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

20,860 篇 会议
105 篇 期刊文献
43 册 图书

馆藏范围

21,007 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,620 篇 工学
- 11,056 篇 计算机科学与技术...
- 2,652 篇 机械工程
- 2,252 篇 软件工程
- 914 篇 光学工程
- 885 篇 电气工程
- 529 篇 控制科学与工程
- 477 篇 信息与通信工程
- 216 篇 测绘科学与技术
- 135 篇 生物工程
- 127 篇 生物医学工程（可授...
- 98 篇 电子科学与技术（可...
- 92 篇 仪器科学与技术
- 46 篇 安全科学与工程
- 40 篇 建筑学
- 40 篇 化学工程与技术
- 39 篇 土木工程
- 37 篇 交通运输工程
- 35 篇 力学（可授工学、理...
- 33 篇 航空宇航科学与技...
3,494 篇 医学
- 3,489 篇 临床医学
- 32 篇 基础医学(可授医学...
2,247 篇 理学
- 1,145 篇 物理学
- 1,081 篇 数学
- 401 篇 生物学
- 384 篇 统计学（可授理学、...
- 245 篇 系统科学
- 46 篇 化学
343 篇 管理学
- 176 篇 管理科学与工程(可...
- 168 篇 图书情报与档案管...
- 34 篇 工商管理
31 篇 法学
19 篇 农学
15 篇 教育学
8 篇 经济学
5 篇 艺术学
2 篇 军事学
1 篇 文学

主题

8,140 篇 computer vision
2,886 篇 training
2,838 篇 pattern recognit...
1,812 篇 computational mo...
1,707 篇 visualization
1,493 篇 cameras
1,431 篇 three-dimensiona...
1,428 篇 feature extracti...
1,360 篇 shape
1,352 篇 face recognition
1,255 篇 image segmentati...
1,135 篇 robustness
1,134 篇 semantics
988 篇 computer archite...
983 篇 object detection
974 篇 layout
966 篇 benchmark testin...
930 篇 codes
903 篇 object recogniti...
900 篇 computer science

机构

174 篇 univ sci & techn...
158 篇 univ chinese aca...
146 篇 carnegie mellon ...
145 篇 chinese univ hon...
109 篇 microsoft resear...
103 篇 zhejiang univ pe...
100 篇 swiss fed inst t...
95 篇 tsinghua univers...
90 篇 microsoft res as...
88 篇 shanghai ai lab ...
85 篇 tsinghua univ pe...
81 篇 zhejiang univers...
77 篇 alibaba grp peop...
74 篇 hong kong univ s...
73 篇 university of sc...
72 篇 peking univ peop...
70 篇 university of ch...
68 篇 shanghai jiao to...
66 篇 univ oxford oxfo...
65 篇 google res mount...

作者

80 篇 van gool luc
70 篇 zhang lei
57 篇 timofte radu
51 篇 luc van gool
48 篇 yang yi
46 篇 xiaoou tang
44 篇 tian qi
43 篇 darrell trevor
43 篇 li stan z.
42 篇 loy chen change
42 篇 qi tian
41 篇 sun jian
39 篇 li fei-fei
38 篇 tao dacheng
37 篇 chen xilin
36 篇 shan shiguang
36 篇 torralba antonio
35 篇 zhou jie
35 篇 vasconcelos nuno
35 篇 liu yang

语言

20,977 篇 英文
12 篇 其他
10 篇 中文
5 篇 土耳其文
2 篇 日文
2 篇 葡萄牙文

检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 21008 条记录，以下是981-990 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：

Progressive Transformation Learning for Leveraging Virtual Images in Training

Progressive Transformation Learning for Leveraging Virtual I...

引用

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

作者： Shen, Yi-Ting Lee, Hyungtae Kwon, Heesung Bhattacharyya, Shuvra S. Univ Maryland College Pk MD USA DEVCOM Army Res Lab Adelphi MD USA

ISBN: (纸本)9798350301298

To effectively interrogate UAV-based images for detecting objects of interest, such as humans, it is essential to acquire large-scale UAV-based datasets that include human instances with various poses captured from widely varying viewing angles. As a viable alternative to laborious and costly data curation, we introduce Progressive Transformation Learning (PTL), which gradually augments a training dataset by adding transformed virtual images with enhanced realism. Generally, a virtual2real transformation generator in the conditional GAN framework suffers from quality degradation when a large domain gap exists between real and virtual images. To deal with the domain gap, PTL takes a novel approach that progressively iterates the following three steps: 1) select a subset from a pool of virtual images according to the domain gap, 2) transform the selected virtual images to enhance realism, and 3) add the transformed virtual images to the training set while removing them from the pool. In PTL, accurately quantifying the domain gap is critical. To do that, we theoretically demonstrate that the feature representation space of a given object detector can be modeled as a multivariate Gaussian distribution from which the Mahalanobis distance between a virtual object and the Gaussian distribution of each object category in the representation space can be readily computed. Experiments show that PTL results in a substantial performance increase over the baseline, especially in the small data and the cross-domain regime.

关键词： detection recognition: Categorization retrieval

来源：评论

学校读者我要写书评

暂无评论

Fake it till you make it: Learning transferable representations from synthetic ImageNet clones

Fake it till you make it: Learning transferable representati...

引用

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

作者： Sariyildiz, Mert Bulent Alahari, Karteek Larlus, Diane Kalantidis, Yannis NAVER LABS Europe Grenoble France Uni Grenoble Alpes CNRS Inria Grenoble INPLJK Grenoble France

ISBN: (纸本)9798350301298

Recent image generation models such as Stable Diffusion have exhibited an impressive ability to generate fairly realistic images starting from a simple text prompt. Could such models render real images obsolete for training image prediction models? In this paper, we answer part of this provocative question by investigating the need for real images when training models for ImageNet classification. Provided only with the class names that have been used to build the dataset, we explore the ability of Stable Diffusion to generate synthetic clones of ImageNet and measure how useful these are for training classification models from scratch. We show that with minimal and class-agnostic prompt engineering, ImageNet clones are able to close a large part of the gap between models produced by synthetic images and models trained with real images, for the several standard classification benchmarks that we consider in this study. More importantly, we show that models trained on synthetic images exhibit strong generalization properties and perform on par with models trained on real data for transfer. Project page: https://***/imagenet-sd

关键词： detection recognition: Categorization retrieval

来源：评论

学校读者我要写书评

暂无评论

Recurrent Homography Estimation Using Homography-Guided Image Warping and Focus Transformer

Recurrent Homography Estimation Using Homography-Guided Imag...

引用

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

作者： Cao, Si-Yuan Zhang, Runmin Luo, Lun Yu, Beinan Sheng, Zehua Li, Junwei Shen, Hui-Liang Zhejiang Univ Ningbo Innovat Ctr Hangzhou Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China

ISBN: (纸本)9798350301298

We propose the Recurrent homography estimation framework using Homography-guided image Warping and Focus transformer (FocusFormer), named RHWF. Both being appropriately absorbed into the recurrent framework, the homography-guided image warping progressively enhances the feature consistency and the attention-focusing mechanism in FocusFormer aggregates the intra-inter correspondence in a global -> nonlocal -> local manner. Thanks to the above strategies, RHWF ranks top in accuracy on a variety of datasets, including the challenging cross-resolution and cross-modal ones. Meanwhile, benefiting from the recurrent framework, RHWF achieves parameter efficiency despite the transformer architecture. Compared to previous state-of-the-art approaches LocalTrans and IHN, RHWF reduces the mean average corner error (MACE) by about 70% and 38.1% on the MSCOCO dataset, while saving the parameter costs by 86.5% and 24.6%. Similar to the previous works, RHWF can also be arranged in 1-scale for efficiency and 2-scale for accuracy, with the 1-scale RHWF already outperforming most of the previous methods. Source code is available at https://***/imdumpl78/RHWF.

关键词： Low-level vision

来源：评论

学校读者我要写书评

暂无评论

Layout-based Causal Inference for Object Navigation

Layout-based Causal Inference for Object Navigation

引用

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

作者： Zhang, Sixian Song, Xinhang Li, Weijie Bai, Yubing Yu, Xinyao Jiang, Shuqiang Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Lab Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350301298

Previous works for ObjectNav task attempt to learn the association (e.g. relation graph) between the visual inputs and the goal during training. Such association contains the prior knowledge of navigating in training environments, which is denoted as the experience. The experience performs a positive effect on helping the agent infer the likely location of the goal when the layout gap between the unseen environments of the test and the prior knowledge obtained in training is minor. However, when the layout gap is significant, the experience exerts a negative effect on navigation. Motivated by keeping the positive effect and removing the negative effect of the experience, we propose the layout-based soft Total Direct Effect (L-sTDE) framework based on the causal inference to adjust the prediction of the navigation policy. In particular, we propose to calculate the layout gap which is defined as the KL divergence between the posterior and the prior distribution of the object layout. Then the sTDE is proposed to appropriately control the effect of the experience based on the layout gap. Experimental results on AI2THOR, RoboTHOR, and Habitat demonstrate the effectiveness of our method. The code is available at https://***/sxzhang/***.

关键词： Embodied vision: Active agents simulation

来源：评论

学校读者我要写书评

暂无评论

Co-designing a Sub-millisecond Latency Event-based Eye Tracking System with Submanifold Sparse CNN

Co-designing a Sub-millisecond Latency Event-based Eye Track...

引用

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

作者： Zhang, Baoheng Gao, Yizhao Li, Jingyuan So, Hayden Kwok-Hay Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350365474

Eye-tracking technology is integral to numerous consumer electronics applications, particularly in the realm of virtual and augmented reality (VR/AR). These applications demand solutions that excel in three crucial aspects: low-latency, low-power consumption, and precision. Yet, achieving optimal performance across all these fronts presents a formidable challenge, necessitating a balance between sophisticated algorithms and efficient backend hardware implementations. In this study, we tackle this challenge through a synergistic software/hardware co-design of the system with an event camera. Leveraging the inherent sparsity of event-based input data, we integrate a novel sparse FPGA dataflow accelerator customized for submanifold sparse convolution neural networks (SCNN). The SCNN implemented on the accelerator can efficiently extract the embedding feature vector from each representation of event slices by only processing the non-zero activations. Subsequently, these vectors undergo further processing by a gated recurrent unit (GRU) and a fully connected layer on the host CPU to generate the eye centers. Deployment and evaluation of our system reveal outstanding performance metrics. On the Event-based Eye-Tracking-AIS2024 dataset, our system achieves 81% p5 accuracy, 99.5% p10 accuracy, and 3.71 Mean Euclidean Distance with 0.7 ms latency while only consuming 2.29 mJ per inference. Notably, our solution opens up opportunities for future eye-tracking systems. Code is available at https://***/CASRHKU/ESDA/tree/eye_tracking.

关键词： dynamic vision sensor event camera event-based vision eye tracking FPGA hardware-software codesign sparse processing

来源：评论

学校读者我要写书评

暂无评论

The Treasure Beneath Multiple Annotations: An Uncertainty-aware Edge Detector

The Treasure Beneath Multiple Annotations: An Uncertainty-aw...

引用

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

作者： Zhou, Caixia Huang, Yaping Pu, Mengyang Guan, Qingji Huang, Li Ling, Haibin Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China SUNY Stony Brook Dept Comp Sci Stony Brook NY USA

ISBN: (纸本)9798350301298

Deep learning-based edge detectors heavily rely on pixel-wise labels which are often provided by multiple annotators. Existing methods fuse multiple annotations using a simple voting process, ignoring the inherent ambiguity of edges and labeling bias of annotators. In this paper, we propose a novel uncertainty-aware edge detector (UAED), which employs uncertainty to investigate the subjectivity and ambiguity of diverse annotations. Specifically, we first convert the deterministic label space into a learnable Gaussian distribution, whose variance measures the degree of ambiguity among different annotations. Then we regard the learned variance as the estimated uncertainty of the predicted edge maps, and pixels with higher uncertainty are likely to be hard samples for edge detection. Therefore we design an adaptive weighting loss to emphasize the learning from those pixels with high uncertainty, which helps the network to gradually concentrate on the important pixels. UAED can be combined with various encoder-decoder backbones, and the extensive experiments demonstrate that UAED achieves superior performance consistently across multiple edge detection benchmarks. The source code is available at https://***/ZhouCX117/UAED.

关键词： Low-level vision

来源：评论

学校读者我要写书评

暂无评论

Label-Free Liver Tumor Segmentation

Label-Free Liver Tumor Segmentation

引用

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

作者： Hu, Qixin Chen, Yixiong Xiao, Junfei Sun, Shuwen Chen, Jieneng Yuille, Alan Zhou, Zongwei Huazhong Univ Sci & Technol Wuhan Peoples R China Chinese Univ Hong Kong Shenzhen Shenzhen Peoples R China Johns Hopkins Univ Baltimore MD USA Nanjing Med Univ Affiliated Hosp 1 Nanjing Peoples R China

ISBN: (纸本)9798350301298

We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intriguing advantages: (I) realistic in shape and texture, which even medical professionals can confuse with real tumors;(II) effective for training AI models, which can perform liver tumor segmentation similarly to the model trained on real tumors-this result is exciting because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to real tumors. This result also implies that manual efforts for annotating tumors voxel by voxel (which took years to create) can be significantly reduced in the future. Moreover, our synthetic tumors can automatically generate many examples of small (or even tiny) synthetic tumors and have the potential to improve the success rate of detecting small liver tumors, which is critical for detecting the early stages of cancer. In addition to enriching the training data, our synthesizing strategy also enables us to rigorously assess the AI robustness.

关键词： cell microscopy Medical and biological vision

来源：评论

学校读者我要写书评

暂无评论

Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient vision Transformers

Beyond Attentive Tokens: Incorporating Token Importance and ...

引用

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

作者： Long, Sifan Zhao, Zhen Pi, Jimin Wang, Shengsheng Wang, Jingdong Jilin Univ Coll Comp Sci & Technol Jilin Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Jilin Jilin Peoples R China Baidu VIS Beijing Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9798350301298

vision transformers have achieved significant improvements on various vision tasks but their quadratic interactions between tokens significantly reduce computational efficiency. Many pruning methods have been proposed to remove redundant tokens for efficient vision transformers recently. However, existing studies mainly focus on the token importance to preserve local attentive tokens but completely ignore the global token diversity. In this paper, we emphasize the cruciality of diverse global semantics and propose an efficient token decoupling and merging method that can jointly consider the token importance and diversity for token pruning. According to the class token attention, we de-couple the attentive and inattentive tokens. In addition to preserving the most discriminative local tokens, we merge similar inattentive tokens and match homogeneous attentive tokens to maximize the token diversity. Despite its simplicity, our method obtains a promising trade-off between model complexity and classification accuracy. On DeiT-S, our method reduces the FLOPs by 35% with only a 0.2% accuracy drop. Notably, benefiting from maintaining the token diversity, our method can even improve the accuracy of DeiT-T by 0.1% after reducing its FLOPs by 40%.

关键词： Deep learning architectures and techniques

来源：评论

学校读者我要写书评

暂无评论

itKD: Interchange Transfer-based Knowledge Distillation for 3D Object Detection

itKD: Interchange Transfer-based Knowledge Distillation for ...

引用

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

作者： Cho, Hyeon Choi, Junyong Baek, Geonwoo Hwang, Wonjun Ajou Univ Suwon South Korea Hyundai Motor Co Seoul South Korea Naver AI Lab Seongnam South Korea

ISBN: (纸本)9798350301298

Point-cloud based 3D object detectors recently have achieved remarkable progress. However, most studies are limited to the development of network architectures for improving only their accuracy without consideration of the computational efficiency. In this paper, we first propose an autoencoder-style framework comprising channel-wise compression and decompression via interchange transfer-based knowledge distillation. To learn the map-view feature of a teacher network, the features from teacher and student networks are independently passed through the shared autoencoder;here, we use a compressed representation loss that binds the channel-wised compression knowledge from both student and teacher networks as a kind of regularization. The decompressed features are transferred in opposite directions to reduce the gap in the interchange reconstructions. Lastly, we present an head attention loss to match the 3D object detection information drawn by the multi-head self-attention mechanism. Through extensive experiments, we verify that our method can train the lightweight model that is well-aligned with the 3D point cloud detection task and we demonstrate its superiority using the well-known public datasets;e.g., Waymo and nuScenes.(1)

关键词： Efficient and scalable vision

来源：评论

学校读者我要写书评

暂无评论

Spatial-Frequency Mutual Learning for Face Super-Resolution

Spatial-Frequency Mutual Learning for Face Super-Resolution

引用

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

作者： Wang, Chenyang Jiang, Junjun Zhong, Zhiwei Liu, Xianming Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China

ISBN: (纸本)9798350301298

Face super-resolution (FSR) aims to reconstruct high-resolution (HR) face images from the low-resolution (LR) ones. With the advent of deep learning, the FSR technique has achieved significant breakthroughs. However, existing FSR methods either have a fixed receptive field or fail to maintain facial structure, limiting the FSR performance. To circumvent this problem, Fourier transform is introduced, which can capture global facial structure information and achieve image-size receptive field. Relying on the Fourier transform, we devise a spatial-frequency mutual network (SFMNet) for FSR, which is the first FSR method to explore the correlations between spatial and frequency domains as far as we know. To be specific, our SFMNet is a two-branch network equipped with a spatial branch and a frequency branch. Benefiting from the property of Fourier transform, the frequency branch can achieve image-size receptive field and capture global dependency while the spatial branch can extract local dependency. Considering that these dependencies are complementary and both favorable for FSR, we further develop a frequency-spatial interaction block (FSIB) which mutually amalgamates the complementary spatial and frequency information to enhance the capability of the model. Quantitative and qualitative experimental results show that the proposed method outperforms state-of-the-art FSR methods in recovering face images. The implementation and model will be released at https://***/wcy-cs/SFMNet.

关键词： Low-level vision

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 << < 95 96 97 98 99 100 101 102 103 104 > >>

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：