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

8,902 篇 会议
42 篇 期刊文献
18 册 图书

馆藏范围

8,961 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

4,560 篇 工学
- 4,019 篇 计算机科学与技术...
- 2,178 篇 软件工程
- 1,242 篇 光学工程
- 555 篇 控制科学与工程
- 431 篇 信息与通信工程
- 429 篇 机械工程
- 294 篇 电气工程
- 287 篇 仪器科学与技术
- 179 篇 生物工程
- 159 篇 生物医学工程（可授...
- 119 篇 电子科学与技术（可...
- 61 篇 安全科学与工程
- 58 篇 建筑学
- 58 篇 化学工程与技术
- 52 篇 土木工程
- 49 篇 交通运输工程
- 40 篇 力学（可授工学、理...
2,065 篇 理学
- 1,382 篇 物理学
- 1,198 篇 数学
- 420 篇 统计学（可授理学、...
- 238 篇 生物学
- 54 篇 化学
- 36 篇 系统科学
263 篇 管理学
- 180 篇 图书情报与档案管...
- 89 篇 管理科学与工程(可...
- 47 篇 工商管理
222 篇 医学
- 221 篇 临床医学
- 39 篇 基础医学(可授医学...
205 篇 艺术学
- 205 篇 设计学（可授艺术学...
45 篇 法学
- 43 篇 社会学
21 篇 农学
14 篇 教育学
9 篇 经济学
6 篇 军事学

主题

3,413 篇 computer vision
1,213 篇 pattern recognit...
946 篇 cameras
908 篇 conferences
765 篇 computer science
674 篇 image segmentati...
626 篇 layout
592 篇 training
548 篇 shape
513 篇 robustness
454 篇 feature extracti...
450 篇 humans
444 篇 face recognition
410 篇 computational mo...
402 篇 object detection
356 篇 computer archite...
354 篇 visualization
336 篇 application soft...
304 篇 lighting
255 篇 pixel

机构

41 篇 microsoft resear...
30 篇 department of co...
25 篇 department of co...
23 篇 institute for co...
22 篇 department of co...
22 篇 school of comput...
20 篇 university of sc...
20 篇 swiss fed inst t...
19 篇 tsinghua univers...
19 篇 institute of com...
18 篇 swiss fed inst t...
17 篇 the robotics ins...
17 篇 carnegie mellon ...
17 篇 computer vision ...
17 篇 department of co...
16 篇 institute of inf...
16 篇 school of comput...
15 篇 school of comput...
15 篇 carnegie mellon ...
15 篇 computer science...

作者

57 篇 timofte radu
27 篇 huang thomas s.
24 篇 van gool luc
22 篇 nayar shree k.
22 篇 t. kanade
21 篇 jain anil k.
20 篇 luc van gool
20 篇 s.k. nayar
19 篇 t.s. huang
18 篇 xiaoou tang
18 篇 murino vittorio
18 篇 horst bischof
18 篇 t. darrell
17 篇 a.k. jain
17 篇 bischof horst
16 篇 g. healey
16 篇 bowyer kevin w.
16 篇 heung-yeung shum
15 篇 m.j. black
15 篇 li stan z.

语言

8,913 篇 英文
40 篇 其他
8 篇 中文
1 篇 土耳其文

检索条件"任意字段=IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops"

共 8962 条记录，以下是1531-1540 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：

COVER: A Comprehensive Video Quality Evaluator

COVER: A Comprehensive Video Quality Evaluator

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Chenlong He Qi Zheng Ruoxi Zhu Xiaoyang Zeng Yibo Fan Zhengzhong Tu Fudan University University of Texas at Austin

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Video quality assessment, especially for a massive scale of user-generated content, is an essential yet challenging computer vision and video analysis problem. Prior methods have been shown to be effective in mirroring subjective human opinion scores; however, they fail to capture the complicated, multi-dimensional aspects of factors that impact the overall perceptual quality. In this paper, we introduce COVER, a comprehensive video quality evaluator, a novel framework designed to evaluate video quality holistically — from a technical, aesthetic, and semantic perspective. Specifically, COVER leverages three parallel branches: (1) a Swin Transformer backbone implemented on spatially sampled crops to predict technical quality; (2) a ConvNet employed on subsampled frames to derive aesthetic quality; (3) a CLIP image encoder executed on re-sized frames to obtain semantic quality. We further propose a simplified cross-gating block to interact with the three branches before feeding into the predicting head. The final quality score is attained using a weighted sum of each sub-score, making a multi-faceted metric. Our experimental results demonstrate that COVER exceeds the state-of-the-art models in multiple UGC video quality datasets. Moreover, COVER offers a diagnosable quality report to explain the quality score in multiple pillars, while it is capable of processing 1080p videos at 3x faster speed than the real-time requirement. To facilitate future research on efficient and explainable video quality research, the code is available at https://***/vztu/COVER.

关键词： computer vision Visualization Semantics User-generated content Streaming media Feature extraction Transformers

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

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

Flow cytometry with event-based vision and spiking neuromorphic hardware

Flow cytometry with event-based vision and spiking neuromorp...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Steven Abreu Muhammed Gouda Alessio Lugnan Peter Bienstman University of Groningen Groningen Netherlands Ghent University - Imec Ghent Belgium

Imaging flow cytometry systems play a critical role in the identification and characterization of large populations of cells or micro-particles. Such systems typically leverage deep artificial neural networks to classify samples. Here we show that an event-based camera and neuromorphic processor can be used in a flow cytometry setup to solve a binary particle classification task with less memory usage, and promising improvements in latency and energy scaling. To reduce the complexity of the spiking neural network, we combine the event-based camera with a free-space optical setup which acts as a non-linear high-dimensional feature map that is computed at the speed of light before the event-based camera receives the signal. We demonstrate, for the first time, a spiking neural network running on neuromorphic hardware for a fully event-based flow cytometry pipeline with 98.45% testing accuracy. Our best artificial neural network on frames of the same data reaches only 97.51%, establishing a neuromorphic advantage also in classification accuracy. We further show that our system will scale favorably to more complex classification tasks. We pave the way for real-time classification with throughput of up to 1,000 samples per second and open up new possibilities for online and on-chip learning in flow cytometry applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ATOM: Attention Mixer for Efficient Dataset Distillation

ATOM: Attention Mixer for Efficient Dataset Distillation

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Samir Khaki Ahmad Sajedi Kai Wang Lucy Z. Liu Yuri A. Lawryshyn Konstantinos N. Plataniotis University of Toronto National University of Singapore Royal Bank of Canada (RBC)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent works in dataset distillation seek to minimize training expenses by generating a condensed synthetic dataset that encapsulates the information present in a larger real dataset. These approaches ultimately aim to attain test accuracy levels akin to those achieved by models trained on the entirety of the original dataset. Previous studies in feature and distribution matching have achieved significant results without incurring the costs of bi-level optimization in the distillation process. Despite their convincing efficiency, many of these methods suffer from marginal downstream performance improvements, limited distillation of contextual information, and subpar cross-architecture generalization. To address these challenges in dataset distillation, we propose the ATtentiOn Mixer (ATOM) module to efficiently distill large datasets using a mixture of channel and spatial-wise attention in the feature matching process. Spatial-wise attention helps guide the learning process based on consistent localization of classes in their respective images, allowing for distillation from a broader receptive field. Meanwhile, channel-wise attention captures the contextual information associated with the class itself, thus making the synthetic image more informative for training. By integrating both types of attention, our ATOM module demonstrates superior performance across various computer vision datasets, including CIFAR10/100 and Tiny-Imagenet. Notably, our method significantly improves performance in scenarios with a low number of images per class, thereby enhancing its potential. Furthermore, we maintain the improvement on cross-architectures and applications such as neural architecture search.

关键词： Training Location awareness computer vision Costs Atomic layer deposition Performance gain Transformers

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

MTN: Forensic Analysis of MP4 Video Files Using Graph Neural Networks

MTN: Forensic Analysis of MP4 Video Files Using Graph Neural...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ziyue Xiang Amit Kumar Singh Yadav Paolo Bestagini Stefano Tubaro Edward J. Delp Video and Image Processing Lab (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milano Italy

MP4 video files are stored using a tree data structure. These trees contain rich information that can be used for forensic analysis. In this paper, we propose MP4 Tree Network (MTN), an approach based on an end-to-end Graph Neural Networks (GNNs) that is used for forensic analysis of MP4 trees. MTN does not use any video pixel data. MTN is trained using Self-Supervised Learning (SSL), which generates semantic-preserving node embeddings for the nodes in an MP4 tree. We also propose a data augmentation technique for MP4 trees, which helps train MTN in data-scarce scenarios. MTN achieves good performance across 3 video forensics tasks on the EVA-7K dataset. We show that MTN can gain more comprehensive understanding about the MP4 trees and is more robust to potential attacks compared to existing methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Data-Driven Approach based on Dynamic Mode Decomposition for Efficient Encoding of Dynamic Light Fields

A Data-Driven Approach based on Dynamic Mode Decomposition f...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Joshitha Ravishankar Sally Khaidem Mansi Sharma Department of Electrical Engineering Indian Institute of Technology Madras India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

Dynamic light fields provide a richer, more realistic 3D representation of a moving scene. However, this leads to higher data rates since excess storage and transmission requirements are needed. We propose a novel approach to efficiently represent and encode dynamic light field data for display applications based on dynamic mode decomposition (DMD). Acquired images are firstly obtained through optimized coded aperture patterns for each temporal frame/camera viewpoint of a dynamic light field. The underlying spatial, angular, and temporal correlations are effectively exploited by a data-driven DMD on these acquired images arranged as time snapshots. Next, High Efficiency Video Coding (HEVC) removes redundancies in light field data, including intra-frame and inter-frame redundancies, while maintaining high reconstruction quality. The proposed scheme is the first of its kind to treat light field videos as mathematical dynamical systems, leverage on dynamic modes of acquired images, and gain flexible coding at various bitrates. Experimental results demonstrate our scheme’s superior compression efficiency and bitrate savings compared to the direct encoding of acquired images using HEVC codec.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Training Strategies for vision Transformers for Object Detection

Training Strategies for Vision Transformers for Object Detec...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Apoorv Singh Motional USA

vision-based Transformer have shown huge application in the perception module of autonomous driving in terms of predicting accurate 3D bounding boxes, owing to their strong capability in modeling long-range dependencies between the visual features. However Transformers, initially designed for language models, have mostly focused on the performance accuracy, and not so much on the inference-time budget. For a safety critical system like autonomous driving, real-time inference at the on-board compute is an absolute necessity. This keeps our object detection algorithm under a very tight run-time budget. In this paper, we evaluated a variety of strategies to optimize on the inference-time of vision transformers based object detection methods keeping a close-watch on any performance variations. Our chosen metric for these strategies is accuracy-runtime joint optimization. Moreover, for actual inference-time analysis we profile our strategies with float32 and float16 precision with TensorRT module. This is the most common format used by the industry for deployment of their Machine Learning networks on the edge devices. We showed that our strategies are able to improve inference-time by 63% at the cost of performance drop of mere 3% for our problem-statement defined in Sec. 3. These strategies brings down vision Transformers detectors [3], [15], [18], [19], [36] inference-time even less than traditional single-image based CNN detectors like FCOS [17], [25], [33]. We recommend practitioners use these techniques to deploy Transformers based hefty multi-view networks on a budge-constrained robotic platform.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection

Back to the Feature: Classical 3D Features are (Almost) All ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Eliahu Horwitz Yedid Hoshen School of Computer Science and Engineering The Hebrew University of Jerusalem Israel

Despite significant advances in image anomaly detection and segmentation, few methods use 3D information. We utilize a recently introduced 3D anomaly detection dataset to evaluate whether or not using 3D information is a lost opportunity. First, we present a surprising finding: standard color-only methods outperform all current methods that are explicitly designed to exploit 3D information. This is counter-intuitive as even a simple inspection of the dataset shows that color-only methods are insufficient for images containing geometric anomalies. This motivates the question: how can anomaly detection methods effectively use 3D information? We investigate a range of shape representations including hand-crafted and deep-learning-based; we demonstrate that rotation invariance plays the leading role in the performance. We uncover a simple 3D-only method that beats all recent approaches while not using deep learning, external pre-training datasets, or color information. As the 3D-only method cannot detect color and texture anomalies, we combine it with color-based features, significantly outperforming previous state-of-the-art. Our method, dubbed BTF (Back to the Feature) achieves pixel-wise ROCAUC: 99.3% and PRO: 96.4% on MVTec 3D-AD.

关键词：

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 << < 150 151 152 153 154 155 156 157 158 159 > >>

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：