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

11,886 篇 会议
5 篇 期刊文献

馆藏范围

11,891 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

8,060 篇 工学
- 7,618 篇 计算机科学与技术...
- 796 篇 机械工程
- 688 篇 电气工程
- 361 篇 软件工程
- 228 篇 控制科学与工程
- 41 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...
- 7 篇 交通运输工程
- 6 篇 电子科学与技术（可...
- 6 篇 建筑学
- 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...
254 篇 理学
- 198 篇 系统科学
- 32 篇 物理学
- 21 篇 生物学
- 19 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...
1,745 篇 computational mo...
1,504 篇 visualization
1,360 篇 three-dimensiona...
1,082 篇 semantics
1,001 篇 benchmark testin...
990 篇 codes
959 篇 computer archite...
892 篇 deep learning
777 篇 conferences
754 篇 task analysis
716 篇 feature extracti...
560 篇 transformers
536 篇 face recognition
526 篇 neural networks
495 篇 object detection
490 篇 image segmentati...
472 篇 cameras

机构

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

作者

74 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
30 篇 zha zheng-jun
29 篇 sun jian
28 篇 li xin
27 篇 qi tian
27 篇 luc van gool
26 篇 vasconcelos nuno
25 篇 liu xiaoming
25 篇 darrell trevor
24 篇 zheng wei-shi
24 篇 luo ping

语言

11,850 篇 英文
40 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 11891 条记录，以下是1371-1380 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：

Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection

Learning on Gradients: Generalized Artifacts Representation ...

引用

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

作者： Tan, Chuangchuang Zhao, Yao Wei, Shikui Gu, Guanghua Wei, Yunchao Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing Peoples R China Yanshan Univ Sch Informat Sci & Engn Qinhuangdao Peoples R China Hebei Key Lab Informat Transmiss & Signal Proc Qinhuangdao Peoples R China

ISBN: (纸本)9798350301298

Recently, there has been a significant advancement in image generation technology, known as GAN. It can easily generate realistic fake images, leading to an increased risk of abuse. However, most image detectors suffer from sharp performance drops in unseen domains. The key of fake image detection is to develop a generalized representation to describe the artifacts produced by generation models. In this work, we introduce a novel detection framework, named Learning on Gradients (LGrad), designed for identifying GAN-generated images, with the aim of constructing a generalized detector with cross-model and cross-data. Specifically, a pretrained CNN model is employed as a transformation model to convert images into gradients. Subsequently, we leverage these gradients to present the generalized artifacts, which are fed into the classifier to ascertain the authenticity of the images. In our framework, we turn the data-dependent problem into a transformation-model-dependent problem. To the best of our knowledge, this is the first study to utilize gradients as the representation of artifacts in GAN-generated images. Extensive experiments demonstrate the effectiveness and robustness of gradients as generalized artifact representations. Our detector achieves a new state-of-the-art performance with a remarkable gain of 11.4%. The code is released at https://***/chuangchuangtan/LGrad.

关键词： detection recognition: Categorization retrieval

来源：评论

学校读者我要写书评

暂无评论

Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection

Discriminative Co-Saliency and Background Mining Transformer...

引用

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

作者： Li, Long Han, Junwei Zhang, Ni Liu, Nian Khan, Salman Cholakkal, Hisham Anwer, Rao Muhammad Khan, Fahad Shahbaz Northwestern Polytech Univ Grande Prairie AB Canada Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Australian Natl Univ Canberra Australia Linkoping Univ CVL Linkoping Sweden

ISBN: (纸本)9798350301298

Most previous co-salient object detection works mainly focus on extracting co-salient cues via mining the consistency relations across images while ignore explicit exploration of background regions. In this paper, we propose a Discriminative co-saliency and background Mining Transformer framework (DMT) based on several economical multi-grained correlation modules to explicitly mine both co-saliency and background information and effectively model their discrimination. Specifically, we first propose a region-to-region correlation module for introducing inter-image relations to pixel-wise segmentation features while maintaining computational efficiency. Then, we use two types of pre-defined tokens to mine co-saliency and background information via our proposed contrast-induced pixel-to-token correlation and co-saliency token-to-token correlation modules. We also design a token-guided feature refinement module to enhance the discriminability of the segmentation features under the guidance of the learned tokens. We perform iterative mutual promotion for the segmentation feature extraction and token construction. Experimental results on three benchmark datasets demonstrate the effectiveness of our proposed method. The source code is available at: https://***/dragonlee258079/DMT.

关键词： Low-level vision

来源：评论

学校读者我要写书评

暂无评论

Adversarial Normalization: I Can visualize Everything (ICE)

Adversarial Normalization: I Can visualize Everything (ICE)

引用

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

作者： Choi, Hoyoung Jin, Seungwan Han, Kyungsik Hanyang Univ Seoul South Korea

ISBN: (纸本)9798350301298

vision transformers use [CLS] tokens to predict image classes. Their explainability visualization has been studied using relevant information from [CLS] tokens or focusing on attention scores during self-attention. Such visualization, however, is challenging because of the dependence of the structure of a vision transformer on skip connections and attention operators, the instability of non-linearities in the learning process, and the limited reflection of self-attention scores on relevance. We argue that the output vectors for each input patch token in a vision transformer retain the image information of each patch location, which can facilitate the prediction of an image class. In this paper, we propose ICE (Adversarial Normalization: I Can visualize Everything), a novel method that enables a model to directly predict a class for each patch in an image;thus, advancing the effective visualization of the explainability of a vision transformer. Our method distinguishes background from foreground regions by predicting background classes for patches that do not determine image classes. We used the DeiT-S model, the most representative model employed in studies, on the explainability visualization of vision transformers. On the ImageNet-Segmentation dataset, ICE outperformed all explainability visualization methods for four cases depending on the model size. We also conducted quantitative and qualitative analyses on the tasks of weakly-supervised object localization and unsupervised object discovery. On the CUB200-2011 and PASCALVOC07/12 datasets, ICE achieved comparable performance to the state-of-the-art methods. We incorporated ICE into the encoder of DeiT-S and improved efficiency by 44.01% on the ImageNet dataset over that achieved by the original DeiT-S model. We showed performance on the accuracy and efficiency comparable to EViT, the state-of-the-art pruning model, demonstrating the effectiveness of ICE. The code is available at https://***/Hanyang-HCC-La

关键词： Explainable computer vision

来源：评论

学校读者我要写书评

暂无评论

Training Debiased Subnetworks with Contrastive Weight Pruning

Training Debiased Subnetworks with Contrastive Weight Prunin...

引用

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

作者： Park, Geon Yeong Lee, Sangmin Lee, Sang Wan Ye, Jong Chul Korea Adv Inst Sci & Technol KAIST Bio & Brain Engn Daejeon South Korea Korea Adv Inst Sci & Technol KAIST Math Sci Daejeon South Korea Korea Adv Inst Sci & Technol KAIST Kim Jaechul Grad Sch AI Daejeon South Korea

ISBN: (纸本)9798350301298

Neural networks are often biased to spuriously correlated features that provide misleading statistical evidence that does not generalize. This raises an interesting question: "Does an optimal unbiased functional subnetwork exist in a severely biased network? If so, how to extract such subnetwork?" While empirical evidence has been accumulated about the existence of such unbiased subnetworks, these observations are mainly based on the guidance of ground-truth unbiased samples. Thus, it is unexplored how to discover the optimal subnetworks with biased training datasets in practice. To address this, here we first present our theoretical insight that alerts potential limitations of existing algorithms in exploring unbiased subnetworks in the presence of strong spurious correlations. We then further elucidate the importance of bias-conflicting samples on structure learning. Motivated by these observations, we propose a Debiased Contrastive Weight Pruning (DCWP) algorithm, which probes unbiased subnetworks without expensive group annotations. Experimental results demonstrate that our approach significantly outperforms state-of-the-art debiasing methods despite its considerable reduction in the number of parameters.

关键词： accountability ethics in vision fairness privacy Transparency

来源：评论

学校读者我要写书评

暂无评论

Boost vision Transformer with GPU-Friendly Sparsity and Quantization

Boost Vision Transformer with GPU-Friendly Sparsity and Quan...

引用

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

作者： Yu, Chong Chen, Tao Gan, Zhongxue Fan, Jiayuan Fudan Univ Acad Engn & Technol Shanghai Peoples R China NVIDIA Corp Taipei Taiwan Fudan Univ Sch Informat Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9798350301298

The transformer extends its success from the language to the vision domain. Because of the stacked self-attention and cross-attention blocks, the acceleration deployment of vision transformer on GPU hardware is challenging and also rarely studied. This paper thoroughly designs a compression scheme to maximally utilize the GPU-friendly 2:4 fine-grained structured sparsity and quantization. Specially, an original large model with dense weight parameters is first pruned into a sparse one by 2:4 structured pruning, which considers the GPU's acceleration of 2:4 structured sparse pattern with FP16 data type, then the floating-point sparse model is further quantized into a fixed-point one by sparse-distillation-aware quantization aware training, which considers GPU can provide an extra speedup of 2:4 sparse calculation with integer tensors. A mixed-strategy knowledge distillation is used during the pruning and quantization process. The proposed compression scheme is flexible to support supervised and unsupervised learning styles. Experiment results show GPUSQ-ViT scheme achieves state-of-the-art compression by reducing vision transformer models 6.4-12.7x on model size and 30.3-62x on FLOPs with negligible accuracy degradation on ImageNet classification, COCO detection and ADE20K segmentation benchmarking tasks. Moreover, GPUSQ-ViT can boost actual deployment performance by 1.39-1.79x and 3.22-3.43x of latency and throughput on A100 GPU, and 1.57-1.69x and 2.11-2.51x improvement of latency and throughput on AGX Orin.

关键词： Efficient and scalable vision

来源：评论

学校读者我要写书评

暂无评论

Difficulty Estimation with Action Scores for computer vision Tasks

Difficulty Estimation with Action Scores for Computer Vision...

引用

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

作者： Arriaga, Octavio Palacio, Sebastian Valdenegro-Toro, Matias Univ Bremen Bremen Germany German Res Ctr Artificial Intelligence Kaiserslautern Germany Univ Groningen Groningen Netherlands

ISBN: (纸本)9798350302493

As more machine learning models are now being applied in real world scenarios it has become crucial to evaluate their difficulties and biases. In this paper we present an unsupervised method for calculating a difficulty score based on the accumulated loss per epoch. Our proposed method does not require any modification to the model, neither any external supervision, and it can be easily applied to a wide range of machine learning tasks. We provide results for the tasks of image classification, image segmentation, and object detection. We compare our score against similar metrics and provide theoretical and empirical evidence of their difference. Furthermore, we show applications of our proposed score for detecting incorrect labels, and test for possible biases.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Classifying Cable Tendency with Semantic Segmentation by Utilizing Real and Simulated RGB Data

Classifying Cable Tendency with Semantic Segmentation by Uti...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Chien, Pei-Chun Liao, Powei Fukuzawa, Eiji Ohya, Jun Yazaki Corp Shizuoka Japan Waseda Univ Tokyo Japan

ISBN: (纸本)9798350318920;9798350318937

Cable tendency is the potential shape or characteristic that a cable may possess while being manipulated, of which some are considered erroneous and should be identified as a part of anomaly detection during an automatic manipulation. This research explores the ability of deep-learning models in learning the cable tendencies that, contrary to typical classification tasks of multi-object scenarios, is to differentiate the multiple states displayable by the same object - in this case, cables. By training multiple models with different combinations of self-collected real-world data and self-generated simulation data, a comparative study is carried out to compare the performance of each approach. In conclusion, the effectiveness of detecting three abnormal states and shapes of cables, and using simulation data is certificated in experiments.

关键词： Algorithms Algorithms Applications Computational photography image and video synthesis Image recognition and understanding Robotics

来源：评论

学校读者我要写书评

暂无评论

Multi-Class Segmentation from Aerial Views using Recursive Noise Diffusion

Multi-Class Segmentation from Aerial Views using Recursive N...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Kolbeinsson, Benedikt Mikolajczyk, Krystian Imperial Coll London London England

ISBN: (纸本)9798350318920;9798350318937

Semantic segmentation from aerial views is a crucial task for autonomous drones, as they rely on precise and accurate segmentation to navigate safely and efficiently. However, aerial images present unique challenges such as diverse viewpoints, extreme scale variations, and high scene complexity. In this paper, we propose an end-to-end multi-class semantic segmentation diffusion model that addresses these challenges. We introduce recursive denoising to allow information to propagate through the denoising process, as well as a hierarchical multi-scale approach that complements the diffusion process. Our method achieves promising results on the UAVid dataset and state-of-the-art performance on the Vaihingen Building segmentation benchmark. Being the first iteration of this method, it shows great promise for future improvements. Our code and models are available at: https://***/benediktkol/recursive-noise-diffusion

关键词： Algorithms Applications Image recognition and understanding Robotics

来源：评论

学校读者我要写书评

暂无评论

Enhancing Soft Biometric Face Template Privacy with Mutual Information-Based Image Attacks

Enhancing Soft Biometric Face Template Privacy with Mutual I...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Rezgui, Zohra Strisciuglio, Nicola Veldhuis, Raymond Univ Twente Enschede Netherlands Norwegian Univ Sci & Technol Gjovik Norway

ISBN: (纸本)9798350370287;9798350370713

The features learned by deep-learning based face recognition networks pose privacy risks as they encode sensitive information that could be used to infer demographic attributes. In this paper, we propose an image-based solution that enhances the soft biometric privacy of the templates generated by face recognition networks. The method uses a reliable mutual information estimation and simulates a minimization step of the mutual information between the features and the target variable. We comprehensively assess the effectiveness of our approach on the gender classification task by formulating two distinct evaluation settings: one for evaluating the performance of the approach's ability to fool a given gender classifier and another for evaluating its ability to hinder the separability of the gender distributions. We conduct an extensive analysis, considering varying levels of perturbation. We show the potential of our method as a privacy-enhancing method that preserves the verification performance as well as a strong single-step adversarial attack.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Event-guided Person Re-Identification via Sparse-Dense Complementary Learning

Event-guided Person Re-Identification via Sparse-Dense Compl...

引用

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

作者： Cao, Chengzhi Fu, Xueyang Liu, Hongjian Huang, Yukun Wang, Kunyu Luo, Jiebo Zha, Zheng-Jun Univ Sci & Technol China Hefei Peoples R China Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9798350301298

Video-based person re-identification (Re-ID) is a prominent computer vision topic due to its wide range of video surveillance applications. Most existing methods utilize spatial and temporal correlations in frame sequences to obtain discriminative person features. However, inevitable degradation, e.g., motion blur contained in frames, leading to the loss of identity-discriminating cues. Recently, a new bio-inspired sensor called event camera, which can asynchronously record intensity changes, brings new vitality to the Re-ID task. With the microsecond resolution and low latency, it can accurately capture the movements of pedestrians even in the degraded environments. In this work, we propose a Sparse-Dense Complementary Learning (SDCL) Framework, which effectively extracts identity features by fully exploiting the complementary information of dense frames and sparse events. Specifically, for frames, we build a CNN-based module to aggregate the dense features of pedestrian appearance step by step, while for event streams, we design a bio-inspired spiking neural network (SNN) backbone, which encodes event signals into sparse feature maps in a spiking form, to extract the dynamic motion cues of pedestrians. Finally, a cross feature alignment module is constructed to fuse motion information from events and appearance cues from frames to enhance identity representation learning. Experiments on several benchmarks show that by employing events and SNN into Re-ID, our method significantly outperforms competitive methods. The code is available at https://***/ChengzhiCao/SDCL.

关键词： detection recognition: Categorization retrieval

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 << < 134 135 136 137 138 139 140 141 142 143 > >>

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：