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

20,860 篇 会议
105 篇 期刊文献
43 册 图书

馆藏范围

21,007 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,620 篇 工学
- 11,056 篇 计算机科学与技术...
- 2,652 篇 机械工程
- 2,252 篇 软件工程
- 914 篇 光学工程
- 885 篇 电气工程
- 529 篇 控制科学与工程
- 477 篇 信息与通信工程
- 216 篇 测绘科学与技术
- 135 篇 生物工程
- 127 篇 生物医学工程（可授...
- 98 篇 电子科学与技术（可...
- 92 篇 仪器科学与技术
- 46 篇 安全科学与工程
- 40 篇 建筑学
- 40 篇 化学工程与技术
- 39 篇 土木工程
- 37 篇 交通运输工程
- 35 篇 力学（可授工学、理...
- 33 篇 航空宇航科学与技...
3,494 篇 医学
- 3,489 篇 临床医学
- 32 篇 基础医学(可授医学...
2,247 篇 理学
- 1,145 篇 物理学
- 1,081 篇 数学
- 401 篇 生物学
- 384 篇 统计学（可授理学、...
- 245 篇 系统科学
- 46 篇 化学
343 篇 管理学
- 176 篇 管理科学与工程(可...
- 168 篇 图书情报与档案管...
- 34 篇 工商管理
31 篇 法学
19 篇 农学
15 篇 教育学
8 篇 经济学
5 篇 艺术学
2 篇 军事学
1 篇 文学

主题

8,141 篇 computer vision
2,886 篇 training
2,841 篇 pattern recognit...
1,809 篇 computational mo...
1,715 篇 visualization
1,493 篇 cameras
1,433 篇 three-dimensiona...
1,433 篇 feature extracti...
1,366 篇 shape
1,360 篇 face recognition
1,243 篇 image segmentati...
1,135 篇 robustness
1,124 篇 semantics
992 篇 computer archite...
985 篇 object detection
982 篇 layout
959 篇 benchmark testin...
935 篇 codes
900 篇 computer science
898 篇 object recogniti...

机构

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

作者

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

语言

20,982 篇 英文
10 篇 中文
7 篇 其他
5 篇 土耳其文
2 篇 日文
2 篇 葡萄牙文

检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 21008 条记录，以下是431-440 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：

Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalization

Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential ...

引用

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

作者： Singha, Mainak Jha, Ankit Bose, Shirsha Nair, Ashwin Abdar, Moloud Banerjee, Biplab Aisin Corp Kariya Aichi Japan Indian Inst Technol Bombay Maharashtra India Tech Univ Munich Munich Germany IISER Thiruvananthapuram Thiruvananthapuram Kerala India Deakin Univ Geelong Vic Australia

ISBN: (纸本)9798350353006

We delve into Open Domain Generalization (ODG), marked by domain and category shifts between training's labeled source and testing's unlabeled target domains. Existing solutions to ODG face limitations due to constrained generalizations of traditional CNN backbones and errors in detecting target open samples in the absence of prior knowledge. Addressing these pitfalls, we introduce ODG-CLIP, harnessing the semantic prowess of the vision-language model, CLIP. Our framework brings forth three primary innovations: Firstly, distinct from prevailing paradigms, we conceptualize ODG as a multi-class classification challenge encompassing both known and novel categories. Central to our approach is modeling a unique prompt tailored for detecting unknown class samples, and to train this, we employ a readily accessible stable diffusion model, elegantly generating proxy images for the open class. Secondly, aiming for domain-tailored classification (prompt) weights while ensuring a balance of precision and simplicity, we devise a novel visual style-centric prompt learning mechanism. Finally, we infuse images with class-discriminative knowledge derived from the prompt space to augment the fidelity of CLIP's visual embeddings. We introduce a novel objective to safeguard the continuity of this infused semantic intel across domains, especially for the shared classes. Through rigorous testing on diverse datasets, covering closed and open-set DG contexts, ODG-CLIP demonstrates clear supremacy, consistently outpacing peers with performance boosts between 8%-16%. Code will be available at https://***/mainaksingha01/ODG-CLIP.

关键词： open domain generalization vision-language models

来源：评论

学校读者我要写书评

暂无评论

Modality-Collaborative Test-Time Adaptation for Action recognition

Modality-Collaborative Test-Time Adaptation for Action Recog...

引用

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

作者： Xiong, Baochen Yang, Xiaoshan Song, Yaguang Wang, Yaowei Xu, Changsheng Chinese Acad Sci CASIA Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Univ Chinese Acad Sci UCAS Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350353006

Video-based Unsupervised Domain Adaptation (VUDA) method improves the generalization of the video model, enabling it to be applied to action recognition tasks in different environments. However, these methods require continuous access to source data during the adaptation process, which are impractical in real scenarios where the source videos are not available with concerns in transmission efficiency or privacy issues. To address this problem, in this paper, we focus on the Multimodal Video Test-Time Adaptation (MVTTA) task. Existing image-based TTA methods cannot be directly applied to this task because videos have domain shifts in multimodal and temporal, which brings difficulties to adaptation. To address the above challenges, we pro-pose a Modality-Collaborative Test-Time Adaptation (MC-TTA) Network. MC-TTA contains maintain teacher and student memory banks respectively for generating pseudo-prototypes and target-prototypes. In the teacher model, we propose Self-assembled Source-friendly Feature Reconstruction (SSFR) to encourage the teacher memory bank to store features that are more likely to be consistent with the source distribution. Through multimodal prototype alignment and cross-modal relative consistency, our method can effectively alleviate domain shift in videos. We evaluate the proposed model on four public video datasets. The results show that our model outperforms existing state-of-the-art methods.

关键词： Multi-modal learning

来源：评论

学校读者我要写书评

暂无评论

Logarithmic Lenses: Exploring Log RGB Data for Image Classification

Logarithmic Lenses: Exploring Log RGB Data for Image Classif...

引用

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

作者： Maxwell, Bruce A. Singhania, Sumegha Patel, Avnish Kumar, Rahul Fryling, Heather Li, Sihan Sun, Haonan He, Ping Li, Zewen Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9798350353006

The design of deep network architectures and training methods in computer vision has been well-explored. However, in almost all cases the images have been used as provided, with little exploration of pre-processing steps beyond normalization and data augmentation. Virtually all images posted on the web or captured by devices are processed for viewing by humans. Is the pipeline used for humans also best for use by computers and deep networks? The human visual system uses logarithmic sensors;differences and sums correspond to ratios and products. Fea-tures in log space will be invariant to intensity changes and robust to color balance changes. Log RGB space also reveals structure that is corrupted by typical pre-processing. We explore using linear and log RGB data for training standard backbone architectures on an image classification task using data derived directly from RAW images to guarantee its integrity. We found that networks trained on log RGB data exhibit improved performance on an unmodified test set and invariance to intensity and color balance modifications without additional training or data augmentation. Furthermore, we found that the gains from using high quality log data could also be partially or fully realized from data in 8-bit sRGB-JPG format by inverting the sRGB transform and taking the log. These results imply existing databases may benefit from this type of pre-processing. While working with log data, we found it was critical to retain the integrity of the log relationships and that networks using log data train best with meta-parameters different than those used for sRGB or linear data. Finally, we introduce a new 10-category 10k RAW image data set (RAW10) for image classification and other purposes to enable further the exploration of log RGB as an input format for deep networks in computer vision.

关键词： computer vision data set image classification physics-based vision

来源：评论

学校读者我要写书评

暂无评论

Anchor-based Robust Finetuning of vision-Language Models

Anchor-based Robust Finetuning of Vision-Language Models

引用

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

作者： Han, Jinwei Lin, Zhiwen Sun, Zhongyisun Gao, Yingguo Yan, Ke Ding, Shouhong Gao, Yuan Xia, Gui-Song Wuhan Univ Sch Comp Sci Wuhan Peoples R China Tencent YouTu Lab Shenzhen Peoples R China Wuhan Univ Elect Informat Sch Wuhan Peoples R China

ISBN: (纸本)9798350353006

We aim at finetuning a vision-language model without hurting its out-of-distribution (OOD) generalization. We address two types of OOD generalization, i.e., i) domain shift such as natural to sketch images, and ii) zero-shot capability to recognize the category that was not contained in the finetune data. Arguably, the diminished OOD generalization after finetuning stems from the excessively simplified finetuning target, which only provides the class information, such as "a photo of a [CLASS]". This is distinct from the process in that CLIP was pretrained, where there is abundant text supervision with rich semantic information. Therefore, we propose to compensate for the finetune process using auxiliary supervision with rich semantic information, which acts as anchors to preserve the OOD generalization. Specifically, two types of anchors are elaborated in our method, including i) text-compensated anchor which uses the images from the finetune set but enriches the text supervision from a pretrained captioner, ii) image-text-pair anchor which is retrieved from the dataset similar to pre-training data of CLIP according to the downstream task, associating with the original CLIP text with rich semantics. Those anchors are utilized as auxiliary semantic information to maintain the original feature space of CLIP, thereby preserving the OOD generalization capabilities. Comprehensive experiments demonstrate that our method achieves in-distribution performance akin to conventional finetuning while attaining new state-of-the-art results on domain shift and zero-shot learning benchmarks.

关键词： Anchor Robust Finetuning

来源：评论

学校读者我要写书评

暂无评论

View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network

View-decoupled Transformer for Person Re-identification unde...

引用

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

作者： Zhang, Quan Wang, Lei Patel, Vishal M. Xie, Xiaohua Lai, Jianhuang Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Pazhou Lab HuangPu Guangzhou Guangdong Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD USA

ISBN: (纸本)9798350353006

Existing person re-identification methods have achieved remarkable advances in appearance-based identity association across homogeneous cameras, such as ground-ground matching. However, as a more practical scenario, aerial-ground person re-identification (AGPReID) among heterogeneous cameras has received minimal attention. To alleviate the disruption of discriminative identity representation by dramatic view discrepancy as the most significant challenge in AGPReID, the view-decoupled transformer (VDT) is proposed as a simple yet effective framework. Two major components are designed in VDT to decouple view-related and view-unrelated features, namely hierarchical subtractive separation and orthogonal loss, where the former separates these two features inside the VDT, and the latter constrains these two to be independent. In addition, we contribute a large-scale AGPReID dataset called CARGO, consisting of five/eight aerial/ground cameras, 5,000 identities, and 108,563 images. Experiments on two datasets show that VDT is a feasible and effective solution for AGPReID, surpassing the previous method on mAP/Rank1 by up to 5.0%/2.7% on CARGO and 3.7%/5.2% on AG-ReID, keeping the same magnitude of computational complexity. Our project is available at https://***/LinlyAC/VDT-AGPReID.

关键词： image recognition person re-identification transformer

来源：评论

学校读者我要写书评

暂无评论

Open-Set Domain Adaptation for Semantic Segmentation

Open-Set Domain Adaptation for Semantic Segmentation

引用

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

作者： Choe, Seun-An Shin, Ah-Hyung Park, Keon-Hee Choi, Jinwoo Park, Gyeong-Moon Kyung Hee Univ Yongin South Korea

ISBN: (纸本)9798350353006

Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer the pixel-wise knowledge from the labeled source domain to the unlabeled target domain. However, current UDA methods typically assume a shared label space between source and target, limiting their applicability in real-world scenarios where novel categories may emerge in the target domain. In this paper, we introduce Open-Set Domain Adaptation for Semantic Segmentation (OSDA-SS) for the first time, where the target domain includes unknown classes. We identify two major problems in the OSDA-SS scenario as follows: 1) the existing UDA methods struggle to predict the exact boundary of the unknown classes, and 2) they fail to accurately predict the shape of the unknown classes. To address these issues, we propose Boundary and Unknown Shape-Aware open-set domain adaptation, coined BUS. Our BUS can accurately discern the boundaries between known and unknown classes in a contrastive manner using a novel dilation-erosion-based contrastive loss. In addition, we propose OpenReMix, a new domain mixing augmentation method that guides our model to effectively learn domain and size-invariant features for improving the shape detection of the known and unknown classes. Through extensive experiments, we demonstrate that our proposed BUS effectively detects unknown classes in the challenging OSDA-SS scenario compared to the previous methods by a large margin. The code is available at https://***/KHU-AGI/BUS.

关键词： Contrastive Learning Domain Adaptation Domain Mixing Augmentation Open-Set recognition Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

OST: Refining Text Knowledge with Optimal Spatio-Temporal Descriptor for General Video recognition

OST: Refining Text Knowledge with Optimal Spatio-Temporal De...

引用

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

作者： Chen, Tongjia Yu, Hongshan Yang, Zhengeng Li, Zechuan Sun, Wei Chen, Chen Hunan Univ Changsha Peoples R China Hunan Normal Univ Changsha Peoples R China Univ Cent Florida Ctr Res Comp Vis Orlando FL USA

ISBN: (纸本)9798350353006

Due to the resource-intensive nature of training vision-language models on expansive video data, a majority of studies have centered on adapting pre-trained image-language models to the video domain. Dominant pipelines propose to tackle the visual discrepancies with additional temporal learners while overlooking the substantial discrepancy for web-scaled descriptive narratives and concise action category names, leading to less distinct semantic space and potential performance limitations. In this work, we prioritize the refinement of text knowledge to facilitate generalizable video recognition. To address the limitations of the less distinct semantic space of category names, we prompt a large language model (LLM) to augment action class names into Spatio-Temporal Descriptors thus bridging the textual discrepancy and serving as a knowledge base for general recognition. Moreover, to assign the best descriptors with different video instances, we propose Optimal Descriptor Solver, forming the video recognition problem as solving the optimal matching flow across frame-level representations and descriptors. Comprehensive evaluations in zero-shot, few-shot, and fully supervised video recognition highlight the effectiveness of our approach. Our best model achieves a state-of-the-art zero-shot accuracy of 75.1% on Kinetics-600.

关键词： Action recognition Multi-modality Video Understanding Video Reognition Video Understanding

来源：评论

学校读者我要写书评

暂无评论

WorDepth: Variational Language Prior for Monocular Depth Estimation

WorDepth: Variational Language Prior for Monocular Depth Est...

引用

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

作者： Zeng, Ziyao Wang, Daniel Yang, Fengyu Park, Hyoungseob Wong, Alex Soatto, Stefano Lao, Dong Yale Univ New Haven CT 06511 USA Univ Calif Los Angeles Los Angeles CA USA

ISBN: (纸本)9798350353006

Three-dimensional (3D) reconstruction from a single image is an ill-posed problem with inherent ambiguities, i.e. scale. Predicting a 3D scene from text description(s) is similarly ill-posed, i.e. spatial arrangements of objects described. We investigate the question of whether two inherently ambiguous modalities can be used in conjunction to produce metric-scaled reconstructions. To test this, we focus on monocular depth estimation, the problem of predicting a dense depth map from a single image, but with an additional text caption describing the scene. To this end, we begin by encoding the text caption as a mean and standard deviation;using a variational framework, we learn the distribution of the plausible metric reconstructions of 3D scenes corresponding to the text captions as a prior. To "select" a specific reconstruction or depth map, we encode the given image through a conditional sampler that samples from the latent space of the variational text encoder, which is then decoded to the output depth map. Our approach is trained alternatingly between the text and image branches: in one optimization step, we predict the mean and standard deviation from the text description and sample from a standard Gaussian, and in the other, we sample using a (image) conditional sampler. Once trained, we directly predict depth from the encoded text using the conditional sampler. We demonstrate our approach on indoor (NYUv2) and out-door (KITTI) scenarios, where we show that language can consistently improve performance in both. Code: https: //***/Adonis-galaxy/WorDepth.

关键词： Monocular Depth Estimation Variational Model vision-Language Model

来源：评论

学校读者我要写书评

暂无评论

Multiview Aerial Visual recognition (MAVREC): Can Multi-view Improve Aerial Visual Perception?

Multiview Aerial Visual Recognition (MAVREC): Can Multi-view...

引用

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

作者： Dutta, Aritra Das, Srijan Nielsen, Jacob Chakraborty, Rajatshubhra Shah, Mubarak UCF AI Initiat Orlando FL 32816 USA UNC Charlotte Charlotte NC USA Univ So Denmark IMADA Odense Denmark UCF CRCV Orlando FL USA

ISBN: (纸本)9798350353006

Despite the commercial abundance of UAVs, aerial data acquisition remains challenging, and the existing Asia and North America-centric open-source UAV datasets are small-scale or low-resolution and lack diversity in scene contextuality. Additionally, the color content of the scenes, solar zenith angle, and population density of different geographies influence the data diversity. These factors conjointly render suboptimal aerial-visual perception of the deep neural network (DNN) models trained primarily on the ground view data, including the open-world foundational models. To pave the way for a transformative era of aerial detection, we present Multiview Aerial Visual recognition or MAVREC, a video dataset where we record synchronized scenes from different perspectives - ground camera and drone-mounted camera. MAVREC consists of around 2.5 hours of industry-standard 2.7K resolution video sequences, more than 0.5 million frames, and 1.1 million annotated bounding boxes. This makes MAVREC the largest ground and aerial view dataset, and the fourth largest among all drone-based datasets across all modalities and tasks. Through our extensive benchmarking on MAVREC, we recognize that augmenting object detectors with ground view images from the corresponding geographical location is a superior pre-training strategy for aerial detection. Building on this strategy, we benchmark MAVREC with a curriculum-based semi-supervised object detection approach that leverages labeled (ground and aerial) and unlabeled (only aerial) images to enhance aerial detection.

关键词： Aerial Dataset Baselines and Benchmarking Drone Dataset Multiview Dataset Object Detection Semi-supervised Learning

来源：评论

学校读者我要写书评

暂无评论

EmotiEffNet and Temporal Convolutional Networks in Video-based Facial Expression recognition and Action Unit Detection

EmotiEffNet and Temporal Convolutional Networks in Video-bas...

引用

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

作者： Savchenko, Andrey, V Sidorova, Anna P. Sber AI Lab Moscow Russia HSE Univ Lab Algorithms & Technol Network Anal Nizhnii Novgorod Russia HSE Univ Nizhnii Novgorod Russia

ISBN: (纸本)9798350365474

This paper examines the video-based facial expression recognition and action unit detection tasks. We propose to use pre-trained EmotiEffNet models for frame-level facial feature extraction and feed them into the Temporal Convolutional Networks to take into account the dynamics of facial expressions. In addition, we study the possibility of combining facial processing with audio feature extraction to improve the accuracy of audio-visual expression recognition. Experimental results for two tasks from the sixth Affective Behavior Analysis in-the-Wild challenge demonstrate that our approach lets us significantly improve quality metrics on validation sets compared to existing non-ensemble techniques. As a result, our approach took third place in the action unit detection and fourth place in the expression recognition.

关键词： Action unit detection EmotiEffNet models Facial expression recognition HSEmotion library

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 << < 40 41 42 43 44 45 46 47 48 49 > >>

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：