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

19,636 篇 会议
48 篇 期刊文献
3 册 图书

馆藏范围

19,686 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

12,586 篇 工学
- 10,354 篇 计算机科学与技术...
- 2,449 篇 机械工程
- 2,010 篇 软件工程
- 815 篇 光学工程
- 598 篇 电气工程
- 433 篇 控制科学与工程
- 329 篇 信息与通信工程
- 211 篇 测绘科学与技术
- 80 篇 生物医学工程（可授...
- 75 篇 生物工程
- 69 篇 电子科学与技术（可...
- 67 篇 仪器科学与技术
- 37 篇 建筑学
- 36 篇 土木工程
- 34 篇 力学（可授工学、理...
- 31 篇 航空宇航科学与技...
- 29 篇 安全科学与工程
- 23 篇 交通运输工程
- 21 篇 化学工程与技术
- 20 篇 材料科学与工程（可...
3,435 篇 医学
- 3,434 篇 临床医学
1,980 篇 理学
- 1,001 篇 数学
- 972 篇 物理学
- 356 篇 统计学（可授理学、...
- 340 篇 生物学
- 235 篇 系统科学
- 26 篇 化学
262 篇 管理学
- 141 篇 管理科学与工程(可...
- 124 篇 图书情报与档案管...
- 26 篇 工商管理
19 篇 法学
12 篇 农学
8 篇 教育学
6 篇 经济学
4 篇 艺术学
2 篇 军事学

主题

7,948 篇 computer vision
2,773 篇 training
2,711 篇 pattern recognit...
1,771 篇 computational mo...
1,660 篇 visualization
1,426 篇 cameras
1,383 篇 three-dimensiona...
1,345 篇 shape
1,236 篇 face recognition
1,222 篇 feature extracti...
1,213 篇 image segmentati...
1,117 篇 robustness
1,094 篇 semantics
977 篇 layout
960 篇 object detection
946 篇 benchmark testin...
944 篇 computer archite...
931 篇 codes
896 篇 computer science
861 篇 deep learning

机构

174 篇 univ sci & techn...
159 篇 carnegie mellon ...
148 篇 univ chinese aca...
144 篇 chinese univ hon...
109 篇 microsoft resear...
103 篇 zhejiang univ pe...
103 篇 tsinghua univ pe...
99 篇 swiss fed inst t...
92 篇 tsinghua univers...
89 篇 microsoft res as...
88 篇 shanghai ai lab ...
81 篇 zhejiang univers...
76 篇 alibaba grp peop...
74 篇 university of sc...
73 篇 hong kong univ s...
73 篇 university of ch...
72 篇 peking univ peop...
68 篇 shanghai jiao to...
66 篇 univ oxford oxfo...
65 篇 google res mount...

作者

79 篇 van gool luc
70 篇 zhang lei
60 篇 timofte radu
48 篇 yang yi
48 篇 luc van gool
46 篇 xiaoou tang
43 篇 darrell trevor
43 篇 tian qi
42 篇 loy chen change
42 篇 sun jian
42 篇 li fei-fei
40 篇 li stan z.
39 篇 qi tian
36 篇 chen xilin
36 篇 torralba antonio
35 篇 vasconcelos nuno
35 篇 shan shiguang
35 篇 liu yang
34 篇 liu xiaoming
34 篇 tao dacheng

语言

19,681 篇 英文
3 篇 中文
2 篇 日文
1 篇 其他

检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015"

共 19687 条记录，以下是891-900 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：

Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detection

Normalizing Flow based Feature Synthesis for Outlier-Aware O...

引用

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

作者： Kumar, Nishant Segvic, Sinisa Eslami, Abouzar Gumhold, Stefan Tech Univ Dresden Dresden Germany Univ Zagreb FER Zagreb Croatia Carl Zeiss Meditec AG Jena Germany

ISBN: (纸本)9798350301298

Real-world deployment of reliable object detectors is crucial for applications such as autonomous driving. However, general-purpose object detectors like Faster R-CNN are prone to providing overconfident predictions for outlier objects. Recent outlier-aware object detection approaches estimate the density of instance-wide features with class-conditional Gaussians and train on synthesized outlier features from their low-likelihood regions. However, this strategy does not guarantee that the synthesized outlier features will have a low likelihood according to the other class-conditional Gaussians. We propose a novel outlier-aware object detection framework that distinguishes outliers from inlier objects by learning the joint data distribution of all inlier classes with an invertible normalizing flow. The appropriate sampling of the flow model ensures that the synthesized outliers have a lower likelihood than inliers of all object classes, thereby modeling a better decision boundary between inlier and outlier objects. Our approach significantly outperforms the state-of-the-art for outlier-aware object detection on both image and video datasets.

关键词： detection recognition: Categorization retrieval

来源：评论

学校读者我要写书评

暂无评论

Divide and Conquer: Answering Questions with Object Factorization and Compositional Reasoning

Divide and Conquer: Answering Questions with Object Factoriz...

引用

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

作者： Chen, Shi Zhao, Qi Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA

ISBN: (纸本)9798350301298

Humans have the innate capability to answer diverse questions, which is rooted in the natural ability to correlate different concepts based on their semantic relationships and decompose difficult problems into sub-tasks. On the contrary, existing visual reasoning methods assume training samples that capture every possible object and reasoning problem, and rely on black-boxed models that commonly exploit statistical priors. They have yet to develop the capability to address novel objects or spurious biases in real-world scenarios, and also fall short of interpreting the rationales behind their decisions. Inspired by humans' reasoning of the visual world, we tackle the aforementioned challenges from a compositional perspective, and propose an integral framework consisting of a principled object factorization method and a novel neural module network. Our factorization method decomposes objects based on their key characteristics, and automatically derives prototypes that represent a wide range of objects. With these prototypes encoding important semantics, the proposed network then correlates objects by measuring their similarity on a common semantic space and makes decisions with a compositional reasoning process. It is capable of answering questions with diverse objects regardless of their availability during training, and overcoming the issues of biased question-answer distributions. In addition to the enhanced generalizability, our framework also provides an interpretable interface for understanding the decision-making process of models. Our code is available at https://***/szzexpoi/POEM.

关键词： and reasoning language vision

来源：评论

学校读者我要写书评

暂无评论

Robust Single Image Reflection Removal Against Adversarial Attacks

Robust Single Image Reflection Removal Against Adversarial A...

引用

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

作者： Song, Zhenbo Zhang, Zhenyuan Zhang, Kaihao Luo, Wenhan Fan, Zhaoxin Ren, Wenqi Lu, Jianfeng Nanjing Univ Sci & Technol Nanjing Peoples R China Australian Natl Univ Canberra ACT Australia Sun Yat Sen Univ Shenzhen Campus Shenzhen Peoples R China Renmin Univ China Beijing Peoples R China

ISBN: (纸本)9798350301298

This paper addresses the problem of robust deep single-image reflection removal (SIRR) against adversarial attacks. Current deep learning based SIRR methods have shown significant performance degradation due to unnoticeable distortions and perturbations on input images. For a comprehensive robustness study, we first conduct diverse adversarial attacks specifically for the SIRR problem, i.e. towards different attacking targets and regions. Then we propose a robust SIRR model, which integrates the cross-scale attention module, the multi-scale fusion module, and the adversarial image discriminator. By exploiting the multi-scale mechanism, the model narrows the gap between features from clean and adversarial images. The image discriminator adaptively distinguishes clean or noisy inputs, and thus further gains reliable robustness. Extensive experiments on Nature, SIR2, and Real datasets demonstrate that our model remarkably improves the robustness of SIRR across disparate scenes.

关键词： Low-level vision

来源：评论

学校读者我要写书评

暂无评论

Switchable Representation Learning Framework with Self-compatibility

Switchable Representation Learning Framework with Self-compa...

引用

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

作者： Wu, Shengsen Bai, Yan Lou, Yihang Linghu, Xiongkun He, Jianzhong Duan, Ling-Yu Peng Cheng Lab Shenzhen Peoples R China Peking Univ Beijing Peoples R China Huawei Technol Shenzhen Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Real-world visual search systems involve deployments on multiple platforms with different computing and storage resources. Deploying a unified model that suits the minimal-constrain platforms leads to limited accuracy. It is expected to deploy models with different capacities adapting to the resource constraints, which requires features extracted by these models to be aligned in the metric space. The method to achieve feature alignments is called "compatible learning". Existing research mainly focuses on the one-to-one compatible paradigm, which is limited in learning compatibility among multiple models. We propose a Switchable representation learning Framework with Self-Compatibility (SFSC). SFSC generates a series of compatible sub-models with different capacities through one training process. The optimization of sub-models faces gradients conflict, and we mitigate this problem from the perspective of the magnitude and direction. We adjust the priorities of sub-models dynamically through uncertainty estimation to co-optimize sub-models properly. Besides, the gradients with conflicting directions are projected to avoid mutual interference. SFSC achieves state-of-the-art performance on the evaluated datasets.

关键词： detection recognition: Categorization retrieval

来源：评论

学校读者我要写书评

暂无评论

Deep Frequency Filtering for Domain Generalization

Deep Frequency Filtering for Domain Generalization

引用

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

作者： Lin, Shiqi Zhang, Zhizheng Huang, Zhipeng Lu, Yan Lan, Cuiling Chu, Peng You, Quanzeng Wang, Jiang Liu, Zicheng Parulkar, Amey Navkal, Viraj Chen, Zhibo Univ Sci & Technol China Hefei Peoples R China Microsoft Albuquerque NM USA Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

Improving the generalization ability of Deep Neural Networks (DNNs) is critical for their practical uses, which has been a longstanding challenge. Some theoretical studies have uncovered that DNNs have preferences for some frequency components in the learning process and indicated that this may affect the robustness of learned features. In this paper, we propose Deep Frequency Filtering (DFF) for learning domain-generalizable features, which is the first endeavour to explicitly modulate the frequency components of different transfer difficulties across domains in the latent space during training. To achieve this, we perform Fast Fourier Transform (FFT) for the feature maps at different layers, then adopt a light-weight module to learn attention masks from the frequency representations after FFT to enhance transferable components while suppressing the components not conducive to generalization. Further, we empirically compare the effectiveness of adopting different types of attention designs for implementing DFF. Extensive experiments demonstrate the effectiveness of our proposed DFF and show that applying our DFF on a plain baseline outperforms the state-of-the-art methods on different domain generalization tasks, including close-set classification and open-set retrieval.

关键词： detection recognition: Categorization retrieval

来源：评论

学校读者我要写书评

暂无评论

GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing

GPT as Psychologist? Preliminary Evaluations for GPT-4V on V...

引用

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

作者： Lu, Hao Niu, Xuesong Wang, Jiyao Wang, Yin Hu, Qingyong Tang, Jiaqi Zhang, Yuting Yuan, Kaishen Huang, Bin Yu, Zitong He, Dengbo Deng, Shuiguang Chen, Hao Chen, Yingcong Shan, Shiguang Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Zhejiang Univ Beijing Inst Gen Artificial Intelligence Hangzhou Peoples R China Zhejiang Univ Hangzhou Peoples R China Great Bay Univ Portsmouth NH USA Beihang Univ Hangzhou Res Inst Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350365474

Multimodal large language models (MLLMs) are designed to process and integrate information from multiple sources, such as text, speech, images, and videos. Despite its success in language understanding, it is critical to evaluate the performance of downstream tasks for better human-centric applications. This paper assesses the application of MLLMs with 5 crucial abilities for affective computing, spanning from visual affective tasks and reasoning tasks. The results show that GPT-4V has high accuracy in facial action unit recognition and micro-expression detection while its general facial expression recognition performance is not accurate. We also highlight the challenges of achieving fine-grained micro-expression recognition and the potential for further study and demonstrate the versatility and potential of GPT-4V for handling advanced tasks in emotion recognition and related fields by integrating with taskrelated agents for more complex tasks, such as heart rate estimation through signal processing. In conclusion, this paper provides valuable insights into the potential applications and challenges of MLLMs in human-centric computing. Our interesting examples are at https://***/Envision-Research/GPT4Affectivity.

关键词： Emotion recognition

来源：评论

学校读者我要写书评

暂无评论

Learning Generative Structure Prior for Blind Text Image Super-resolution

Learning Generative Structure Prior for Blind Text Image Sup...

引用

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

作者： Li, Xiaoming Zuo, Wangmeng Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore Harbin Inst Technol Harbin Peoples R China

ISBN: (纸本)9798350301298

Blind text image super-resolution (SR) is challenging as one needs to cope with diverse font styles and unknown degradation. To address the problem, existing methods perform character recognition in parallel to regularize the SR task, either through a loss constraint or intermediate feature condition. Nonetheless, the high-level prior could still fail when encountering severe degradation. The problem is further compounded given characters of complex structures, e.g., Chinese characters that combine multiple pictographic or ideographic symbols into a single character. In this work, we present a novel prior that focuses more on the character structure. In particular, we learn to encapsulate rich and diverse structures in a StyleGAN and exploit such generative structure priors for restoration. To restrict the generative space of StyleGAN so that it obeys the structure of characters yet remains flexible in handling different font styles, we store the discrete features for each character in a codebook. The code subsequently drives the StyleGAN to generate high-resolution structural details to aid text SR. Compared to priors based on character recognition, the proposed structure prior exerts stronger character-specific guidance to restore faithful and precise strokes of a designated character. Extensive experiments on synthetic and real datasets demonstrate the compelling performance of the proposed generative structure prior in facilitating robust text SR. Our code is available at https://***/csxmli2016/MARCONet.

关键词： Low-level vision

来源：评论

学校读者我要写书评

暂无评论

PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification

PIP-Net: Patch-Based Intuitive Prototypes for Interpretable ...

引用

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

作者： Nauta, Meike Schloetterer, Joerg van Keulen, Maurice Seifert, Christin Univ Twente Enschede Netherlands Univ Duisburg Essen Essen Germany

ISBN: (纸本)9798350301298

Interpretable methods based on prototypical patches recognize various components in an image in order to explain their reasoning to humans. However, existing prototype-based methods can learn prototypes that are not in line with human visual perception, i.e., the same prototype can refer to different concepts in the real world, making interpretation not intuitive. Driven by the principle of explainability-bydesign, we introduce PIP-Net (Patch-based Intuitive Prototypes Network): an interpretable image classification model that learns prototypical parts in a self-supervised fashion which correlate better with human vision. PIP-Net can be interpreted as a sparse scoring sheet where the presence of a prototypical part in an image adds evidence for a class. The model can also abstain from a decision for out-of-distribution data by saying "I haven't seen this before". We only use image-level labels and do not rely on any part annotations. PIP-Net is globally interpretable since the set of learned prototypes shows the entire reasoning of the model. A smaller local explanation locates the relevant prototypes in one image. We show that our prototypes correlate with ground-truth object parts, indicating that PIP-Net closes the "semantic gap" between latent space and pixel space. Hence, our PIP-Net with interpretable prototypes enables users to interpret the decision making process in an intuitive, faithful and semantically meaningful way. Code is available at https://***/M- Nauta/PIPNet.

关键词： Explainable computer vision

来源：评论

学校读者我要写书评

暂无评论

High-fidelity Event-Radiance Recovery via Transient Event Frequency

High-fidelity Event-Radiance Recovery via Transient Event Fr...

引用

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

作者： Han, Jin Asano, Yuta Shi, Boxin Zheng, Yinqiang Sato, Imari Univ Tokyo Grad Sch Informat Sci & Technol Tokyo Japan Natl Inst Informat Tokyo Japan Peking Univ Sch Comp Sci Natl Key Lab Multimedia Informat Proc Beijing Peoples R China Peking Univ Sch Comp Sci Natl Engn Res Ctr Visual Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

High-fidelity radiance recovery plays a crucial role in scene information reconstruction and understanding. Conventional cameras suffer from limited sensitivity in dynamic range, bit depth, and spectral response, etc. In this paper, we propose to use event cameras with bio-inspired silicon sensors, which are sensitive to radiance changes, to recover precise radiance values. We reveal that, under active lighting conditions, the transient frequency of event signals triggering linearly reflects the radiance value. We propose an innovative method to convert the high temporal resolution of event signals into precise radiance values. The precise radiance values yields several capabilities in image analysis. We demonstrate the feasibility of recovering radiance values solely from the transient event frequency (TEF) through multiple experiments.

关键词： Physics-based vision and shape-from-X

来源：评论

学校读者我要写书评

暂无评论

Texts as Images in Prompt Tuning for Multi-Label Image recognition

Texts as Images in Prompt Tuning for Multi-Label Image Recog...

引用

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

作者： Guo, Zixian Dong, Bowen Ji, Zhilong Bai, Jinfeng Guo, Yiwen Zuo, Wangmeng Harbin Inst Technol Harbin Peoples R China Tomorrow Adv Life Beijing Peoples R China Pazhou Lab Guangzhou Peoples R China

ISBN: (纸本)9798350301298

Prompt tuning has been employed as an efficient way to adapt large vision-language pre-trained models (e.g. CLIP) to various downstream tasks in data-limited or label-limited settings. Nonetheless, visual data (e.g., images) is by default prerequisite for learning prompts in existing methods. In this work, we advocate that the effectiveness of image-text contrastive learning in aligning the two modalities (for training CLIP) further makes it feasible to treat texts as images for prompt tuning and introduce TaI prompting. In contrast to the visual data, text descriptions are easy to collect, and their class labels can be directly derived. Particularly, we apply TaI prompting to multi-label image recognition, where sentences in the wild serve as alternatives to images for prompt tuning. Moreover, with TaI, double-grained prompt tuning (TaI-DPT) is further presented to extract both coarse-grained and fine-grained embeddings for enhancing the multi-label recognition performance. Experimental results show that our proposed TaI-DPT outperforms zero-shot CLIP by a large margin on multiple benchmarks, e.g., MS-COCO, VOC2007, and NUS-WIDE, while it can be combined with existing methods of prompting from images to improve recognition performance further. The code is released at https://***/guozix/TaI-DPT.

关键词： continual low-shot meta or long-tail learning Transfer

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 << < 86 87 88 89 90 91 92 93 94 95 > >>

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：