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

3,915 篇 会议
2 篇 期刊文献

馆藏范围

3,917 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

3,011 篇 工学
- 2,919 篇 计算机科学与技术...
- 216 篇 软件工程
- 141 篇 机械工程
- 133 篇 光学工程
- 42 篇 生物工程
- 28 篇 信息与通信工程
- 25 篇 电气工程
- 17 篇 控制科学与工程
- 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
1,559 篇 医学
- 1,558 篇 临床医学
- 3 篇 基础医学(可授医学...
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
14 篇 管理学
- 7 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

2,408 篇 computer vision
1,085 篇 training
1,043 篇 pattern recognit...
805 篇 conferences
709 篇 computational mo...
543 篇 visualization
491 篇 computer archite...
446 篇 three-dimensiona...
410 篇 semantics
409 篇 benchmark testin...
383 篇 codes
331 篇 transformers
290 篇 deep learning
277 篇 feature extracti...
260 篇 neural networks
256 篇 task analysis
238 篇 shape
216 篇 image segmentati...
204 篇 measurement
202 篇 object detection

机构

72 篇 tsinghua univ pe...
59 篇 univ sci & techn...
55 篇 zhejiang univ pe...
55 篇 chinese univ hon...
51 篇 carnegie mellon ...
51 篇 peng cheng lab p...
49 篇 swiss fed inst t...
47 篇 sensetime res pe...
46 篇 shanghai ai lab ...
42 篇 univ hong kong p...
38 篇 huawei noahs ark...
35 篇 univ chinese aca...
35 篇 shanghai jiao to...
33 篇 alibaba grp peop...
32 篇 tech univ munich...
31 篇 stanford univ st...
30 篇 peking univ peop...
30 篇 swiss fed inst t...
29 篇 adobe res san jo...
29 篇 google res mount...

作者

63 篇 timofte radu
30 篇 van gool luc
19 篇 yang yi
19 篇 qiao yu
18 篇 loy chen change
16 篇 zhang lei
16 篇 radu timofte
15 篇 sun jian
14 篇 liu yang
14 篇 liu shuaicheng
14 篇 tao dacheng
13 篇 li xin
13 篇 fan haoqiang
13 篇 chen wei-ting
12 篇 luo ping
12 篇 chen dongdong
12 篇 wang kai
12 篇 wang xinchao
12 篇 torralba antonio
12 篇 ghanem bernard

语言

3,916 篇 英文
1 篇 其他

检索条件"任意字段=2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022"

共 3917 条记录，以下是3661-3670 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：

Fast Human Head and Shoulder Detection Using Convolutional Networks and RGBD Data

Fast Human Head and Shoulder Detection Using Convolutional N...

引用

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

作者： Wassim A. El Ahmar Farzan Erlik Nowruzi Robert Laganiere Department of Electrical Engineering and Computer Science University of Ottawa Ottawa Ontario Canada

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We introduce a new real-time approach for human head and shoulder detection from RGB-D data based on a combination of image processing and deep learning approaches. Candidate head-top locations (CHL) are generated from a fast and accurate image processing algorithm that operates on depth data. We propose enhancements to the CHL algorithm making it three times faster. Various deep learning models are then evaluated for the tasks of classification and detection on the candidate head-top locations to regress the head bounding boxes and detect shoulder keypoints. We propose three different models based on convolutional neural networks for this problem. Experimental results for different architectures of our model are discussed. We also compare the performance of our models to other state of the art methods in terms of accuracy of detections and computational cost and show that our proposed models are on par with the state of the art in terms of precision-recall of head detection and precision of shoulders detection, with the biggest advantage of our models being in terms of computation time. We also analyze the effect of adding the depth channel on the performance of the network.

关键词： Head Proposals Magnetic heads Machine learning Three-dimensional displays Training Task analysis

来源：评论

学校读者我要写书评

暂无评论

Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition

Bracketing Image Restoration and Enhancement with High-Low F...

引用

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

作者： Genggeng Chen Kexin Dai Kangzhen Yang Tao Hu Xiangyu Chen Yongqing Yang Wei Dong Peng Wu Yanning Zhang Qingsen Yan Xi’an University of Architecture and Technology Northwestern Polytechnical University University of Macau Xi’an Institute of Optics and Precision Mechanics of CAS

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In real-world scenarios, due to a series of image degradations, obtaining high-quality, clear content photos is challenging. While significant progress has been made in synthesizing high-quality images, previous methods for image restoration and enhancement often overlooked the characteristics of different degradations. They applied the same structure to address various types of degradation, resulting in less-than-ideal restoration outcomes. Inspired by the notion that high/low frequency information is applicable to different degradations, we introduce HLNet, a Bracketing Image Restoration and Enhancement method based on high-low frequency decomposition. Specifically, we employ two modules for feature extraction: shared weight modules and non-shared weight modules. In the shared weight modules, we use SCConv to extract common features from different degradations. In the non-shared weight modules, we introduce the High-Low Frequency Decomposition Block (HLFDB), which employs different methods to handle high-low frequency information, enabling the model to address different degradations more effectively. Compared to other networks, our method takes into account the characteristics of different degradations, thus achieving higher-quality image restoration.

关键词： Degradation computer vision conferences Feature extraction Image restoration pattern recognition Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Multi-Modal Fusion of Event and RGB for Monocular Depth Estimation Using a Unified Transformer-based Architecture

Multi-Modal Fusion of Event and RGB for Monocular Depth Esti...

引用

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

作者： Anusha Devulapally Md Fahim Faysal Khan Siddharth Advani Vijaykrishnan Narayanan The Pennsylvania State University Samsung Electronics America

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the field of robotics and autonomous navigation, accurate pixel-level depth estimation has gained significant importance. Event cameras or dynamic vision sensors, capture asynchronous changes in brightness at the pixel level, offering benefits such as high temporal resolution, no motion blur, and a wide dynamic range. However, unlike traditional cameras that measure absolute intensity, event cameras lack the ability to provide scene context. Efficiently combining the advantages of both asynchronous events and synchronous RGB images to enhance depth estimation remains a challenge. In our study, we introduce a unified transformer that combines both event and RGB modalities to achieve precise depth prediction. In contrast to individual transformers for input modalities, a unified transformer model captures inter-modal dependencies and uses self-attention to enhance event-RGB contextual interactions. This approach exceeds the performance of recurrent neural network (RNN) methods used in state-of-the-art models. To encode the temporal information from events, convLSTMs are used before the transformer to improve depth estimation. Our proposed architecture outperforms the existing approaches in terms of absolute mean depth error, achieving state-of-the-art results in most cases. Additionally, the performance is also seen in other metrics like RMSE, absolute relative difference and depth thresholds compared to the existing approaches. The source code is available at: https://***/anusha-devulapally/ER-F2D.

关键词： Measurement computer vision Accuracy Recurrent neural networks Robot vision systems Estimation computer architecture

来源：评论

学校读者我要写书评

暂无评论

Eff-UNet: A Novel Architecture for Semantic Segmentation in Unstructured Environment

Eff-UNet: A Novel Architecture for Semantic Segmentation in ...

引用

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

作者： Bhakti Baheti Shubham Innani Suhas Gajre Sanjay Talbar SGGSIET Center of Excellence in Signal and Image Processing Nanded India

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Since the last few decades, the number of road causalities has seen continuous growth across the globe. Nowa-days intelligent transportation systems are being developed to enable safe and relaxed driving and scene understanding of the surrounding environment is an integral part of it. While several approaches are being developed for semantic scene segmentation based on deep learning and Convolutional Neural Network (CNN), these approaches assume well structured road infrastructure and driving environment. We focus our work on recent India Driving Lite Dataset (IDD), which contains data from unstructured driving environment and was hosted as an online challenge in NCVPRIPG 2019. We propose a novel architecture named as Eff-UNet which combines the effectiveness of compound scaled EfficientNet as the encoder for feature extraction with UNet decoder for reconstructing the fine-grained segmentation map. High level feature information as well as low level spatial information useful for precise segmentation are combined. The proposed architecture achieved 0.7376 and 0.6276 mean Intersection over Union (mIoU) on validation and test dataset respectively and won first prize in IDD lite segmentation challenge outperforming other approaches in the literature.

关键词： Image segmentation Semantics Roads Feature extraction Decoding Image resolution computer architecture

来源：评论

学校读者我要写书评

暂无评论

Dynamic Inference: A New Approach Toward Efficient Video Action recognition

Dynamic Inference: A New Approach Toward Efficient Video Act...

引用

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

作者： Wenhao Wu Dongliang He Xiao Tan Shifeng Chen Yi Yang Shilei Wen MMLab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Computer Vision Technology (VIS) Baidu Inc. China University of Technology Sydney Australia

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Though action recognition in videos has achieved great success recently, it remains a challenging task due to the massive computational cost. Designing lightweight networks is a possible solution, but it may degrade the recognition performance. In this paper, we innovatively propose a general dynamic inference idea to improve inference efficiency by leveraging the variation in the distinguishability of different videos. The dynamic inference approach can be achieved from aspects of the network depth and the number of input video frames, or even in a joint input-wise and network depth-wise manner. In a nutshell, we treat input frames and network depth of the computational graph as a 2-dimensional grid, and several checkpoints are placed on this grid in advance with a prediction module. The inference is carried out progressively on the grid by following some predefined route, whenever the inference process comes across a checkpoint, an early prediction can be made depending on whether the early stop criteria meets. For the proof-of-concept purpose, we instantiate several dynamic inference frameworks. In these instances, we overcome the drawback of limited temporal coverage resulted from an early prediction by a novel frame permutation scheme, and alleviate the conflict between progressive computation and video temporal relation modeling by introducing the online temporal shift module. Extensive experiments are conducted to thoroughly analyze the effectiveness of our ideas and to inspire future research efforts. Results on various datasets also evident the superiority of our approach.

关键词： Computational modeling Convolution Three-dimensional displays Writing Solid modeling Feature extraction computer vision

来源：评论

学校读者我要写书评

暂无评论

Deep Lighting Environment Map Estimation from Spherical Panoramas

Deep Lighting Environment Map Estimation from Spherical Pano...

引用

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

作者： Vasileios Gkitsas Nikolaos Zioulis Federico Alvarez Dimitrios Zarpalas Petros Daras Centre for Research and Technology Hellas (CERTH) Information Technologies Institute (ITI) Signals Systems and Radiocommunications Department (SSRD) Universidad Politecnica de Madrid (UPM)

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Estimating a scene's lighting is a very important task when compositing synthetic content within real environments, with applications in mixed reality and postproduction. In this work we present a data-driven model that estimates an HDR lighting environment map from a single LDR monocular spherical panorama. In addition to being a challenging and ill-posed problem, the lighting estimation task also suffers from a lack of facile illumination ground truth data, a fact that hinders the applicability of data-driven methods. We approach this problem differently, exploiting the availability of surface geometry to employ image-based relighting as a data generator and supervision mechanism. This relies on a global Lambertian assumption that helps us overcome issues related to pre-baked lighting. We relight our training data and complement the model's supervision with a photometric loss, enabled by a differentiable image-based relighting technique. Finally, since we predict spherical spectral coefficients, we show that by imposing a distribution prior on the predicted coefficients, we can greatly boost performance. Code and models available at ***/DeepPanoramaLighting.

关键词： Lighting Estimation Task analysis Geometry Data models Image color analysis Rendering (computer graphics)

来源：评论

学校读者我要写书评

暂无评论

Blurry-Consistency Segmentation Framework with Selective Stacking on Differential Interference Contrast 3D Breast Cancer Spheroid

Blurry-Consistency Segmentation Framework with Selective Sta...

引用

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

作者： Thanh-Huy Nguyen Thi Kim Ngan Ngo Mai Anh Vu Ting-Yuan Tu Department of Biomedical Engineering IMBSL Lab National Cheng Kung University Tainan ROC

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The ability of three-dimensional (3D) spheroid modeling to study the invasive behavior of breast cancer cells has drawn increased attention. The deep learning-based image processing framework is very effective at speeding up the cell morphological analysis process. Out-of-focus photos taken while capturing 3D cells under several z-slices, however, could negatively impact the deep learning model. In this work, we created a new algorithm to handle blurry images while preserving the stacked image quality. Furthermore, we proposed a unique training architecture that leverages consistency training to help reduce the bias of the model when dense-slice stacking is applied. Additionally, the model’s stability is increased under the sparse-slice stacking effect by utilizing the self-training approach. The new blurring stacking technique and training flow are combined with the suggested architecture and self-training mechanism to provide an innovative yet easy-to-use framework. Our methods produced noteworthy experimental outcomes in terms of both quantitative and qualitative aspects.

关键词： Training Solid modeling Three-dimensional displays Microscopy Stacking computer architecture Breast cancer

来源：评论

学校读者我要写书评

暂无评论

Efficient and Accurate Face Alignment by Global Regression and Cascaded Local Refinement

Efficient and Accurate Face Alignment by Global Regression a...

引用

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

作者： Jinzhan Su Zhe Wang Chunyuan Liao Haibin Ling Meitu Inc Hiscene Technology Temple University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Despite great advances witnessed on facial image alignment in recent years, high accuracy high speed face alignment algorithms still have rooms to improve especially for applications where computation resources are limited. Addressing this issue, we propose a new face landmark localization algorithm by combining global regression and local refinement. In particular, for a given image, our algorithm first estimates its global facial shape through a global regression network (GRegNet) and then using cascaded local refinement networks (LRefNet) to sequentially improve the alignment result. Compared with previous face alignment algorithms, our key innovation is the sharing of low level features in GRegNet with LRefNet. Such feature sharing not only significantly improves the algorithm efficiency, but also allows full exploration of rich locality-sensitive details carried with shallow network layers and consequently boosts the localization accuracy. The advantages of our algorithm is clearly validated in our thorough experiments on four popular face alignment benchmarks, 300-W, AFLW, COFW and WFLW. On all datasets, our algorithm produces state-of-the-art alignment accuracy, while enjoys the smallest computational complexity.

关键词： Shape Face Feature extraction Estimation Task analysis computer architecture Regression tree analysis

来源：评论

学校读者我要写书评

暂无评论

Exploring the Role of Audio in Video Captioning

Exploring the Role of Audio in Video Captioning

引用

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

作者： Yuhan Shen Linjie Yang Longyin Wen Haichao Yu Ehsan Elhamifar Heng Wang Northeastern University ByteDance

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent focus in video captioning has been on designing architectures that can consume both video and text modalities, and using large-scale video datasets with text transcripts for pre-training, such as HowTo100M. Though these approaches have achieved significant improvement, the audio modality is often ignored in video captioning. In this work, we present an audio-visual framework, which aims to fully exploit the potential of the audio modality for captioning. Instead of relying on text transcripts extracted via automatic speech recognition (ASR), we argue that learning with raw audio signals can be more beneficial, as audio has additional information including acoustic events, speaker identity, etc. Our contributions are twofold. First, we observed that the model overspecializes to the audio modality when pre-training with both video and audio modality, since the ground truth (i.e., text transcripts) can be solely predicted using audio. We proposed a Modality Balanced Pre-training (MBP) loss to mitigate this issue and significantly improve the performance on downstream tasks. Second, we slice and dice different design choices of the cross-modal module, which may become an information bottleneck and generate inferior results. We proposed new local-global fusion mechanisms to improve information exchange across audio and video. We demonstrate significant improvements by leveraging the audio modality on four datasets, and even outperform the state of the art on some metrics without relying on the text modality as the input.

关键词： Measurement computer vision Computational modeling conferences Predictive models Acoustics Data mining

来源：评论

学校读者我要写书评

暂无评论

Ternary MobileNets via Per-Layer Hybrid Filter Banks

Ternary MobileNets via Per-Layer Hybrid Filter Banks

引用

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

作者： Dibakar Gope Jesse Beu Urmish Thakker Matthew Mattina Arm ML Research Lab

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

MobileNets family of computer vision neural networks have fueled tremendous progress in the design and organization of resource-efficient architectures in recent years. New applications with stringent real-time requirements on highly constrained devices require further compression of MobileNets-like compute-efficient networks. Model quantization is a widely used technique to compress and accelerate neural network inference and prior works have quantized MobileNets to 4 - 6 bits, albeit with a modest to significant drop in accuracy. While quantization to sub-byte values (i.e. precision ≤ 8 bits) has been valuable, even further quantization of MobileNets to binary or ternary values is necessary to realize significant energy savings and possibly runtime speedups on specialized hardware, such as ASICs and FPGAs. Under the key observation that convolutional filters at each layer of a deep neural network may respond differently to ternary quantization, we propose a novel quantization method that generates per-layer hybrid filter banks consisting of full-precision and ternary weight filters for MobileNets. Using this proposed quantization method, we quantize a substantial portion of weight filters of MobileNets to ternary values resulting in a 27.98% savings in energy, and a 51.07% reduction in the model size, while achieving comparable accuracy and no degradation in throughput on specialized hardware in comparison to the baseline full-precision MobileNets. Finally, we demonstrate the generalizability and effectiveness of hybrid filter banks to other neural network architectures.

关键词： Convolution Quantization (signal) computer architecture Neural networks Throughput Hardware Computational modeling

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392页 << < 363 364 365 366 367 368 369 370 371 372 > >>

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：