain

ISBN: (纸本)9798350301298

Neural captioners are typically trained to mimic human-generated references without optimizing for any specific communication goal, leading to problems such as the generation of vague captions. In this paper, we show that fine-tuning an out-of-the-box neural captioner with a self-supervised discriminative communication objective helps to recover a plain, visually descriptive language that is more informative about image contents. Given a target image, the system must learn to produce a description that enables an out-of-the-box text-conditioned image retriever to identify such image among a set of candidates. We experiment with the popular ClipCap captioner, also replicating the main results with BLIP. In terms of similarity to ground-truth human descriptions, the captions emerging from discriminative finetuning lag slightly behind those generated by the non-finetuned model, when the latter is trained and tested on the same caption dataset. However, when the model is used without further tuning to generate captions for out-of-domain datasets, our discriminatively-finetuned captioner generates descriptions that resemble human references more than those produced by the same captioner wihtout finetuning. We further show that, on the Conceptual Captions dataset, discriminatively finetuned captions are more helpful than either vanilla ClipCap captions or ground-truth captions for human annotators tasked with an image discrimination task.(1)

关键词： language reasoning vision

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 << < 60 61 62 63 64 65 66 67 68 69 > >>

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：