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

3,317 篇 会议
3 篇 期刊文献

馆藏范围

3,320 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,915 篇 工学
- 1,820 篇 计算机科学与技术...
- 376 篇 软件工程
- 142 篇 机械工程
- 136 篇 光学工程
- 42 篇 生物工程
- 28 篇 信息与通信工程
- 11 篇 控制科学与工程
- 9 篇 电气工程
- 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
174 篇 理学
- 136 篇 物理学
- 43 篇 生物学
- 29 篇 数学
- 16 篇 统计学（可授理学、...
- 10 篇 化学
29 篇 医学
- 28 篇 临床医学
- 3 篇 基础医学(可授医学...
15 篇 管理学
- 8 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 教育学
2 篇 农学
1 篇 经济学

主题

1,179 篇 computer vision
801 篇 conferences
570 篇 training
484 篇 pattern recognit...
330 篇 computational mo...
279 篇 computer archite...
255 篇 visualization
180 篇 feature extracti...
160 篇 neural networks
153 篇 semantics
152 篇 task analysis
145 篇 cameras
143 篇 deep learning
140 篇 three-dimensiona...
139 篇 benchmark testin...
126 篇 low-level vision
117 篇 vision
115 篇 language
113 篇 image segmentati...
112 篇 estimation

机构

35 篇 tsinghua univ pe...
34 篇 univ sci & techn...
28 篇 shanghai ai lab ...
26 篇 chinese univ hon...
25 篇 university of sc...
25 篇 tsinghua univers...
24 篇 peng cheng labor...
23 篇 swiss fed inst t...
23 篇 swiss fed inst t...
23 篇 peng cheng lab p...
22 篇 zhejiang univ pe...
22 篇 carnegie mellon ...
20 篇 univ chinese aca...
20 篇 university of ch...
18 篇 harbin inst tech...
18 篇 shanghai jiao to...
18 篇 sensetime res pe...
16 篇 hong kong univ s...
15 篇 fudan univ peopl...
14 篇 national key lab...

作者

60 篇 timofte radu
26 篇 radu timofte
19 篇 van gool luc
16 篇 lei lei
15 篇 loy chen change
15 篇 qiao yu
14 篇 fan haoqiang
13 篇 zhang yulun
13 篇 liu shuaicheng
13 篇 luc van gool
12 篇 chen chen
12 篇 chen wei-ting
12 篇 li chongyi
11 篇 wangmeng zuo
11 篇 pan jinshan
11 篇 marcos v. conde
10 篇 wei dong
10 篇 zhou shangchen
10 篇 suiyi zhao
10 篇 yu lei

语言

3,319 篇 英文
1 篇 其他

检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是2211-2220 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：

TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature Signals

TCCT-Net: Two-Stream Network Architecture for Fast and Effic...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Alexander Vedernikov Puneet Kumar Haoyu Chen Tapio Seppänen Xiaobai Li Center for Machine Vision and Signal Analysis University of Oulu Finland State Key Laboratory of Blockchain and Data Security Zhejiang University China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Engagement analysis finds various applications in healthcare, education, advertisement, services. Deep Neural Networks, used for analysis, possess complex architecture and need large amounts of input data, computational power, inference time. These constraints challenge embedding systems into devices for real-time use. To address these limitations, we present a novel two-stream feature fusion "Tensor-Convolution and Convolution-Transformer Network" (TCCT-Net) architecture. To better learn the meaningful patterns in the temporal-spatial domain, we design a "CT" stream that integrates a hybrid convolutional-transformer. In parallel, to efficiently extract rich patterns from the temporal-frequency domain and boost processing speed, we introduce a "TC" stream that uses Continuous Wavelet Transform (CWT) to represent information in a 2D tensor form. Evaluated on the EngageNet dataset, the proposed method outperforms existing baselines, utilizing only two behavioral features (head pose rotations) compared to the 98 used in baseline models. Furthermore, comparative analysis shows TCCT-Net’s architecture offers an order-of-magnitude improvement in inference speed compared to state-of-the-art image-based Recurrent Neural Network (RNN) methods. The code will be released at https://***/vedernikovphoto/TCCT_Net.

关键词： Convolutional codes Accuracy Recurrent neural networks Continuous wavelet transforms Wavelet domain Convolution Computational modeling

来源：评论

学校读者我要写书评

暂无评论

IndoFashion : Apparel Classification for Indian Ethnic Clothes

IndoFashion : Apparel Classification for Indian Ethnic Cloth...

引用

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

作者： Rajput, Pranjal Singh Aneja, Shivangi Delft Univ Technol Delft Netherlands Tech Univ Munich Munich Germany

ISBN: (纸本)9781665448994

Cloth categorization is an important research problem that is used by e-commerce websites for displaying correct products to the end-users. Indian clothes have a large number of clothing categories both for men and women. The traditional Indian clothes like "Saree" and "Dhoti" are worn very differently from western clothes like t-shirts and jeans. Moreover, the style and patterns of ethnic clothes have a very different distribution from western outfits. Thus the models trained on standard cloth datasets fail on ethnic outfits. We introduce the first large-scale ethnic dataset of over 106K images with 15 different categories for fine-grained classification of Indian ethnic clothes. We gathered a diverse dataset from a large number of Indian e-commerce websites. We then evaluate several baselines for the cloth classification task on our dataset. We obtain 88.43% classification accuracy. We hope that our dataset would foster research in the development of several algorithms such as cloth classification, landmark detection, especially for ethnic clothes. The dataset is publicly available here (1).

关键词： computer vision conferences Clothing pattern recognition Classification algorithms Task analysis Standards

来源：评论

学校读者我要写书评

暂无评论

MvAV-pix2pixHD: Multi-view Aerial View Image Translation

MvAV-pix2pixHD: Multi-view Aerial View Image Translation

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jun Yu Keda Lu Shenshen Du Lin Xu Peng Chang Houde Liu Bin Lan Tianyu Liu University of Science and Technology of China Ping An Technology Co. Ltd China PAII Inc. Tsinghua Shenzhen International Graduate School Jianghuai Advance Technology Center

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multi-modal aerial view image translation involves converting aerial images from one modality to another while preserving basic details and features. These modalities encompass Synthetic Aperture Radar (SAR), Infrared (IR), Visible Light (RGB), Electro-Optical (EO), and other image types. Recently, various methods have been proposed to tackle this task, but the focus tends to be on paired image research, overlooking the discrepancies found in aerial images of the same location captured at different times and angles, termed incomplete matching or multi-view image translation. Consequently, we propose MvAV-pix2pixHD to address this issue. For multi-view data sampling, we propose two methods: random sampling and time-priority sampling. Additionally, within the pix2pixHD framework, we introduce an inverse generator to ensure the basic semantic features of the generated images and incorporate three robust loss functions to constrain the authenticity of the generated images. We conduct extensive experiments on two multi-view image translation tasks in the Multi-modal Aerial View Imagery Challenge: Translation (MAVIC-T). Experimental results demonstrate the superiority of our proposed method, and we achieved second place in the MAVIC-T competition in the 20th ieee Workshop on Perception Beyond the Visible Spectrum of the CVPR 2024.

关键词： computer vision Image resolution conferences Computational modeling Semantics computer architecture Generators

来源：评论

学校读者我要写书评

暂无评论

SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation

SegFormer3D: an Efficient Transformer for 3D Medical Image S...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Shehan Perera Pouyan Navard Alper Yilmaz Photogrammetric Computer Vision Lab The Ohio State University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The adoption of vision Transformers (ViTs) based architectures represents a significant advancement in 3D Medical Image (MI) segmentation, surpassing traditional Convolutional Neural Network (CNN) models by enhancing global contextual understanding. While this paradigm shift has significantly enhanced 3D segmentation performance, state-of-the-art architectures require extremely large and complex architectures with large scale computing resources for training and deployment. Furthermore, in the context of limited datasets, often encountered in medical imaging, larger models can present hurdles in both model generalization and convergence. In response to these challenges and to demonstrate that lightweight models are a valuable area of research in 3D medical imaging, we present SegFormer3D, a hierarchical Transformer that calculates attention across multiscale volumetric features. Additionally, SegFormer3D avoids complex decoders and uses an all-MLP decoder to aggregate local and global attention features to produce highly accurate segmentation masks. The proposed memory efficient Transformer preserves the performance characteristics of a significantly larger model in a compact design. SegFormer3D democratizes deep learning for 3D medical image segmentation by offering a model with 33× less parameters and a 13× reduction in GFLOPS compared to the current state-of-the-art (SOTA). We benchmark SegFormer3D against the current SOTA models on three widely used datasets Synapse, BRaTs, and ACDC, achieving competitive results. Code: https://***/OSUPCVLab/***

关键词： Training Image segmentation Solid modeling Three-dimensional displays Computational modeling computer architecture Transformers

来源：评论

学校读者我要写书评

暂无评论

TlTScore: Towards Long-Tail Effects in Text-to-Visual Evaluation with Generative Foundation Models

TlTScore: Towards Long-Tail Effects in Text-to-Visual Evalua...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Pengliang Ji Junchen Liu Carnegie Mellon University UC Berkeley

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Evaluation of generative foundation models (GenFMs) for text-to-visual tasks has been enhanced by automatic alignment metrics such as CLIPScore, complementing human feedback. However, existing evaluation methods suffer from a severe long-tail effect, where the balance between token count and semantic validity in the initial step, hinders the accurate evaluation of advanced aspects such as composition. We analyze this drawback and attribute it to a lack of symbolic reasoning attention, while GenFMs demonstrate strong discriminative abilities in handling symbolism. To this end, we propose a pioneering paradigm for evaluating GenFMs’ text-to-visual (T2V) generation using neuro-symbolic thinking to mitigate the long-tail effect. By explicitly embedding Mixture-of-experts (MoE) Large vision Models (LVMs), we introduce symbolic-level understanding while maintaining the strong neuro-level reasoning capability. Through the fusion of semantic and compositional knowledge at the neuro-to-symbolic level, our approach outperforms state-of-the-art T2V evaluation methods, exhibiting stronger compositional reasoning ability on Winoground and better alignment with human judgment. We also demonstrate our impressive effectiveness on diverse tasks, including text-to-3D and text-to-video. To further advance the T2V evaluation of GenFMs, we propose a challenging benchmark that includes richer and more diverse compositional and semantic information compared to Winoground. Overall, our work opens a new direction for neuro-to-symbolic visio-linguistic evaluation of GenFMs and aims to drive further progress in the field.

关键词： Measurement computer vision Accuracy conferences Semantics Benchmark testing Cognition

来源：评论

学校读者我要写书评

暂无评论

Adaptive Render-Video Streaming for Virtual Environments

Adaptive Render-Video Streaming for Virtual Environments

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jia-Jie Lim Matthias S. Treder Aaron Chadha Yiannis Andreopoulos Sony Interactive Entertainment London UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In cloud-based gaming and virtual reality (G&VR), scene content is rendered in a cloud server and streamed as low-latency encoded video to the client device. In this context, distributed rendering aims to offload parts of the rendering to the client. An adaptive approach is proposed, which dynamically assigns assets to client-side vs. server-side rendering according to varying rendering time and bitrate targets. This is achieved by streaming perceptually-optimized scene control weights to the client, which are compressed with a composable autoencoder in conjunction with select video segments. This creates an adaptive render-video (REVI) streaming framework, which allows for substantial tradeoffs between client rendering time and the bitrate required to stream visually-lossless video from the server to the client. In order to estimate and control the client rendering time and the required bitrate of each subset of each scene, a random-forest based regressor is proposed, in conjunction with the use of AIMD (additive-increase/multiplicative-decrease) to ensure predetermined average bitrate or render-time targets are met. Experiments are presented based on typical sets of G&VR scenes rendered in Blender and HEVC low-latency encoding. A key result is that, when the client is providing for 50% of the rendering time needed to render the whole scene, up to 60% average bitrate saving is achieved versus streaming the entire scene to the client as video.

关键词： Cloud computing Bit rate Lighting Virtual environments Streaming media Ray tracing Rendering (computer graphics)

来源：评论

学校读者我要写书评

暂无评论

Virtually Enriched NYU Depth V2 Dataset for Monocular Depth Estimation: Do We Need Artificial Augmentation?

Virtually Enriched NYU Depth V2 Dataset for Monocular Depth ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Dmitry Ignatov Andrey Ignatov Radu Timofte Computer Vision Lab CAIDAS & IFI University of Wurzburg Germany Computer Vision Lab ETH Zurich Switzerland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present ANYU, a new virtually augmented version of the NYU depth v2 dataset, designed for monocular depth estimation. In contrast to the well-known approach where full 3D scenes of a virtual world are utilized to generate artificial datasets, ANYU was created by incorporating RGB-D representations of virtual reality objects into the original NYU depth v2 images. We specifically did not match each generated virtual object with an appropriate texture and a suitable location within the real-world image. Instead, an assignment of texture, location, lighting, and other rendering parameters was randomized to maximize a diversity of the training data, and to show that it is randomness that can improve the generalizing ability of a dataset. By conducting extensive experiments with our virtually modified dataset and validating on the original NYU depth v2 and iBims-1 benchmarks, we show that ANYU improves the monocular depth estimation performance and generalization of deep neural networks with considerably different architectures, especially for the current state-of-the-art VPD model. To the best of our knowledge, this is the first work that augments a real-world dataset with randomly generated virtual 3D objects for monocular depth estimation. We make our ANYU dataset publicly available in two training configurations with 10% and 100% additional synthetically enriched RGB-D pairs of training images, respectively, for efficient training and empirical exploration of virtual augmentation at https://***/ABrain-One/ANYU.

关键词： Training Solid modeling Three-dimensional displays Computational modeling Estimation Training data Lighting

来源：评论

学校读者我要写书评

暂无评论

Transformers for Orbit Determination Anomaly Detection and Classification

Transformers for Orbit Determination Anomaly Detection and C...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Nathan Parrish Ré Matthew Popplewell Michael Caudill Timothy Sullivan Tyler Hanf Benjamin Tatman Kanak Parmar Tyler Presser Sai Chikine Michael Grant Richard Poulson Advanced Space LLC Westminster CO

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper presents the judicious application of machine learning algorithms to solve two fundamental challenges in spacecraft orbit determination (OD): identification of systematic anomalies (nominal vs anomalous behavior) and the classification of these anomalies to explain probable causes (such as unexpected spacecraft maneuvers or mis-modeled small forces). Traditional OD is based on well-tested iterative linearization methods (variations of the Kalman filter). These are commonly understood in the astrodynamics community to require manual tuning for a given set of mission assumptions and re-tuning when those assumptions are invalidated. OD data are typically long, sparse, multi-variate sequences consisting of multiple observation phenomenologies. These characteristics make the OD problem fundamentally well-suited to the same machine learning architectures (namely, transformers) that have found success with language modeling and other sequence-based data modeling. The approach taken here is to simulate various failure modes in the traditional OD process using NASA’s Monte software, then process the simulated data in three different transformer-based architectures. The three transformer architectures all act as classifiers and can be described at a high level as: 1) treat each epoch with data as a feature vector, with the input data comprising a single, long sequence; 2) similar to the first, but with nested self-attention to efficiently handle longer sequences; and 3) plot the data, then classify the plot with a vision transformer (ViT) model. Model performance is studied as a function of the hyperparameter trade space.

关键词： Space vehicles computer vision Navigation computer architecture Transformers Extraterrestrial measurements Data models

来源：评论

学校读者我要写书评

暂无评论

Evaluating Multimodal Large Language Models across Distribution Shifts and Augmentations

Evaluating Multimodal Large Language Models across Distribut...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Aayush Atul Verma Amir Saeidi Shamanthak Hegde Ajay Therala Fenil Denish Bardoliya Nagaraju Machavarapu Shri Ajay Kumar Ravindhiran Srija Malyala Agneet Chatterjee Yezhou Yang Chitta Baral Arizona State University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Foundational models such as Multimodal Large Language Models (MLLMs) with their ability to interpret images and generate intricate responses has led to their widespread adoption across multiple computer vision and natural language processing tasks. However, they suffer from hallucinations and struggle to reason over complex reasoning tasks. In this work, we evaluate the performance of MLLMs across multiple multimodal augmentations and evaluate their performance in out-of-distribution settings. We benchmark 3 models, across 2 vision-language datasets, VQAv2 and CLEVR, and assess their performance across adversarial transformations in both the vision and language modalities. We introduce image perturbations using various augmentations, including noise addition, blurring, and median filtering and generate adversarial questions which contain conjunctions, disjunctions and negations. Additionally, we conduct a detailed fine-grained analysis to assess the model’s performance on particular question categories, such as those related to shape and color, across images featuring identical or varying objects. Our findings indicate a notable decrease in the performance of current MLLMs for synthetic images, with a gradual decline observed across both vision and language augmentations. Specifically, Gaussian Noise Addition emerges as the most detrimental augmentation, and we observe a significant drop in performance with complex questions containing multiple connectives. In these times of rapid development and deployment of MLLMs in real-world settings, we believe our findings are a first step towards benchmarking the robustness and out-of-distribution behavior of such models.

关键词： computer vision Analytical models Shape Large language models Computational modeling Perturbation methods Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

LaDiffGAN: Training GANs with Diffusion Supervision in Latent Spaces

LaDiffGAN: Training GANs with Diffusion Supervision in Laten...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xuhui Liu Bohan Zeng Sicheng Gao Shanglin Li Yutang Feng Hong Li Boyu Liu Jianzhuang Liu Baochang Zhang Beihang University Shenzhen Institute of Advanced Technology Shenzhen China Zhongguancun Laboratory Beijing China Nanchang Institute of Technology Nanchang China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Diffusion models have recently become increasingly popular in a number of computer vision tasks, but they fail to achieve satisfactory results for unsupervised image-to-image translation, since they require massive training data and rely heavily on extra guidance. In this scenario, GANs can alleviate these issues existing in diffusion models, albeit with suboptimal quality. In this paper, we leverage the advantages of both GANs and diffusion models by training GANs with diffusion supervision in latent spaces (LaDiffGAN) to solve the unsupervised image-to-image translation task. Firstly, to promote style transfer quality, we encode the data in specific latent spaces with styles of the target and source domains. Secondly, we introduce the diffusion process with different amounts of Gaussian noise to enhance the modeling capability of GANs on the complex data distribution. We accordingly design a latent diffusion GAN loss to align the latent features between generated and training images. Lastly, we introduce a heterogeneous conditional denoising loss that incorporates image-level supervision to further improve the quality of generated results. Our LaDiffGAN significantly alleviates the drawbacks associated with diffusion models, such as data leakage, high inference cost, and high dependence on large training data sets. Extensive experiments show that LaDiffGAN outperforms previous GAN models and delivers comparable or even better performance than diffusion models.

关键词： Training computer vision Image synthesis Gaussian noise Noise reduction Training data Diffusion processes

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 << < 218 219 220 221 222 223 224 225 226 227 > >>

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：