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

23,134 篇 会议
90 篇 期刊文献
15 册 图书

馆藏范围

23,238 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,629 篇 工学
- 11,160 篇 计算机科学与技术...
- 3,336 篇 软件工程
- 2,413 篇 机械工程
- 1,663 篇 光学工程
- 1,204 篇 电气工程
- 972 篇 控制科学与工程
- 738 篇 信息与通信工程
- 381 篇 仪器科学与技术
- 322 篇 生物工程
- 239 篇 生物医学工程（可授...
- 188 篇 电子科学与技术（可...
- 109 篇 化学工程与技术
- 104 篇 安全科学与工程
- 99 篇 测绘科学与技术
- 85 篇 建筑学
- 82 篇 土木工程
- 82 篇 交通运输工程
- 56 篇 力学（可授工学、理...
3,695 篇 医学
- 3,683 篇 临床医学
- 76 篇 基础医学(可授医学...
3,136 篇 理学
- 1,880 篇 物理学
- 1,603 篇 数学
- 547 篇 统计学（可授理学、...
- 466 篇 生物学
- 243 篇 系统科学
- 107 篇 化学
491 篇 管理学
- 290 篇 图书情报与档案管...
- 212 篇 管理科学与工程(可...
- 74 篇 工商管理
252 篇 艺术学
- 251 篇 设计学（可授艺术学...
58 篇 法学
38 篇 农学
25 篇 教育学
19 篇 经济学
10 篇 军事学
3 篇 文学

主题

10,395 篇 computer vision
3,893 篇 pattern recognit...
3,105 篇 training
2,097 篇 computational mo...
1,894 篇 visualization
1,797 篇 cameras
1,481 篇 feature extracti...
1,479 篇 three-dimensiona...
1,468 篇 shape
1,445 篇 image segmentati...
1,288 篇 robustness
1,229 篇 computer archite...
1,214 篇 semantics
1,112 篇 conferences
1,112 篇 benchmark testin...
1,102 篇 layout
1,093 篇 object detection
1,085 篇 computer science
1,022 篇 codes
903 篇 face recognition

机构

137 篇 univ sci & techn...
124 篇 univ chinese aca...
121 篇 chinese univ hon...
109 篇 carnegie mellon ...
108 篇 tsinghua univers...
105 篇 microsoft resear...
97 篇 zhejiang univ pe...
95 篇 swiss fed inst t...
85 篇 university of sc...
84 篇 zhejiang univers...
81 篇 shanghai ai lab ...
79 篇 university of ch...
75 篇 shanghai jiao to...
70 篇 microsoft res as...
70 篇 alibaba grp peop...
66 篇 adobe research
65 篇 national laborat...
64 篇 peking univ peop...
61 篇 univ oxford oxfo...
60 篇 peng cheng labor...

作者

80 篇 van gool luc
72 篇 timofte radu
65 篇 zhang lei
49 篇 luc van gool
40 篇 yang yi
37 篇 loy chen change
36 篇 li stan z.
34 篇 chen chen
34 篇 qi tian
33 篇 liu yang
33 篇 xiaoou tang
33 篇 murino vittorio
33 篇 tian qi
32 篇 pascal fua
30 篇 sun jian
29 篇 darrell trevor
28 篇 li xin
28 篇 hanqing lu
28 篇 ying shan
28 篇 nuno vasconcelos

语言

23,160 篇 英文
52 篇 其他
20 篇 中文
5 篇 土耳其文
2 篇 日文

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

共 23239 条记录，以下是21-30 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：

Focusing on what to Decode and what to Train: SOV Decoding with Specific Target Guided DeNoising and vision Language Advisor

Focusing on what to Decode and what to Train: SOV Decoding w...

引用

2025 ieee/cvf Winter conference on Applications of computer vision, WACV 2025

作者： Chen, Junwen Wang, Yingcheng Yanai, Keiji The University of Electro-Communications Department of Informatics Tokyo Japan

ISBN: (纸本)9798331510831

Recent transformer-based methods achieve notable gains in the Human-object Interaction Detection (HOID) task by leveraging the detection of DETR and the prior knowledge of vision-Language Model (VLM). However, these methods suffer from extended training times and complex optimization due to the entanglement of object detection and HOI recognition during the decoding process. Especially, the query embeddings used to predict both labels and boxes suffer from ambiguous representations, and the gap between the prediction of HOI labels and verb labels is not considered. To address these challenges, we introduce SOV-STG-VLA with three key components: Subject-Object-Verb (SOV) decoding, Specific Target Guided (STG) denoising, and a vision-Language Advisor (VLA). Our SOV decoders disentangle object detection and verb recognition with a novel interaction region representation. The STG denoising strategy learns label embeddings with ground-truth information to guide the training and inference. Our SOV-STG achieves a fast convergence speed and high accuracy and builds a foundation for the VLA to incorporate the prior knowledge of the VLM. We introduce a vision advisor decoder to fuse both the interaction region information and the VLM's vision knowledge and a Verb-HOI prediction bridge to promote interaction representation learning. Our VLA notably improves our SOV-STG and achieves SOTA performance with one-sixth of training epochs compared to recent SOTA. Code and models are available at https://***/cjw2021/SOV-STG-VLA. © 2025 ieee.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

Beyond Spatial Explanations: Explainable Face recognition in the Frequency Domain

Beyond Spatial Explanations: Explainable Face Recognition in...

引用

2025 ieee/cvf Winter conference on Applications of computer vision, WACV 2025

作者： Huber, Marco Damer, Naser Fraunhofer Institute for Computer Graphics Research Igd Germany Technische Universität Darmstadt Germany

ISBN: (纸本)9798331510831

The need for more transparent face recognition (FR), along with other visual-based decision-making systems has recently attracted more attention in research, society, and industry. The reasons why two face images are matched or not matched by a deep learning-based face recognition system are not obvious due to the high number of parameters and the complexity of the models. However, it is important for users, operators, and developers to ensure trust and accountability of the system and to analyze drawbacks such as biased behavior. While many previous works use spatial semantic maps to highlight the regions that have a significant influence on the decision of the face recognition system, frequency components which are also considered by CNNs, are neglected. In this work, we take a step forward and investigate explainable face recognition in the unexplored frequency domain. This makes this work the first to propose explainability of verification-based decisions in the frequency domain, thus explaining the relative influence of the frequency components of each input toward the obtained outcome. To achieve this, we manipulate face images in the spatial frequency domain and investigate the impact on verification outcomes. In extensive quantitative experiments, along with investigating two special scenarios cases, cross-resolution FR and morphing attacks (the latter in supplementary material), we observe the applicability of our proposed frequency-based explanations. © 2025 ieee.

关键词： Frequency domain analysis

来源：评论

学校读者我要写书评

暂无评论

FitDiff: Robust Monocular 3D Facial Shape and Reflectance Estimation using Diffusion Models

FitDiff: Robust Monocular 3D Facial Shape and Reflectance Es...

引用

2025 ieee/cvf Winter conference on Applications of computer vision, WACV 2025

作者： Galanakis, Stathis Lattas, Alexandros Moschoglou, Stylianos Zafeiriou, Stefanos Imperial College London United Kingdom Huawei Noah's Ark Lab Canada

ISBN: (纸本)9798331510831

The remarkable progress in 3D face reconstruction has resulted in high-detail and photorealistic facial representations. Recently, Diffusion Models have revolutionized the capabilities of generative methods by surpassing the performance of GANs. In this work, we present FitDiff, a diffusion-based 3D facial avatar generative model. Lever-aging diffusion principles, our model accurately generates relightable facial avatars, utilizing an identity embedding extracted from an 'in-the-wild' 2D facial image. The introduced multimodal diffusion model is the first to concurrently output facial reflectance maps (diffuse and specular albedo and normals) and shapes, showcasing great generalization capabilities. It is solely trained on an annotated subset of a public facial dataset, paired with 3D reconstructions. We revisit the typical 3D facial fitting approach by guiding a reverse diffusion process using perceptual and face recognition losses. Being the first 3D LDM conditioned on face recognition embeddings, FitDiff reconstructs relightable human avatars, that can be used as-is in common rendering engines, starting only from an unconstrained facial image, and achieving state-of-the-art performance. © 2025 ieee.

关键词： Rendering (computer graphics)

来源：评论

学校读者我要写书评

暂无评论

Multi-Level Feature Distillation of Joint Teachers Trained on Distinct Image Datasets

Multi-Level Feature Distillation of Joint Teachers Trained o...

引用

2025 ieee/cvf Winter conference on Applications of computer vision, WACV 2025

作者： Iordache, Adrian Alexe, Bogdan Ionescu, Radu Tudor University of Bucharest Department of Computer Science Bucharest Romania 'Gheorghe Mihoc - Caius Iacob' Institute of Mathematical Statistics and Applied Mathematics The Romanian Academy Bucharest Romania

ISBN: (纸本)9798331510831

We propose a novel teacher-student framework to distill knowledge from multiple teachers trained on distinct datasets. Each teacher is first trained from scratch on its own dataset. Then, the teachers are combined into a joint architecture, which fuses the features of all teachers at multiple representation levels. The joint teacher architecture is fine-tuned on samples from all datasets, thus gathering useful generic information from all data samples. Finally, we employ a multi-level feature distillation procedure to transfer the knowledge to a student model for each of the considered datasets. We conduct image classification experiments on seven benchmarks, and action recognition experiments on three benchmarks. To illustrate the power of our feature distillation procedure, the student architectures are chosen to be identical to those of the individual teachers. To demonstrate the flexibility of our approach, we combine teachers with distinct architectures. We show that our novel Multi-Level Feature Distillation (MLFD) can significantly surpass equivalent architectures that are either trained on individual datasets, or jointly trained on all datasets at once. Furthermore, we confirm that each step of the proposed training procedure is well motivated by a comprehensive ablation study. We publicly release our code at https://***/AdrianIordache/MLFD. © 2025 ieee.

关键词： Teaching

来源：评论

学校读者我要写书评

暂无评论

@BENCH: Benchmarking vision-Language Models for Human-centered Assistive Technology

@BENCH: Benchmarking Vision-Language Models for Human-center...

引用

2025 ieee/cvf Winter conference on Applications of computer vision, WACV 2025

作者： Jiang, Xin Zheng, Junwei Liu, Ruiping Li, Jiahang Zhang, Jiaming Matthiesen, Sven Stiefelhagen, Rainer Karlsruhe Institute of Technology Germany Li Auto Inc. China

ISBN: (纸本)9798331510831

As vision-Language Models (VLMs) advance, human-centered Assistive Technologies (ATs) for helping People with Visual Impairments (PVIs) are evolving into generalists, capable of performing multiple tasks simultaneously. However, benchmarking VLMs for ATs remains under-explored. To bridge this gap, we first create a novel AT benchmark (@ Bench). Guided by a pre-design user study with PVIs, our benchmark includes the five most crucial vision-language tasks: Panoptic Segmentation, Depth Estimation, Optical Character recognition (OCR), Image Captioning, and Visual Question Answering (VQA). Besides, we propose a novel AT model (@MODEL) that addresses all tasks simultaneously and can be expanded to more assistive functions for helping PVIs. Our framework exhibits outstanding performance across tasks by integrating multi-modal information, and it offers PVIs a more comprehensive assistance. Extensive experiments prove the effectiveness and generalizability of our framework. © 2025 ieee.

关键词： Assistive technology

来源：评论

学校读者我要写书评

暂无评论

Enhancing vision-Language Few-Shot Adaptation with Negative Learning

Enhancing Vision-Language Few-Shot Adaptation with Negative ...

引用

2025 ieee/cvf Winter conference on Applications of computer vision, WACV 2025

作者： Zhang, Ce Stepputtis, Simon Sycara, Katia Xie, Yaqi School of Computer Science Carnegie Mellon University United States

ISBN: (纸本)9798331510831

Large-scale pre-trained vision-Language Models (VLMs) have exhibited impressive zero-shot performance and transferability, allowing them to adapt to downstream tasks in a data-efficient manner. However, when only a few labeled samples are available, adapting VLMs to distinguish subtle differences between similar classes in specific downstream tasks remains challenging. In this work, we propose a Simple yet effective Negative Learning approach, SimNL, to more efficiently exploit the task-specific knowledge from few-shot labeled samples. Unlike previous methods that focus on identifying a set of representative positive features defining 'what is a {CLASS} ', SimNL discovers a complementary set of negative features that define 'what is not a {CLASS}', providing additional insights that supplement the positive features to enhance task-specific recognition capability. Further, we identify that current adaptation approaches are particularly vulnerable to potential noise in the fewshot sample set. To mitigate this issue, we introduce a plug-and-play few-shot instance reweighting technique to suppress noisy outliers and amplify clean samples for more stable adaptation. Our extensive experimental results across 15 datasets validate that the proposed SimNL outperforms existing state-of-the-art methods on both few-shot learning and domain generalization tasks while achieving competitive computational efficiency. Code is available at https://***/zhangce01/SimNL. © 2025 ieee.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

SpectFormer: Frequency and Attention is what you need in a vision Transformer

SpectFormer: Frequency and Attention is what you need in a V...

引用

2025 ieee/cvf Winter conference on Applications of computer vision, WACV 2025

作者： Patro, Badri N. Namboodiri, Vinay P. Agneeswaran, Vijay S. Microsoft United States University of Bath United Kingdom

ISBN: (纸本)9798331510831

vision transformers have been applied successfully for image recognition tasks. There have been either multi-headed self-attention based (ViT [12], DelT [54]) simi-lar to the original work in textual models or more re-cently based on spectral layers (Fnet [29], GFNet [46], AFNO [15]). We hypothesize that spectral layers cap-ture high-frequency information such as lines and edges, while attention layers capture token interactions. We inves-tigate this hypothesis through this work and observe that indeed mixing spectral and multi-headed attention layers provides a better transformer architecture. We thus pro-pose the novel Spectformer architecture for vision trans-formers that has initial spectral and deeper multi-headed attention layers. We believe that the resulting representation allows the transformer to capture the feature repre-sentation appropriately and it yields improved performance over other transformer representations. For instance, it im-proves the top-1 accuracy by 2% on ImageNet compared to both GFNet-H and LiT. SpectFormer-H-S reaches 84.25% top-1 accuracy on ImageNet-1 K (state of the art for small version). Further, Spectformer-H-L achieves 85.7% which is the state of the art for the comparable base version of the transformers. We further validated the SpectFormer per-formance in other scenarios such as transfer learning on standard datasets such as ClFAR-10, ClFAR-100, Oxford-lIlT-flower, and Standford Car datasets. We then investigate its use in downstream tasks such as object detection and instance segmentation on the MS-COCO dataset and ob-serve that Spectformer shows consistent performance that is comparable to the best backbones and can be further optimized and improved. The source code is available on this website https://***/badripatro/SpectFormers. © 2025 ieee.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Proceedings - 2025 ieee/cvf Winter conference on Applications of computer vision Workshops, WACVW 2025

Proceedings - 2025 IEEE/CVF Winter Conference on Application...

引用

2025 ieee/cvf Winter conference on Applications of computer vision Workshops, WACVW 2025

ISBN: (纸本)9798331536626

The proceedings contain 166 papers. The topics discussed include: applying computer vision to analyze self-injurious behaviors in children with autism spectrum disorder;underwater image enhancement and object detection: are poor object detection results on enhanced images due to missing human labels?;enhancing weakly-supervised object detection on static images through (hallucinated) motion;a zero-shot learning approach for ephemeral gully detection from remote sensing using vision language models;Attrivision: advancing generalization in pedestrian attribute recognition using CLIP;human gaze improves vision transformers by token masking;SSTAR: skeleton-based spatio-temporal action recognition for intelligent video surveillance and suicide prevention in metro stations;and offline signature verification in the banking domain.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Learning Visual Grounding from Generative vision and Language Model

Learning Visual Grounding from Generative Vision and Languag...

引用

2025 ieee/cvf Winter conference on Applications of computer vision, WACV 2025

作者： Wang, Shijie Kim, Dahun Taalimi, Ali Sun, Chen Kuo, Weicheng Brown University United States Google DeepMind United Kingdom Google Cloud United States

ISBN: (纸本)9798331510831

Visual grounding tasks aim to localize image regions based on natural language references. In this work, we ex-plore whether generative VLMs predominantly trained on image-text data could be leveraged to scale up the text an-notation of visual grounding data. We find that grounding knowledge already exists in generative VLM and can be elicited by proper prompting. We thus prompt a VLM to generate object-level descriptions by feeding it object regions from existing object detection datasets. We fur-ther propose attribute modeling to explicitly capture the im-portant object attributes, and spatial relation modeling to capture inter-object relationship, both of which are common linguistic pattern in referring expression. Our constructed dataset (500K images, 1M objects, 16M referring expressions) is one of the largest grounding datasets to date, and the first grounding dataset with purely model-generated queries and human-annotated objects. To verify the qual-ity of this data, we conduct zero-shot transfer experiments to the popular RefCoco benchmarks for both referring expression comprehension (REC) and segmentation (RES) tasks. On both tasks, our model significantly outperform the state-of-the-art approaches without using human anno-tated visual grounding data. Our results demonstrate the promise of generative VLM to scale up visual grounding in the real world. © 2025 ieee.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

U-MedSAM: Uncertainty-Aware MedSAM for Medical Image Segmentation

U-MedSAM: Uncertainty-Aware MedSAM for Medical Image Segmen...

引用

International Challenge on Segment Anything in Medical Images on Laptop held in conjunction with the ieee/cvf conference on computer vision and pattern recognition, CVPR 2024

作者： Wang, Xin Liu, Xiaoyu Huang, Peng Huang, Pu Hu, Shu Zhu, Hongtu Albany United States School of Physics and Electronics Shandong Normal University Jinan China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Department of Computer and Information Technology Purdue University West Lafayette United States University of North Carolina at Chapel Hill Chapel Hill United States

ISBN: (纸本)9783031818530

Medical Image Foundation Models have proven to be powerful tools for mask prediction across various datasets. However, accurately assessing the uncertainty of their predictions remains a significant challenge. To address this, we propose a new model, U-MedSAM, which integrates the MedSAM model with an uncertainty-aware loss function and the Sharpness-Aware Minimization (SharpMin) optimizer. The uncertainty-aware loss function automatically combines region-based, distribution-based, and pixel-based loss designs to enhance segmentation accuracy and robustness. SharpMin improves generalization by finding flat minima in the loss landscape, thereby reducing overfitting. Our method was evaluated in the CVPR24 MedSAM on Laptop challenge, where U-MedSAM demonstrated promising performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Medical imaging

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 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：