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

11,885 篇 会议
5 篇 期刊文献

馆藏范围

11,890 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

8,059 篇 工学
- 7,617 篇 计算机科学与技术...
- 796 篇 机械工程
- 688 篇 电气工程
- 360 篇 软件工程
- 228 篇 控制科学与工程
- 40 篇 光学工程
- 19 篇 生物工程
- 17 篇 信息与通信工程
- 12 篇 生物医学工程（可授...
- 6 篇 电子科学与技术（可...
- 6 篇 建筑学
- 6 篇 交通运输工程
- 5 篇 仪器科学与技术
- 5 篇 化学工程与技术
- 5 篇 安全科学与工程
- 4 篇 土木工程
3,347 篇 医学
- 3,346 篇 临床医学
- 4 篇 基础医学(可授医学...
- 4 篇 公共卫生与预防医...
253 篇 理学
- 198 篇 系统科学
- 32 篇 物理学
- 21 篇 生物学
- 18 篇 数学
- 9 篇 统计学（可授理学、...
- 7 篇 化学
17 篇 管理学
- 12 篇 管理科学与工程(可...
- 7 篇 图书情报与档案管...
- 5 篇 工商管理
3 篇 法学
- 3 篇 社会学
3 篇 教育学
- 3 篇 教育学
2 篇 农学
1 篇 经济学
1 篇 军事学

主题

5,633 篇 computer vision
2,668 篇 training
2,203 篇 pattern recognit...
1,747 篇 computational mo...
1,502 篇 visualization
1,360 篇 three-dimensiona...
1,074 篇 semantics
999 篇 benchmark testin...
986 篇 codes
959 篇 computer archite...
891 篇 deep learning
777 篇 conferences
754 篇 task analysis
700 篇 feature extracti...
561 篇 transformers
533 篇 face recognition
527 篇 neural networks
495 篇 object detection
490 篇 image segmentati...
468 篇 cameras

机构

174 篇 univ sci & techn...
145 篇 carnegie mellon ...
144 篇 univ chinese aca...
144 篇 tsinghua univ pe...
134 篇 chinese univ hon...
110 篇 zhejiang univ pe...
109 篇 peng cheng lab p...
99 篇 swiss fed inst t...
91 篇 tsinghua univers...
90 篇 shanghai ai lab ...
87 篇 sensetime res pe...
86 篇 shanghai jiao to...
83 篇 zhejiang univers...
82 篇 tech univ munich...
79 篇 university of sc...
79 篇 stanford univ st...
78 篇 univ hong kong p...
77 篇 australian natl ...
76 篇 alibaba grp peop...
75 篇 peng cheng labor...

作者

75 篇 timofte radu
64 篇 van gool luc
50 篇 zhang lei
43 篇 yang yi
37 篇 loy chen change
36 篇 tao dacheng
32 篇 zhou jie
31 篇 chen chen
30 篇 liu yang
30 篇 tian qi
29 篇 sun jian
29 篇 zha zheng-jun
28 篇 li xin
27 篇 qi tian
26 篇 vasconcelos nuno
25 篇 liu xiaoming
25 篇 darrell trevor
24 篇 zheng wei-shi
24 篇 luo ping
24 篇 ying shan

语言

11,863 篇 英文
26 篇 其他
1 篇 中文

检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11890 条记录，以下是521-530 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：

Video Interaction recognition using an Attention Augmented Relational Network and Skeleton Data

Video Interaction Recognition using an Attention Augmented R...

引用

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

作者： Askari, Farzaneh Yared, Cyril Ramaprasad, Rohit Garg, Devin Hu, Anjun Clark, James J. McGill Univ Montreal PQ Canada Univ Calif San Diego San Diego CA USA Univ Oxford Oxford England

ISBN: (纸本)9798350365474

Recognizing interactions in multi-person videos, known as Video Interaction recognition (VIR), is crucial for understanding video content. Often the human skeleton pose (skeleton, for short) is a popular feature for VIR as the main feature, given its success for the task in hand. While many studies have made progress using complex architectures like Graph Neural Networks (GNN) and Transformers to capture interactions in videos, studies such as [33] that apply simple, easy to train, and adaptive architectures such as Relation reasoning Network (RN) [37], yield competitive results. Inspired by this trend, we propose the Attention Augmented Relational Network (AARN), a straightforward yet effective model that uses skeleton data to recognize interactions in videos. AARN outperforms other RN-based models and remains competitive against larger, more intricate models. We evaluate our approach on a challenging real-world Hockey Penalty Dataset (HPD), where the videos depict complex interactions between players in a non-laboratory recording setup, in addition to popular benchmark datasets demonstrating strong performance. Lastly, we show the impact of skeleton quality on the classification accuracy and the struggle of off-the-shelf pose estimators to extract precise skeleton from the challenging HPD dataset.

关键词： Musculoskeletal system

来源：评论

学校读者我要写书评

暂无评论

CUE-Net: Violence Detection Video Analytics with Spatial Cropping, Enhanced UniformerV2 and Modified Efficient Additive Attention

CUE-Net: Violence Detection Video Analytics with Spatial Cro...

引用

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

作者： Senadeera, Damith Chamalke Yang, Xiaoyun Kollias, Dimitrios Slabaugh, Gregory Queen Mary Univ London Sch Elect Engn & Comp Sci London England Queen Marys Digital Environm Res Inst DERI London England Remark AI UK Ltd London England

ISBN: (纸本)9798350365474

In this paper we introduce CUE-Net, a novel architecture designed for automated violence detection in video surveillance. As surveillance systems become more prevalent due to technological advances and decreasing costs, the challenge of efficiently monitoring vast amounts of video data has intensified. CUE-Net addresses this challenge by combining spatial Cropping with an enhanced version of the UniformerV2 architecture, integrating convolutional and self-attention mechanisms alongside a novel Modified Efficient Additive Attention mechanism (which reduces the quadratic time complexity of self-attention) to effectively and efficiently identify violent activities. This approach aims to overcome traditional challenges such as capturing distant or partially obscured subjects within video frames. By focusing on both local and global spatio-temporal features, CUE-Net achieves state-of-the-art performance on the RWF-2000 and RLVS datasets, surpassing existing methods. The source code is available at (1).

关键词： computer vision Cropping Deep Learning Efficient Additive Attention UniFormerV2 Video Analytics Violence Detection

来源：评论

学校读者我要写书评

暂无评论

Seeing the Unseen: Visual Common Sense for Semantic Placement

Seeing the Unseen: Visual Common Sense for Semantic Placemen...

引用

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

作者： Ramrakhya, Ram Kembhavi, Aniruddha Batra, Dhruv Kira, Zsolt Zeng, Kuo-Hao Weihs, Luca Georgia Inst Technol Atlanta GA 30332 USA PRIOR Allen Inst AI Seattle WA USA PRIOR AI2 Seattle WA USA

ISBN: (纸本)9798350353006

computer vision tasks typically involve describing what is present in an image (e.g. classification, detection, segmentation, and captioning). We study a visual common sense task that requires understanding 'what is not present'. Specifically, given an image (e.g. of a living room) and a name of an object ("cushion"), a vision system is asked to predict semantically-meaningful regions (masks or bounding boxes) in the image where that object could be placed or is likely be placed by humans (e.g. on the sofa). We call this task: Semantic Placement (SP) and believe that such common-sense visual understanding is critical for assitive robots (tidying a house), AR devices (automatically rendering an object in the user's space), and visually-grounded chatbots with common sense. Studying the invisible is hard. Datasets for image description are typically constructed by curating relevant images (e.g. via image search with object names) and asking humans to annotate the contents of the image;neither of those two steps are straightforward for objects not present in the image. We overcome this challenge by operating in the opposite direction: we start with an image of an object in context, which is easy to find online, and then remove that object from the image via inpainting. This automated pipeline converts unstructured web data into a dataset comprising pairs of images with/without the object. With this proposed data generation pipeline, we collect a novel dataset, containing similar to 1.3M images across 9 object categories. We then train a SP prediction model, called CLIP-UNet, on our dataset. The CLIP-UNet outperforms existing VLMs and baselines that combine semantic priors with object detectors, generalizes well to real-world and simulated images and exhibits semantics-aware reasoning for object placement. In our user studies, we find that the SP masks predicted by CLIP-UNet are favored 43.7% and 31.3% times when comparing against the 4 SP baselines on real and simulated

关键词： Common Sense Reasoning computer vision Embodied AI

来源：评论

学校读者我要写书评

暂无评论

Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Models

Strategies to Improve Real-World Applicability of Laparoscop...

引用

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

作者： Kolbinger, Fiona R. He, Jiangpeng Ma, Jinge Zhu, Fengqing Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9798350365474

Accurate identification and localization of anatomical structures of varying size and appearance in laparoscopic imaging are necessary to leverage the potential of computer vision techniques for surgical decision support. Segmentation performance of such models is traditionally reported using metrics of overlap such as IoU. However, imbalanced and unrealistic representation of classes in the training data and suboptimal selection of reported metrics have the potential to skew nominal segmentation performance and thereby ultimately limit clinical translation. In this work, we systematically analyze the impact of class characteristics (i.e., organ size differences), training and test data composition (i.e., representation of positive and negative examples), and modeling parameters (i.e., foreground-to-background class weight) on eight segmentation metrics: accuracy, precision, recall, IoU, F1 score (Dice Similarity Coefficient), specificity, Hausdorff Distance, and Average Symmetric Surface Distance. Our findings support two adjustments to account for data biases in surgical data science: First, training on datasets that are similar to the clinical real-world scenarios in terms of class distribution, and second, class weight adjustments to optimize segmentation model performance with regard to metrics of particular relevance in the respective clinical setting.

关键词： Class Imbalance computer-Assisted Surgery Laparoscopic Surgery Semantic Segmentation Surgical Data Science

来源：评论

学校读者我要写书评

暂无评论

Video Frame Interpolation via Direct Synthesis with the Event-based Reference

Video Frame Interpolation via Direct Synthesis with the Even...

引用

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

作者： Liu, Yuhan Deng, Yongjian Chen, Hao Yang, Hen Beijing Univ Technol Coll Comp Sci Beijing Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Dhaka Bangladesh

ISBN: (纸本)9798350353013;9798350353006

Video Frame Interpolation (VFI) has witnessed a surge in popularity due to its abundant downstream applications. Event-based VFI (E-VFI) has recently propelled the advancement of VFI. Thanks to the high temporal resolution benefits, event cameras can bridge the informational void present between successive video frames. Most state-of-the-art E-VFI methodologies follow the conventional VFI paradigm, which pivots on motion estimation between consecutive frames to generate intermediate frames through a process of warping and refinement. However, this reliance engenders a heavy dependency on the quality and consistency of keyframes, rendering these methods susceptible to challenges in extreme real-world scenarios, such as missing moving objects and severe occlusion dilemmas. This study proposes a novel E-VFI framework that directly synthesize intermediate frames leveraging event-based reference, obviating the necessity for explicit motion estimation and substantially enhancing the capacity to handle motion occlusion. Given the sparse and inherently noisy nature of event data, we prioritize the reliability of the event-based reference, leading to the development of an innovative event-aware reconstruction strategy for accurate reference generation. Besides, we implement a bi-directional event- guided alignment from keyframes to the reference using the introduced E-PCD module. Finally, a transformer-based decoder is adopted for prediction refinement. Comprehensive experimental evaluations on both synthetic and real- world datasets underscore the superiority of our approach and its potential to execute high-quality VFI tasks.

关键词： Event-Based vision Video Frame Interpolation Video Synthesis

来源：评论

学校读者我要写书评

暂无评论

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/cvf conference on computer vision and pattern recognition (cvpr)

作者： Khai Trinh Xuan Khoi Nguyen Nguyen Bach Hoang Ngo Vu Dinh Xuan Minh-Hung An Quang-Vinh Dinh Ho Chi Minh City Univ Technol VNU HCM Ho Chi Minh City Vietnam Univ Sci VNU HCM Ho Chi Minh City Vietnam Univ Informat Technol VNU HCM Ho Chi Minh City Vietnam FPT Telecom Hanoi Vietnam AI Lab AI VIETNAM Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Ho Chi Minh City Vietnam

ISBN: (纸本)9798350365474

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-CIty2024-Track2

关键词： large language model large vision language model video captioning video instance captioning video-text model

来源：评论

学校读者我要写书评

暂无评论

Can the accuracy bias by facial hairstyle be reduced through balancing the training data?

Can the accuracy bias by facial hairstyle be reduced through...

引用

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

作者： Ozturk, Kagan Wu, Haiyu Bowyer, Kevin W. Univ Notre Dame Notre Dame IN 46556 USA

ISBN: (纸本)9798350365474

Appearance of a face can be greatly altered by growing a beard and mustache. The facial hairstyles in a pair of images can cause marked changes to the impostor distribution and the genuine distribution. Also, different distributions of facial hairstyle across demographics could cause a false impression of relative accuracy across demographics. We first show that, even though larger training sets boost the recognition accuracy on all facial hairstyles, accuracy variations caused by facial hairstyles persist regardless of the size of the training set. Then, we analyze the impact of having different fractions of the training data represent facial hairstyles. We created balanced training sets using a set of identities available in Webface42M that both have clean-shaven and facial hair images. We find that, even when a face recognition model is trained with a balanced clean-shaven / facial hair training set, accuracy variation on the test data does not diminish. Next, data augmentation is employed to further investigate the effect of facial hair distribution in training data by manipulating facial hair pixels with the help of facial landmark points and a facial hair segmentation model. Our results show facial hair causes an accuracy gap between clean-shaven and facial hair images, and this impact can be significantly different between African-Americans and Caucasians.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation

APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semanti...

引用

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

作者： He, Weizhao Zhang, Yang Zhuo, Wei Shen, Linlin Yang, Jiaqi Deng, Songhe Sun, Liang Shenzhen Univ Comp Vision Inst Sch Comp Sci Software Engn Shenzhen Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen Peoples R China Univ Nottingham Sch Comp Sci Nottingham England

ISBN: (纸本)9798350353006

Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their training and application scenarios share similar domains, and their performances degrade significantly while applied to a distinct domain. To this end, we propose to leverage the cutting-edge foundation model, the Segment Anything Model (SAM), for generalization enhancement. The SAM however performs unsatisfactorily on domains that are distinct from its training data, which primarily comprise natural scene images, and it does not support automatic segmentation of specific semantics due to its interactive prompting mechanism. In our work, we introduce APSeg, a novel auto-prompt network for cross-domain few-shot semantic segmentation (CD-FSS), which is designed to be auto-prompted for guiding cross-domain segmentation. Specifically, we propose a Dual Prototype Anchor Transformation (DPAT) module that fuses pseudo query prototypes extracted based on cycle-consistency with support port prototypes, allowing features to be transformed into a more stable domain-agnostic space. Additionally, a Meta Prompt (MPG) module is introduced to automatically generate prompt embeddings, eliminating the need for manual visual prompts. We build an efficient model which can be applied directly to target domains without fine-tuning. Extensive experiments on four cross-domain datasets show that our model outperforms the state-of-the-art CD-FSS method by 5.24% and 3.10% in average accuracy on 1-shot and 5-shot settings, respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Hairy Ground Truth Enhancement for Semantic Segmentation

Hairy Ground Truth Enhancement for Semantic Segmentation

引用

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

作者： Fischer, Sophie Voiculescu, Irina Univ Oxford Dept Comp Sci Oxford England

ISBN: (纸本)9798350365474

Semantic segmentation is a key task within applications of machine learning for medical imaging, requiring large amounts of medical scans annotated by clinicians. The high cost of data annotation means that models need to make the most of all available ground truth masks;yet many models consider two false positive (or false negative) pixel predictions as 'equally wrong' regardless of the individual pixels' relative position to the ground truth mask. These methods also have no sense of whether a pixel is solitary or belongs to a contiguous group. We propose the Hairy transform, a novel method to enhance ground truths using 3D 'hairs' to represent each pixel's position relative to objects in the ground truth. We illustrate its effectiveness using a mainstream model and loss function on a commonly used cardiac MRI dataset, as well as a set of synthetic data constructed to highlight the effect of the method during training. The overall improvement in segmentation results comes at the small cost of a one-off pre-processing step, and can easily be integrated into any standard machine learning model. Rather than looking to make minute improvements for mostly correct 'standard' masks we instead show how this method helps improve robustness against catastrophic failures for edge cases.

关键词： computer vision Ground Truth Enhancement Machine Learning Medical Imaging

来源：评论

学校读者我要写书评

暂无评论

Multi Model Ensemble for Compound Expression recognition

Multi Model Ensemble for Compound Expression Recognition

引用

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

作者： Yu, Jun Zhu, Jichao Zhu, Wangyuan Cai, Zhongpeng Zhao, Gongpeng Wei, Zhihong Xie, Guochen Zhang, Zerui Liu, Qingsong Liang, Jiaen Univ Sci & Technol China Hefei Anhui Peoples R China Unisound AI Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350365474

Compound Expression recognition (CER) plays a crucial role in interpersonal interactions. Due to the complexity of human emotional expressions, which leads to the existence of compound expressions, it is necessary to consider both local and global facial expressions comprehensively for recognition. In this paper, to address this issue, we propose a solution for compound expression recognition based on ensemble learning methods. Specifically, our task is classification. We trained three expression classification models based on convolutional networks (ResNet50), vision Transformers, and multi-scale local attention networks, respectively. Then, by using late fusion, integrated the outputs of three models to predict the final result, leveraging the strengths of different models. Our method achieves high accuracy on RAF-DB and in sixth Affective Behavior Analysis in-the-wild (ABAW) Challenge, achieves an F1 score of 0.224 on the test set of C-EXPR-DB.

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 << < 49 50 51 52 53 54 55 56 57 58 > >>

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：