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

20,798 篇 会议
88 篇 期刊文献
65 册 图书

馆藏范围

20,950 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,275 篇 工学
- 10,923 篇 计算机科学与技术...
- 2,484 篇 机械工程
- 2,307 篇 软件工程
- 913 篇 光学工程
- 771 篇 电气工程
- 556 篇 控制科学与工程
- 405 篇 信息与通信工程
- 210 篇 测绘科学与技术
- 131 篇 生物医学工程（可授...
- 104 篇 电子科学与技术（可...
- 100 篇 生物工程
- 92 篇 仪器科学与技术
- 56 篇 化学工程与技术
- 52 篇 建筑学
- 48 篇 土木工程
- 44 篇 安全科学与工程
- 38 篇 力学（可授工学、理...
- 38 篇 航空宇航科学与技...
- 35 篇 交通运输工程
3,457 篇 医学
- 3,449 篇 临床医学
- 34 篇 基础医学(可授医学...
2,315 篇 理学
- 1,154 篇 数学
- 1,132 篇 物理学
- 417 篇 统计学（可授理学、...
- 386 篇 生物学
- 252 篇 系统科学
- 57 篇 化学
353 篇 管理学
- 184 篇 图书情报与档案管...
- 176 篇 管理科学与工程(可...
- 32 篇 工商管理
28 篇 法学
20 篇 农学
15 篇 教育学
9 篇 经济学
8 篇 艺术学
5 篇 文学
5 篇 军事学

主题

8,203 篇 computer vision
3,010 篇 pattern recognit...
2,732 篇 training
1,769 篇 computational mo...
1,657 篇 visualization
1,483 篇 cameras
1,415 篇 shape
1,369 篇 three-dimensiona...
1,369 篇 face recognition
1,285 篇 image segmentati...
1,272 篇 feature extracti...
1,178 篇 robustness
1,090 篇 semantics
1,040 篇 layout
1,007 篇 object detection
975 篇 object recogniti...
969 篇 computer science
946 篇 computer archite...
946 篇 benchmark testin...
931 篇 codes

机构

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

作者

80 篇 van gool luc
71 篇 zhang lei
59 篇 timofte radu
48 篇 yang yi
47 篇 xiaoou tang
44 篇 darrell trevor
43 篇 tian qi
43 篇 luc van gool
42 篇 loy chen change
42 篇 sun jian
42 篇 li fei-fei
40 篇 qi tian
39 篇 li stan z.
37 篇 liu yang
37 篇 chen xilin
36 篇 shan shiguang
35 篇 liu xiaoming
35 篇 vasconcelos nuno
35 篇 torralba antonio
32 篇 zhou jie

语言

20,928 篇 英文
14 篇 中文
6 篇 其他
2 篇 日文
2 篇 土耳其文

检索条件"任意字段=2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009"

共 20951 条记录，以下是201-210 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：

Low-Resource vision Challenges for Foundation Models

Low-Resource Vision Challenges for Foundation Models

引用

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

作者： Zhang, Yunhua Doughty, Hazel Snoek, Cees G. M. Univ Amsterdam Amsterdam Netherlands Leiden Univ Leiden Netherlands

ISBN: (纸本)9798350353006

Low-resource settings are well-established in natural language processing, where many languages lack sufficient data for deep learning at scale. However, low-resource problems are under-explored in computer vision. In this paper, we address this gap and explore the challenges of low-resource image tasks with vision foundation models. We first collect a benchmark of genuinely low-resource image data, covering historic maps, circuit diagrams, and mechanical drawings. These low-resource settings all share three challenges: data scarcity, fine-grained differences, and the distribution shift from natural images to the specialized domain of interest. While existing foundation models have shown impressive generalizability, we find they cannot transfer well to our low-resource tasks. To begin to tackle the challenges of low-resource vision, we introduce one simple baseline per challenge. Specifically, we i) enlarge the data space by generative models, ii) adopt the best sub-kernels to encode local regions for fine-grained difference discovery and iii) learn attention for specialized domains. Experiments on our three low-resource tasks demonstrate our proposals already provide a better baseline than transfer learning, data augmentation, and fine-grained methods. This highlights the unique characteristics and challenges of low-resource vision for foundation models that warrant further investigation. Project page: https://***/Low-Resource-vision/.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Attentive Illumination Decomposition Model for Multi-Illuminant White Balancing

Attentive Illumination Decomposition Model for Multi-Illumin...

引用

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

作者： Kim, Dongyoung Kim, Jinwoo Yu, Junsang Kim, Seon Joo Yonsei Univ Seoul South Korea Samsung Adv Inst Technol Suwon South Korea

ISBN: (纸本)9798350353006

White balance (WB) algorithms in many commercial cameras assume single and uniform illumination, leading to undesirable results when multiple lighting sources with different chromaticities exist in the scene. Prior research on multi-illuminant WB typically predicts illumination at the pixel level without fully grasping the scene's actual lighting conditions, including the number and color of light sources. This often results in unnatural outcomes lacking in overall consistency. To handle this problem, we present a deep white balancing model that leverages the slot attention, where each slot is in charge of representing individual illuminants. This design enables the model to generate [ chromaticities and weight maps for individual illuminants, which are then fused to compose the final illumination map. Furthermore, we propose the centroid-matching loss, which regulates the activation of each slot based on the color range, thereby enhancing the model to separate illumination more effectively. Our method achieves the state-of-the-art performance on both single- and multi-illuminant WB benchmarks, and also offers additional information such as the number of illuminants in the scene and their chromaticity. This capability allows for illumination editing, an application not feasible with prior methods.

关键词： Low level vision Photography White Balancing

来源：评论

学校读者我要写书评

暂无评论

Not All Prompts Are Secure: A Switchable Backdoor Attack Against Pre-trained vision Transfomers

Not All Prompts Are Secure: A Switchable Backdoor Attack Aga...

引用

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

作者： Yang, Sheng Bai, Jiawang Gao, Kuofeng Yang, Yong Li, Yiming Xia, Shu-Tao Tsinghua Univ Beijing Peoples R China Tencent Secur Platform Dept Shenzhen Peoples R China Zhejiang Univ Hangzhou Peoples R China Peng Cheng Lab Res Ctr Artificial Intelligence Shenzhen Peoples R China

ISBN: (纸本)9798350353006

Given the power of vision transformers, a new learning paradigm, pretraining and then prompting, makes it more efficient and effective to address downstream visual recognition tasks. In this paper, we identify a novel security threat towards such a paradigm from the perspective of backdoor attacks. Specifically, an extra prompt token, called the switch token in this work, can turn the backdoor mode on, i.e., converting a benign model into a backdoored one. Once under the backdoor mode, a specific trigger can force the model to predict a target class. It poses a severe risk to the users of cloud API, since the malicious behavior can not be activated and detected under the benign mode, thus making the attack very stealthy. To attack a pretrained model, our proposed attack, named SWARM, learns a trigger and prompt tokens including a switch token. They are optimized with the clean loss which encourages the model always behaves normally even the trigger presents, and the backdoor loss that ensures the backdoor can be activated by the trigger when the switch is on. Besides, we utilize the crossmode feature distillation to reduce the effect of the switch token on clean samples. The experiments on diverse visual recognition tasks confirm the success of our switchable backdoor attack, i.e., achieving 95%+ attack success rate, and also being hard to be detected and removed. Our code is available at https://***/20000yshust/SWARM.

关键词： Backdoor computer vision Parameter-Efficient Fine Tuning Switchable vision Transformers Visual Prompting

来源：评论

学校读者我要写书评

暂无评论

PeVL: Pose-Enhanced vision-Language Model for Fine-Grained Human Action recognition

PeVL: Pose-Enhanced Vision-Language Model for Fine-Grained H...

引用

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

作者： Zhang, Haosong Leong, Mei Chee Li, Liyuan Lin, Weisi Nanyang Technol Univ Inst Infocomm Res I2R A STAR Singapore Singapore

ISBN: (纸本)9798350353006

Recent progress in vision-Language (VL) foundation models has revealed the great advantages of cross-modality learning. However, due to a large gap between vision and text, they might not be able to sufficiently utilize the benefits of cross-modality information. In the field of human action recognition, the additional pose modality may bridge the gap between vision and text to improve the effectiveness of cross-modality learning. In this paper, we propose a novel framework, called Pose-enhanced vision-Language (PeVL) model, to adapt the VL model with pose modality to learn effective knowledge of fine-grained human actions. Our PeVL model includes two novel components: an Unsymmetrical Cross-Modality Refinement (UCMR) block and a Semantic-Guided Multi-level Contrastive (SGMC) module. The UCMR block includes Pose-guided Visual Refinement (P2V-R) and Visual-enriched Pose Refinement (V2P-R) for effective cross-modality learning. The SGMC module includes Multi-level Contrastive Associations of vision-text and pose-text at both action and sub-action levels, and a Semantic-Guided Loss, enabling effective contrastive learning with text. Built upon a pre-trained VL foundation model, our model integrates trainable adapters and can be trained end-to-end. Our novel PeVL design over VL foundation model yields remarkable performance gains on four fine-grained human action recognition datasets, achieving a new SOTA with a significantly small number of FLOPs for low-cost re-training.(1)

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Validating Privacy-Preserving Face recognition under a Minimum Assumption

Validating Privacy-Preserving Face Recognition under a Minim...

引用

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

作者： Zhang, Hui Dong, Xingbo Lai, YenLung Zhou, Ying Zhang, Xiaoyan Lv, Xingguo Jin, Zhe Li, Xuejun Anhui Univ Anhui Prov Key Lab Secure Artificial Intelligence Hefei Anhui Peoples R China

ISBN: (纸本)9798350353006

The widespread use of cloud-based face recognition technology raises privacy concerns, as unauthorized access to face images can expose personal information or be exploited for fraudulent purposes. In response, privacy-preserving face recognition (PPFR) schemes have emerged to hide visual information and thwart unauthorized access. However, the validation methods employed by these schemes often rely on unrealistic assumptions, leaving doubts about their true effectiveness in safeguarding facial privacy. In this paper, we introduce a new approach to privacy validation called Minimum Assumption Privacy Protection Validation (Map(2)V). This is the first exploration of formulating a privacy validation method utilizing deep image priors and zeroth-order gradient estimation, with the potential to serve as a general framework for PPFR evaluation. Building upon Map(2)V, we comprehensively validate the privacy-preserving capability of PPFRs through a combination of human and machine vision. The experiment results and analysis demonstrate the effectiveness and generalizability of the proposed Map(2)V, showcasing its superiority over native privacy validation methods from PPFR works of literature. Additionally, this work exposes privacy vulnerabilities in evaluated state-of-the-art PPFR schemes, laying the foundation for the subsequent effective proposal of countermeasures. The source code is available at https://***/Beauty9882/MAP2V.

关键词： Deep Generative Prior Minimum Assumption Privacy Validation Privacy-Preserving Face recognition Zeroth-order Gradient Estimation

来源：评论

学校读者我要写书评

暂无评论

OMNIPARSER: A Unified Framework for Text Spotting, Key Information Extraction and Table recognition

OMNIPARSER: A Unified Framework for Text Spotting, Key Infor...

引用

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

作者： Wan, Jianqiang Song, Sibo Yu, Wenwen Liu, Yuliang Cheng, Wenqing Huang, Fei Bai, Xiang Yao, Cong Yang, Zhibo Alibaba Grp Hangzhou Peoples R China Huazhong Univ Sci & Technol Hangzhou Peoples R China

ISBN: (纸本)9798350353006

Recently, visually-situated text parsing (VsTP) has experienced notable advancements, driven by the increasing demand for automated document understanding and the emergence of Generative Large Language Models (LLMs) capable of processing document-based questions. Various methods have been proposed to address the challenging problem of VsTP. However, due to the diversified targets and heterogeneous schemas, previous works usually design task-specific architectures and objectives for individual tasks, which inadvertently leads to modal isolation and complex workflow. In this paper, we propose a unified paradigm for parsing visually-situated text across diverse scenarios. Specifically, we devise a universal model, called OmniParser, which can simultaneously handle three typical visually-situated text parsing tasks: text spotting, key information extraction, and table recognition. In OmniParser, all tasks share the unified encoder-decoder architecture, the unified objective: point-conditioned text generation, and the unified input&output representation: prompt & structured sequences. Extensive experiments demonstrate that the proposed OmniParser achieves state-of-the-art (SOTA) or highly competitive performances on 7 datasets for the three visually-situated text parsing tasks, despite its unified, concise design. The code is available at AdvancedLiterateMachinery.

关键词： key information extraction table recognition text spotting unified framework

来源：评论

学校读者我要写书评

暂无评论

From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with vision-Language Models

From Pixels to Graphs: Open-Vocabulary Scene Graph Generatio...

引用

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

作者： Li, Rongjie Zhang, Songyang Lin, Dahua Chen, Kai He, Xuming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (纸本)9798350353006

Scene graph generation (SGG) aims to parse a visual scene into an intermediate graph representation for down-stream reasoning tasks. Despite recent advancements, existing methods struggle to generate scene graphs with novel visual relation concepts. To address this challenge, we introduce a new open-vocabulary SGG framework based on sequence generation. Our framework leverages vision-language pre-trained models (VLM) by incorporating an image-to-graph generation paradigm. Specifically, we generate scene graph sequences via image-to-text generation with VLM and then construct scene graphs from these sequences. By doing so, we harness the strong capabilities of VLM for open-vocabulary SGG and seamlessly integrate explicit relational modeling for enhancing the VL tasks. Experimental results demonstrate that our design not only achieves superior performance with an open vocabulary but also enhances downstream vision-language task performance through explicit relation modeling knowledge.

关键词： Scene Graph Generation Scene Understanding vision-language

来源：评论

学校读者我要写书评

暂无评论

PELA: Learning Parameter-Efficient Models with Low-Rank Approximation

PELA: Learning Parameter-Efficient Models with Low-Rank Appr...

引用

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

作者： Guo, Yangyang Wang, Guangzhi Kankanhalli, Mohan Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9798350353006

Applying a pre-trained large model to downstream tasks is prohibitive under resource-constrained conditions. Re-cent dominant approaches for addressing efficiency issues involve adding a few learnable parameters to the fixed backbone model. This strategy, however, leads to more challenges in loading large models for downstream fine-tuning with limited resources. In this paper, we propose a novel method for increasing the parameter efficiency of pre-trained models by introducing an intermediate pre-training stage. To this end, we first employ low-rank approximation to compress the original large model and then devise a feature distillation module and a weight perturbation regularization module. These modules are specifically designed to enhance the low-rank model. In particular, we update only the low-rank model while freezing the backbone parameters during pre-training. This allows for direct and efficient utilization of the low-rank model for downstream fine-tuning tasks. The proposed method achieves both efficiencies in terms of required parameters and computation time while maintaining comparable results with minimal modifications to the backbone architecture. Specifically, when applied to three vision-only and one vision-language Transformer models, our approach often demonstrates a merely similar to 0.6 point decrease in performance while reducing the original parameter size by 1/3 to 2/3. We release our code at link.

关键词： Knowledge Distillation Low-rank Approximation vision-Language

来源：评论

学校读者我要写书评

暂无评论

Test-Time Adaptation for Depth Completion

Test-Time Adaptation for Depth Completion

引用

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

作者： Park, Hyoungseob Gupta, Anjali Wong, Alex Yale Vision Lab New Haven CT 06501 USA

ISBN: (纸本)9798350353006

It is common to observe performance degradation when transferring models trained on some (source) datasets to target testing data due to a domain gap between them. Existing methods for bridging this gap, such as domain adaptation (DA), may require the source data on which the model was trained (often not available), while others, i.e., source-free DA, require many passes through the testing data. We propose an online test-time adaptation method for depth completion, the task of inferring a dense depth map from a single image and associated sparse depth map, that closes the performance gap in a single pass. We first present a study on how the domain shift in each data modality affects model performance. Based on our observations that the sparse depth modality exhibits a much smaller covariate shift than the image, we design an embedding module trained in the source domain that preserves a mapping from features encoding only sparse depth to those encoding image and sparse depth. During test time, sparse depth features are projected using this map as a proxy for source domain features and are used as guidance to train a set of auxiliary parameters (i.e., adaptation layer) to align image and sparse depth features from the target test domain to that of the source domain. We evaluate our method on indoor and outdoor scenarios and show that it improves over baselines by an average of 21.1%. Code available at ***/seobbro/TTA-depth-completion.

关键词： 3D computer vision Depth completion Depth estimation Multi-modal fusion Test time adaptation

来源：评论

学校读者我要写书评

暂无评论

THRONE: An Object-based Hallucination Benchmark for the Free-form Generations of Large vision-Language Models

THRONE: An Object-based Hallucination Benchmark for the Free...

引用

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

作者： Kaul, Prannay Li, Zhizhong Yang, Hao Dukler, Yonatan Swaminathan, Ashwin Taylor, C. J. Soatto, Stefano Univ Oxford VGG Oxford England AWS AI Labs Oxford England

ISBN: (纸本)9798350353006

Mitigating hallucinations in large vision-language models (LVLMs) remains an open problem. Recent benchmarks do not address hallucinations in open-ended free-form responses, which we term "Type I hallucinations". Instead, they focus on hallucinations responding to very specific question formats-typically a multiple-choice response regarding a particular object or attribute-which we term "Type II hallucinations". Additionally, such benchmarks often require external API calls to models which are subject to change. In practice, we observe that a reduction in Type II hallucinations does not lead to a reduction in Type I hallucinations but rather that the two forms of hallucinations are often anti-correlated. To address this, we pro-pose THRONE, a novel object-based automatic framework for quantitatively evaluating Type I hallucinations in LVLM free-form outputs. We use public language models (LMs) to identify hallucinations in LVLM responses and compute informative metrics. By evaluating a large selection of recent LVLMs using public datasets, we show that an improvement in existing metrics do not lead to a reduction in Type I hallucinations, and that established benchmarks for measuring Type I hallucinations are incomplete. Finally, we provide a simple and effective data augmentation method to reduce Type I and Type II hallucinations as a strong baseline.

关键词： benchmark hallucination large language model large vision-language model LLM LVLM

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 << < 17 18 19 20 21 22 23 24 25 26 > >>

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：