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

3,318 篇 英文
2 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2131-2140 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：

Divide and Conquer Boosting for Enhanced Traffic Safety Description and Analysis with Large vision Language Model

Divide and Conquer Boosting for Enhanced Traffic Safety Desc...

引用

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

作者： Khai Trinh Xuan Khoi Nguyen Nguyen Bach Hoang Ngo Vu Dinh Xuan Minh-Hung An Quang-Vinh Dinh Ho Chi Minh City University of Technology VNU-HCM Vietnam Vietnam National University Ho Chi Minh City University of Science VNU-HCM Vietnam University of Information Technology VNU-HCM Vietnam FPT Telecom Vietnam AI Lab - AI Vietnam

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The increasing complexity of traffic dynamics has underscored the necessity for advanced traffic safety description and analysis, challenging the efficacy of current methodologies in comprehensively understanding and predicting safety conditions from transportation videos. This paper addresses these challenges by analyzing specific segments crucial for precise traffic safety descriptions. Through this examination, we introduce an innovative preprocessing method named "segment extraction", facilitating the creation of a novel segment-based training dataset. Additionally, we present a practical two-stage training framework specifically tailored for this dataset. This framework is designed to produce accurate descriptions of traffic safety by incorporating the unique attributes of our segment-based training datasets. Leveraging these contributions, our method achieved a notable 2nd rank with a score of 32.8877 in the AI City Challenge Track2 test set: Traffic Safety Description and Analysis 2024. The source code for the proposed approaches is openly accessible at https://***/AIVIETNAMResearch/AI-CIty-2024-Track2

关键词： Training Pedestrians Source coding Urban areas Data preprocessing Transportation Data models

来源：评论

学校读者我要写书评

暂无评论

Knowledge Distillation for Efficient Instance Semantic Segmentation with Transformers

Knowledge Distillation for Efficient Instance Semantic Segme...

引用

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

作者： Maohui Li Michael Halstead Chris McCool University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Instance-based semantic segmentation provides detailed per-pixel scene understanding information crucial for both computer vision and robotics applications. However, state-of-the-art approaches such as Mask2Former are computationally expensive and reducing this computational burden while maintaining high accuracy remains challenging. Knowledge distillation has been regarded as a potential way to compress neural networks, but to date limited work has explored how to apply this to distill information from the output queries of a model such as *** this paper, we match the output queries of the student and teacher models to enable a query-based knowledge distillation scheme. We independently match the teacher and the student to the groundtruth and use this to define the teacher to student relationship for knowledge distillation. Using this approach we show that it is possible to perform knowledge distillation where the student models can have a lower number of queries and the backbone can be changed from a Transformer architecture to a convolutional neural network architecture. Experiments on two challenging agricultural datasets, sweet pepper (BUP20) and sugar beet (SB20), and Cityscapes demonstrate the efficacy of our approach. Across the three datasets the student models obtain an average absolute performance improvement in AP of 1.8 and 1.9 points for ResNet-50 and Swin-Tiny backbone respectively. To the best of our knowledge, this is the first work to propose knowledge distillation schemes for instance semantic segmentation with transformer-based models.

关键词： Knowledge engineering computer vision Semantic segmentation Computational modeling Impedance matching Neural networks computer architecture

来源：评论

学校读者我要写书评

暂无评论

3D Kinematics Estimation from Video with a Biomechanical Model and Synthetic Training Data

3D Kinematics Estimation from Video with a Biomechanical Mod...

引用

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

作者： Zhi-Yi Lin Bofan Lyu Judith Cueto Fernandez Eline van der Kruk Ajay Seth Xucong Zhang Department of Intelligent Systems Computer Vision Lab Delft University of Technology Department of BioMechanical Engineering Faculty of Mechanical Engineering Delft University of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Accurate 3D kinematics estimation of human body is crucial in various applications for human health and mobility, such as rehabilitation, injury prevention, and diagnosis, as it helps to understand the biomechanical loading experienced during movement. Conventional marker-based motion capture is expensive in terms of financial investment, time, and the expertise required. Moreover, due to the scarcity of datasets with accurate annotations, existing markerless motion capture methods suffer from challenges including unreliable 2D keypoint detection, limited anatomic accuracy, and low generalization capability. In this work, we propose a novel biomechanics-aware network that directly outputs 3D kinematics from two input views with consideration of biomechanical prior and spatio-temporal information. To train the model, we create synthetic dataset ODAH with accurate kinematics annotations generated by aligning the body mesh from the SMPL-X model and a full-body OpenSim skeletal model. Our extensive experiments demonstrate that the proposed approach, only trained on synthetic data, outperforms previous state-of-the-art methods when evaluated across multiple datasets, revealing a promising direction for enhancing video-based human motion capture.

关键词： Biomechanics Visualization computer vision Accuracy Three-dimensional displays Biological system modeling Estimation

来源：评论

学校读者我要写书评

暂无评论

Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT

Mitigating Challenges of the Space Environment for Onboard A...

引用

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

作者： Miguel Ortiz Del Castillo Jonathan Morgan Jack McRobbie Clint Therakam Zaher Joukhadar Robert Mearns Simon Barraclough Richard Sinnott Andrew Woods Chris Bayliss Kris Ehinger Ben Rubinstein James Bailey Airlie Chapman Michele Trenti The University of Melbourne

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Artificial intelligence (AI) and autonomous edge computing in space are emerging areas of interest to augment capabilities of nanosatellites, where modern sensors generate orders of magnitude more data than can typically be transmitted to mission control. Here, we present the hardware and software design of an onboard AI subsystem hosted on SpIRIT. The system is optimised for on-board computer vision experiments based on visible light and long wave infrared cameras. This paper highlights the key design choices made to maximise the robustness of the system in harsh space conditions, and their motivation relative to key mission requirements, such as limited compute resources, resilience to cosmic radiation, extreme temperature variations, distribution shifts, and very low transmission bandwidths. The payload, called Loris, consists of six visible light cameras, three infrared cameras, a camera control board and a Graphics Processing Unit (GPU) system-on-module. Loris enables the execution of AI models with on-orbit fine-tuning as well as a next-generation image compression algorithm, including progressive coding. This innovative approach not only enhances the data processing capabilities of nanosatellites but also lays the groundwork for broader applications to remote sensing from space.

关键词： computer vision Temperature distribution Image coding Space missions Small satellites Graphics processing units Aerospace electronics

来源：评论

学校读者我要写书评

暂无评论

BAA-NGP: Bundle-Adjusting Accelerated Neural Graphics Primitives

BAA-NGP: Bundle-Adjusting Accelerated Neural Graphics Primit...

引用

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

作者： Sainan Liu Shan Lin Jingpei Lu Alexey Supikov Michael Yip Intel Labs UCSD

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Implicit neural representations have become pivotal in robotic perception, enabling robots to comprehend 3D environments from 2D images. Given a set of camera poses and associated images, the models can be trained to synthesize novel, unseen views. To successfully navigate and interact in dynamic settings, robots require the understanding of their spatial surroundings driven by unassisted reconstruction of 3D scenes and camera poses from real-time video footage. Existing approaches like COLMAP and bundle-adjusting neural radiance field methods take hours to days to process due to the high computational demands of feature matching, dense point sampling, and training of a multi-layer perceptron structure with a large number of parameters. To address these challenges, we propose a framework called bundle-adjusting accelerated neural graphics primitives (BAA-NGP) which leverages accelerated sampling and hash encoding to expedite automatic pose refinement/estimation and 3D scene reconstruction. Experimental results demonstrate 10 to 20 × speed improvement compared to other bundle-adjusting neural radiance field methods without sacrificing the quality of pose estimation. The github repository can be found here https://***/IntelLabs/baa-ngp.

关键词： Training Graphics Three-dimensional displays Robot vision systems Video sequences Streaming media Cameras

来源：评论

学校读者我要写书评

暂无评论

Cache and Reuse: Rethinking the Efficiency of On-device Transfer Learning

Cache and Reuse: Rethinking the Efficiency of On-device Tran...

引用

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

作者： Yuedong Yang Hung-Yueh Chiang Guihong Li Diana Marculescu Radu Marculescu Chandra Family Department of Electrical and Computer Engineering The University of Texas at Austin

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Training only the last few layers in deep neural networks has been considered an effective strategy for enhancing the efficiency of on-device training. Prior work has adopted this approach and focused on accelerating backpropagation. However, by conducting a thorough system-wide analysis, we discover that the primary bottleneck is actually the forward propagation through the frozen layers, rather than backpropagation, if only the last few layers are trained. To address this issue, we introduce the "cache and reuse" idea for on-device transfer learning and propose a two-stage training method, which consists of a cache initialization stage, where we store the output from the frozen layers, followed by a training stage. To make our approach practical, we also propose augmented feature caching and cache compression to address the challenges of non-cacheable feature maps and cache size explosion. We carry out extensive experiments on various models (e.g., convolutional neural network and vision transformers) using real edge devices to demonstrate the effectiveness of our method. As an example, on NVIDIA Jetson Orin NX with MobileNet-V2, our approach boosts the training speed by 6.6 ×, and improves the accuracy by 2.1%. For EfficientNet-b0, our method increases the training speed by 2.2 × and improves its accuracy by 1.3%. Therefore, our approach represents a significant improvement in enabling practical on-device transfer learning for edge devices with limited resources.

关键词： Training Backpropagation computer vision Accuracy conferences Transfer learning Transformers

来源：评论

学校读者我要写书评

暂无评论

QAttn: Efficient GPU Kernels for mixed-precision vision Transformers

QAttn: Efficient GPU Kernels for mixed-precision Vision Tran...

引用

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

作者： Piotr Kluska Adrián Castelló Florian Scheidegger A. Cristiano I. Malossi Enrique S. Quintana-Ortí IBM Research Europe Universitat Politècnica de València Universitat Politècnica de València IBM Research Europe

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

vision Transformers have demonstrated outstanding performance in computer vision tasks. Nevertheless, this superior performance for large models comes at the expense of increasing memory usage for storing the parameters and intermediate activations. To accelerate model inference, in this work we develop and evaluate integer and mixed-precision kernels in Triton for the efficient execution of two fundamental building blocks of transformers –linear layer and attention– on graphics processing units (GPUs). On an NVIDIA A100 GPU, our kernel implementations of vision Transformers achieve a throughput speedup of up to 7x compared with reference kernels in PyTorch floating-point single precision (FP32). Additionally, the accuracy for the ViT Large model top-1 drops by less than one percent on the ImageNet1K classification task. We also observe up to 6x increased throughput by applying our kernels to the Segment Anything Model image encoder while keeping the mIOU close to the FP32 reference on the COCO2017 dataset for static and dynamic quantization. Furthermore, our kernels demonstrate improved speed to the TensorRT INT8 linear layer, and we improve the throughput of base FP16 (half precision) Triton attention on average by up to 19 ± 4.01%. We have open-sourced the QAtnn framework, which is tightly integrated with the PyTorch quantization workflow https://***/IBM/qattn.

关键词： computer vision Image segmentation Quantization (signal) Computational modeling Graphics processing units Prototypes Transformers

来源：评论

学校读者我要写书评

暂无评论

Recursive Joint Cross-Modal Attention for Multimodal Fusion in Dimensional Emotion recognition

Recursive Joint Cross-Modal Attention for Multimodal Fusion ...

引用

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

作者： R. Gnana Praveen Jahangir Alam Computer Research Institute of Montreal (CRIM) Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Though multimodal emotion recognition has achieved significant progress over recent years, the potential of rich synergic relationships across the modalities is not fully exploited. In this paper, we introduce Recursive Joint Cross-Modal Attention (RJCMA) to effectively capture both intra- and inter-modal relationships across audio, visual, and text modalities for dimensional emotion recognition. In particular, we compute the attention weights based on cross-correlation between the joint audio-visual-text feature representations and the feature representations of individual modalities to simultaneously capture intra- and inter-modal relationships across the modalities. The attended features of the individual modalities are again fed as input to the fusion model in a recursive mechanism to obtain more refined feature representations. We have also explored Temporal Convolutional Networks (TCNs) to improve the temporal modeling of the feature representations of individual modalities. Extensive experiments are conducted to evaluate the performance of the proposed fusion model on the challenging Affwild2 dataset. By effectively capturing the synergic intra- and inter-modal relationships across audio, visual, and text modalities, the proposed fusion model achieves a Concordance Correlation Coefficient (CCC) of 0.585 (0.542) and 0.674 (0.619) for valence and arousal, respectively, on the validation set (test set). This shows a significant improvement over the baseline of 0.240 (0.211) and 0.200 (0.191) for valence and arousal, respectively, in the validation set (test set), achieving second place in the valence-arousal challenge of the 6th Affective Behavior Analysis in-the-Wild (ABAW) competition. The code is available on GitHub: https://***/praveena2j/RJCMA.

关键词： Correlation coefficient Visualization Emotion recognition computer vision Computational modeling conferences Refining

来源：评论

学校读者我要写书评

暂无评论

Multi-Scale Feature Fusion using Channel Transformers for Guided Thermal Image Super Resolution

Multi-Scale Feature Fusion using Channel Transformers for Gu...

引用

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

作者： Raghunath Sai Puttagunta Birendra Kathariya Zhu Li George York University of Missouri-Kansas City US Air Force Academy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Thermal imaging, leveraging the infrared spectrum, offers a compelling alternative to visible spectrum (VIS) imagery in challenging environmental conditions like low- light, occlusions, and adverse weather. However, its widespread adoption in computer vision tasks is hampered by lower spatial resolution. We address this challenge by proposing a novel framework titled Multi-Scale Feature Fusion using Channel Transformers (MSFFCT) for Guided Thermal Image Super-Resolution (GTISR).GTISR tackles the resolution limitations of thermal imagery. It leverages high-resolution RGB information as a guide to reconstruct high-resolution thermal imagery from low-resolution thermal inputs. At the core of MSFFCT lies a novel deep learning architecture that combines the strengths of two powerful approaches: channel-based transformers and multi-scale *** overcomes inherent limitations of Convolutional Neural Networks (CNNs) typically used in super-resolution tasks. CNNs often suffer from restricted receptive fields, limiting their ability to capture long-range dependencies within the image. Additionally, computational cost grows significantly with larger inputs. MSFFCT addresses these shortcomings by enabling efficient processing of global information and offering superior scalability. MSFFCT achieved state-of-the-art results on the ×8 and × 16 GTISR tasks of the 2024 Perception Beyond Visual Spectrum (PBVS) challenge, winning 2 nd place in both tasks and demonstrating its effectiveness in real-world scenarios.

关键词： computer vision Visualization Limiting Superresolution Imaging Transformer cores Transformers

来源：评论

学校读者我要写书评

暂无评论

Beyond Appearances: Material Segmentation with Embedded Spectral Information from RGB-D imagery

Beyond Appearances: Material Segmentation with Embedded Spec...

引用

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

作者： Fabian Perez Hoover Rueda-Chacón Universidad Industrial de Santander

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the realm of computer vision, material segmentation of natural scenes represents a challenge, driven by the complex and diverse appearances of materials. Traditional approaches often rely on RGB images, which can be deceptive given the variability in appearances due to different lighting conditions. Other methods, that employ polarization or spectral imagery, offer a more reliable material differentiation but their cost and accessibility restrict their everyday usage. In this work, we propose a deep learning framework that bridges the gap between high-fidelity material segmentation and the practical constraints of data acquisition. Our approach leverages a training strategy that employs a paired RGBD-spectral data to incorporate spectral information directly within the neural network. This encoding process is facilitated by a Spectral Feature Mapper (SFM) layer, a novel module that embeds unique spectral characteristics into the network, thus enabling the network to infer materials from standard RGB-D images. Once trained, the model allows to conduct material segmentation on widely available devices without the need for direct spectral data input. In addition, we generate the 3D point cloud from the RGB-D image pair, to provide a richer spatial context for scene understanding. Through simulations using available datasets, and real experiments conducted with an iPad Pro, our method demonstrates superior performance in material segmentation compared to other methods. Code is available at: https://***/Factral/Spectral-material-segmentation

关键词： Deep learning Training Image segmentation computer vision Visualization Three-dimensional displays Lighting

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 << < 210 211 212 213 214 215 216 217 218 219 > >>

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：