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

8,905 篇 会议
43 篇 期刊文献
18 册 图书

馆藏范围

8,965 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

4,564 篇 工学
- 4,024 篇 计算机科学与技术...
- 2,182 篇 软件工程
- 1,241 篇 光学工程
- 558 篇 控制科学与工程
- 433 篇 信息与通信工程
- 430 篇 机械工程
- 294 篇 电气工程
- 288 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...
- 64 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
- 52 篇 交通运输工程
- 40 篇 力学（可授工学、理...
2,066 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
- 55 篇 化学
- 36 篇 系统科学
266 篇 管理学
- 182 篇 图书情报与档案管...
- 92 篇 管理科学与工程(可...
- 47 篇 工商管理
223 篇 医学
- 222 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

3,414 篇 computer vision
1,216 篇 pattern recognit...
946 篇 cameras
908 篇 conferences
765 篇 computer science
674 篇 image segmentati...
618 篇 layout
598 篇 training
548 篇 shape
518 篇 robustness
451 篇 feature extracti...
448 篇 humans
445 篇 face recognition
405 篇 computational mo...
402 篇 object detection
365 篇 visualization
356 篇 computer archite...
336 篇 application soft...
304 篇 lighting
257 篇 image reconstruc...

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 ...
14 篇 national laborat...

作者

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

语言

8,904 篇 英文
53 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 8966 条记录，以下是1471-1480 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 and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report

Fast and Accurate Single-Image Depth Estimation on Mobile De...

引用

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

作者： Ignatov, Andrey Malivenko, Grigory Plowman, David Shukla, Samarth Timofte, Radu Zhang, Ziyu Wang, Yicheng Huang, Zilong Luo, Guozhong Yu, Gang Fu, Bin Wang, Yiran Li, Xingyi Shi, Min Xian, Ke Cao, Zhiguo Du, Jin-Hua Wu, Pei-Lin Ge, Chao Yao, Jiaoyang Tu, Fangwen Li, Bo Yoo, Jung Eun Seo, Kwanggyoon Xu, Jialei Li, Zhenyu Liu, Xianming Jiang, Junjun Chen, Wei-Chi Joya, Shayan Fan, Huanhuan Kang, Zhaobing Li, Ang Feng, Tianpeng Liu, Yang Sheng, Chuannan Yin, Jian Benavides, Fausto T. Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Raspberry Pi Trading Ltd Cambridge England AI Witchlabs Zurich Switzerland Tencent GY Lab Shenzhen Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Sch Artificial Intelligence & Automat Wuhan Peoples R China Chinese Acad Sci Inst Automat Nanjing Artificial Intelligence Chip Res Beijing Peoples R China Black Sesame Technol Inc Singapore Singapore Korea Adv Inst Sci & Technol Visual Media Lab Daejeon South Korea Harbin Inst Technol Harbin Peoples R China Peng Cheng Lab Shenzhen Peoples R China Natl Cheng Kung Univ Multimedia & Comp Vis Lab Tainan Taiwan Samsung Res UK Cambridge England OPPO Res Inst Beijing Peoples R China Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665448994

Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms. For this, the participants were provided with a new large-scale dataset containing RGB-depth image pairs obtained with a dedicated stereo ZED camera producing high-resolution depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the popular Raspberry Pi 4 platform with a mobile ARM-based Broadcom chipset. The proposed solutions can generate VGA resolution depth maps at up to 10 FPS on the Raspberry Pi 4 while achieving high fidelity results, and are compatible with any Android or Linux-based mobile devices. A detailed description of all models developed in the challenge is provided in this paper.

关键词： Meters computer vision Image resolution conferences Estimation Real-time systems pattern recognition

来源：评论

学校读者我要写书评

暂无评论

MARRS: Modern Backbones Assisted Co-training for Rapid and Robust Semi-Supervised Domain Adaptation

MARRS: Modern Backbones Assisted Co-training for Rapid and R...

引用

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

作者： Saurabh Kumar Jain Sukhendu Das Department of Computer Science Engineering Visualization and Perception Lab Indian Institute of Technology Madras India

Semi-Supervised Domain Adaptation (SSDA) aims to develop domain invariant models from scarcely labeled target domain in addition to the fully labeled source domain. Current SSDA works are often applied in conjunction with ResNet34 backbone, which makes them overlook the advantages of utilizing other backbones. Hence, in this paper, we investigate the impact of employing different modern backbones in SSDA and propose a novel solution named Modern Backbones Assisted Co-training for Rapid and Robust Semi-Supervised Domain Adaptation (MARRS), that uses discriminative features of two modern backbones for training linear classifiers using the well established co-training framework. To induce diversity among classifiers for effective co-training, we propose a novel module that produces diversity at three levels, namely image, backbone, and feature distribution levels. Experiments reveal that MARRS not only achieves state-of-the-art performance across all popular SSDA datasets, but also drastically cuts the computation time compared to other SSDA approaches, making MARRS a rapid and robust solution for SSDA. We also provide extensive ablation experiments to verify our framework’s vitality and primary design choices.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Language-guided Multi-modal Emotional Mimicry Intensity Estimation

Language-guided Multi-modal Emotional Mimicry Intensity Esti...

引用

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

作者： Feng Qiu Wei Zhang Chen Liu Lincheng Li Heming Du Tianchen Guo Xin Yu Netease Fuxi AI Lab The University of Queensland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Emotional Mimicry Intensity (EMI) estimation aims to identify the intensity of mimicry exhibited by individuals in response to observed emotions. The challenge in EMI estimation lies in discerning nuanced facial expression cues on mimicry behaviors based on the seed video and the text instructions. In this paper, we propose a multi-modal EMI estimation framework by leveraging visual, auditory, and textual modalities to capture a comprehensive emotional profile. We first extract representations for each modality separately and then fuse the modality-specific representations via a Temporal Segment Network, optimizing for temporal coherence and emotional context. Furthermore, we find that participants demonstrate notable proficiency in mimicking text instructions, yet exhibit less effectiveness in replicating facial expressions and vocal tones. In light of this, we design a contrastive learning mechanism to refine the extracted feature based on textual guidance. By doing so, features derived from similar text instructions are closely aligned, enhancing the estimation of emotional mimicry intensity by leveraging the dominant textual modality. Experiments conducted on the Hume-Vidmimic2 dataset illustrate the effectiveness of our framework in EMI estimation. Our framework is recognized as the leading solution in the Emotional Mimicry Intensity (EMI) Estimation Challenge at the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW). More information for the Competition can be found in: 6th ABAW.

关键词： Visualization Emotion recognition computer vision Fuses conferences Electromagnetic interference Estimation

来源：评论

学校读者我要写书评

暂无评论

DeDoDe v2: Analyzing and Improving the DeDoDe Keypoint Detector

DeDoDe v2: Analyzing and Improving the DeDoDe Keypoint Detec...

引用

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

作者： Johan Edstedt Georg Bökman Zhenjun Zhao Linköping University Chalmers University of Technology The Chinese University of Hong Kong Texas A&M University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we analyze and improve into the recently proposed DeDoDe keypoint detector. We focus our analysis on some key issues. First, we find that DeDoDe keypoints tend to cluster together, which we fix by performing nonmax suppression on the target distribution of the detector during training. Second, we address issues related to data augmentation. In particular, the DeDoDe detector is sensitive to large rotations. We fix this by including 90-degree rotations as well as horizontal flips. Finally, the decoupled nature of the DeDoDe detector makes evaluation of downstream usefulness problematic. We fix this by matching the keypoints with a pretrained dense matcher (RoMa) and evaluating two-view pose estimates. We find that the original long training is detrimental to performance, and therefore propose a much shorter training schedule. We integrate all these improvements into our proposed detector DeDoDe v2 and evaluate it with the original DeDoDe descriptor on the MegaDepth-1500 and IMC2022 benchmarks. Our proposed detector significantly increases pose estimation results, notably from 75.9 to 78.3 mAA on the IMC2022 challenge. Code and weights are available at ***/Parskatt/DeDoDe.

关键词： Training Schedules computer vision Codes conferences Pose estimation Detectors

来源：评论

学校读者我要写书评

暂无评论

Two Stage Dehazing Framework for Dense and Non-Homogeneous Dehazing

Two Stage Dehazing Framework for Dense and Non-Homogeneous D...

引用

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

作者： Wei Song Yichang Gao Jiahao Xiong Hualiang Lin Dong Li Yun Zhang Guangdong University of Technology Guangzhou China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In real-world environments, haze often causes a decrease in visibility, leading to potentially severe consequences. Although current methods based on the assumption of homogeneous haze density have achieved commendable results, dehazing techniques for non-homogeneous density haze still fall short in terms of visibility restoration and color accuracy. We observe that although single-stage methods have made significant strides, a multi-stage enhancement can further improve dehazing in terms of both visibility and color restoration. In this paper, we propose a two-stage dehaze framework, named Two Stage Dehazing Framework. Our approach consists of a DehazeNet, which does not require specifying a particular model for dehazing and can accept a hazy image as input, producing an clear image of the same dimensions as the original. Two such DehazeNet are sequentially connected to form the final serial DehazeNet. Moreover, to better approximate the output image to real-world scenes, we propose the Multi-Scale Attention Head. Our method achieved third place in NTIRE 2024 Dense and NonHomogeneous Dehazing Challenge, demonstrating outstanding performance metrics in the Peak Signal to Noise Ratio (PSNR), the Structral Similarity Index (SSIM), and the Mean Opinion Score (MOS). Related code will be acailable on code.

关键词： Training Measurement computer vision PSNR Head Codes Image color analysis

来源：评论

学校读者我要写书评

暂无评论

ELSA: Exploiting Layer-wise N:M Sparsity for vision Transformer Acceleration

ELSA: Exploiting Layer-wise N:M Sparsity for Vision Transfor...

引用

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

作者： Ning-Chi Huang Chi-Chih Chang Wei-Cheng Lin Endri Taka Diana Marculescu Kai-Chiang Wu National Yang Ming Chiao Tung University University of Texas at Austin

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

N:M sparsity is an emerging model compression method supported by more and more accelerators to speed up sparse matrix multiplication in deep neural networks. Most existing N:M sparsity methods compress neural networks with a uniform setting for all layers in a network or heuristically determine the layer-wise configuration by considering the number of parameters in each layer. However, very few methods have been designed for obtaining a layer-wise customized N:M sparse configuration for vision transformers (ViTs), which usually consist of transformer blocks involving the same number of *** this work, to address the challenge of selecting suitable sparse configuration for ViTs on N:M sparsity-supporting accelerators, we propose ELSA, Exploiting Layer-wise N:M Sparsity for ViTs. Considering not only all N:M sparsity levels supported by a given accelerator but also the expected throughput improvement, our methodology can reap the benefits of accelerators supporting mixed sparsity by trading off negligible accuracy loss with both memory usage and inference time reduction for ViT models. For instance, our approach achieves a noteworthy 2.9× reduction in FLOPs to both Swin-B and DeiT-B with only a marginal degradation of accuracy on ImageNet. Our code is publicly available at https://***/ningchihuang/ELSA.

关键词： Training Degradation computer vision Accuracy Transformers Throughput Software

来源：评论

学校读者我要写书评

暂无评论

How to Benchmark vision Foundation Models for Semantic Segmentation?

How to Benchmark Vision Foundation Models for Semantic Segme...

引用

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

作者： Tommie Kerssies Daan De Geus Gijs Dubbelman Eindhoven University of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent vision foundation models (VFMs) have demonstrated proficiency in various tasks but require supervised fine-tuning to perform the task of semantic segmentation effectively. Benchmarking their performance is essential for selecting current models and guiding future model developments for this task. The lack of a standardized benchmark complicates comparisons. Therefore, the primary objective of this paper is to study how VFMs should be benchmarked for semantic segmentation. To do so, various VFMs are fine-tuned under various settings, and the impact of individual settings on the performance ranking and training time is assessed. Based on the results, the recommendation is to fine-tune the ViT-B variants of VFMs with a 16 × 16 patch size and a linear decoder, as these settings are representative of using a larger model, more advanced decoder and smaller patch size, while reducing training time by more than 13 times. Using multiple datasets for training and evaluation is also recommended, as the performance ranking across datasets and domain shifts varies. Linear probing, a common practice for some VFMs, is not recommended, as it is not representative of end-to-end fine-tuning. The benchmarking setup recommended in this paper enables a performance analysis of VFMs for semantic segmentation. The findings of such an analysis reveal that pretraining with promptable segmentation is not beneficial, whereas masked image modeling (MIM) with abstract representations is crucial, even more important than the type of supervision used. The code for efficiently fine-tuning VFMs for semantic segmentation can be accessed through the project page 1 .

关键词： Training computer vision Analytical models Codes Semantic segmentation conferences Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

Salient Object-Aware Background Generation using Text-Guided Diffusion Models

Salient Object-Aware Background Generation using Text-Guided...

引用

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

作者： Amir Erfan Eshratifar João V. B. Soares Kapil Thadani Shaunak Mishra Mikhail Kuznetsov Yueh-Ning Ku Paloma De Juan Yahoo Research Amazon ByteDance

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Generating background scenes for salient objects plays a crucial role across various domains including creative design and e-commerce, as it enhances the presentation and context of subjects by integrating them into tailored environments. Background generation can be framed as a task of text-conditioned outpainting, where the goal is to extend image content beyond a salient object’s boundaries on a blank background. Although popular diffusion models for text-guided inpainting can also be used for outpainting by mask inversion, they are trained to fill in missing parts of an image rather than to place an object into a scene. Consequently, when used for background creation, inpainting models frequently extend the salient object’s boundaries and thereby change the object’s identity, which is a phenomenon we call "object expansion." This paper introduces a model for adapting inpainting diffusion models to the salient object outpainting task using Stable Diffusion and ControlNet architectures. We present a series of qualitative and quantitative results across models and datasets, including a newly proposed metric to measure object expansion that does not require any human labeling. Compared to Stable Diffusion 2.0 Inpainting, our proposed approach reduces object expansion by 3.6× on average with no degradation in standard visual metrics across multiple datasets.

关键词： Measurement Degradation Adaptation models Visualization conferences Diffusion models pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Latent Flow Diffusion for Deepfake Video Generation

Latent Flow Diffusion for Deepfake Video Generation

引用

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

作者： Aashish Chandra K Aashutosh A V Srijan Das Abhijit Das Birla Institute of Technology & Science Pilani India University of North Carolina Charlotte USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image-to-video generation with conditional identity swap popularly known as deepfake, aims to synthesize a new video for the target identity guided by an image of the target and a video of the source identity. The biggest challenge of these tasks lies in the simultaneous generation of realistic spatial appearance and temporal dynamics corresponding to the given target image and source video. In this paper, we propose a deepfake generation technique using novel latent flow diffusion (LFD) that includes an optical flow sequence in the latent space based on a given source video to warp the given target image. Compared to previous works on video diffusion, our proposed LFD can swap the spatial details maintaining temporal information by utilizing the spatial content of the given target image and employing the latent flow of the source video. Our model consists of three stages: a Flow predictor model captures the optical flow of the source video, two-fold Transformer encoding layers predict the driving frame and a conditioned image-to-video generator guided by the driving frame generates the final deep fake video. We conducted multiple experiments and our proposed model has consistently outperformed prior video diffusion models for deepfake generation.

关键词： Deepfakes computer vision Image motion analysis Image coding Predictive models Transformers Generators

来源：评论

学校读者我要写书评

暂无评论

PitcherNet: Powering the Moneyball Evolution in Baseball Video Analytics

PitcherNet: Powering the Moneyball Evolution in Baseball Vid...

引用

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

作者： Jerrin Bright Bavesh Balaji Yuhao Chen David A Clausi John S Zelek University of Waterloo Waterloo Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the high-stakes world of baseball, every nuance of a pitcher’s mechanics holds the key to maximizing performance and minimizing runs. Traditional analysis methods often rely on pre-recorded offline numerical data, hindering their application in the dynamic environment of live games. Broadcast video analysis, while seemingly ideal, faces significant challenges due to factors like motion blur and low resolution. To address these challenges, we introduce PitcherNet, an end-to-end automated system that analyzes pitcher kinematics directly from live broadcast video, thereby extracting valuable pitch statistics including velocity, release point, pitch position, and release extension. This system leverages three key components: (1) Player tracking and identification by decoupling actions from player kinematics; (2) Distribution and depth-aware 3D human modeling; and (3) Kinematic-driven pitch statistics. Experimental validation demonstrates that PitcherNet achieves robust analysis results with 96.82% accuracy in pitcher tracklet identification, reduced joint position error by 1.8mm and superior analytics compared to baseline methods. By enabling performance-critical kinematic analysis from broadcast video, PitcherNet paves the way for the future of baseball analytics by optimizing pitching strategies, preventing injuries, and unlocking a deeper understanding of pitcher mechanics, forever transforming the game.

关键词： Solid modeling Three-dimensional displays Tracking Visual analytics Kinematics Games pattern recognition

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 << < 144 145 146 147 148 149 150 151 152 153 > >>

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：