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
104 篇 期刊文献
43 册 图书

馆藏范围

21,006 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,619 篇 工学
- 11,055 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
- 884 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
2,840 篇 pattern recognit...
1,809 篇 computational mo...
1,715 篇 visualization
1,492 篇 cameras
1,433 篇 three-dimensiona...
1,433 篇 feature extracti...
1,366 篇 shape
1,360 篇 face recognition
1,243 篇 image segmentati...
1,135 篇 robustness
1,124 篇 semantics
992 篇 computer archite...
984 篇 object detection
982 篇 layout
959 篇 benchmark testin...
935 篇 codes
899 篇 computer science
898 篇 object recogniti...

机构

174 篇 univ sci & techn...
158 篇 univ chinese aca...
153 篇 carnegie mellon ...
145 篇 chinese univ hon...
109 篇 microsoft resear...
103 篇 zhejiang univ pe...
99 篇 swiss fed inst t...
95 篇 tsinghua univers...
90 篇 microsoft res as...
90 篇 tsinghua univ pe...
88 篇 shanghai ai lab ...
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...
72 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
58 篇 timofte radu
48 篇 yang yi
47 篇 luc van gool
46 篇 xiaoou tang
44 篇 tian qi
43 篇 darrell trevor
42 篇 loy chen change
42 篇 sun jian
41 篇 qi tian
40 篇 li stan z.
38 篇 li fei-fei
37 篇 chen xilin
36 篇 shan shiguang
35 篇 zhou jie
35 篇 vasconcelos nuno
35 篇 liu yang
35 篇 torralba antonio
34 篇 liu xiaoming

语言

20,981 篇 英文
10 篇 中文
7 篇 其他
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 21007 条记录，以下是1101-1110 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：

Toward Stable, Interpretable, and Lightweight Hyperspectral Super-resolution

Toward Stable, Interpretable, and Lightweight Hyperspectral ...

引用

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

作者： Guo, Wen-Jin Xie, Weiying Jiang, Kai Li, Yunsong Lei, Jie Fang, Leyuan Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha Peoples R China

ISBN: (纸本)9798350301298

For real applications, existing HSI-SR methods are not only limited to unstable performance under unknown scenarios but also suffer from high computation consumption. In this paper, we develop a new coordination optimization framework for stable, interpretable, and lightweight HSI-SR. Specifically, we create a positive cycle between fusion and degradation estimation under a new probabilistic framework. The estimated degradation is applied to fusion as guidance for a degradation-aware HSI-SR. Under the framework, we establish an explicit degradation estimation method to tackle the indeterminacy and unstable performance caused by the black-box simulation in previous methods. Considering the interpretability in fusion, we integrate spectral mixing prior into the fusion process, which can be easily realized by a tiny autoencoder, leading to a dramatic release of the computation burden. Based on the spectral mixing prior, we then develop a partial fine-tune strategy to reduce the computation cost further. Comprehensive experiments demonstrate the superiority of our method against the state-of-the-arts under synthetic and real datasets. For instance, we achieve a 2.3 dB promotion on PSNR with 120x model size reduction and 4300x FLOPs reduction under the CAVE dataset. Code is available in https://***/WenjinGuo/DAEM.

关键词： Low-level vision

来源：评论

学校读者我要写书评

暂无评论

Decoupled Multimodal Distilling for Emotion recognition

Decoupled Multimodal Distilling for Emotion Recognition

引用

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

作者： Li, Yong Wang, Yuanzhi Cui, Zhen Nanjing Univ Sci & Technol Sch Comp Sci & Engn Key Lab Intelligent Percept & Syst High Dimens In Minist EducPCA Lab Nanjing Peoples R China

ISBN: (纸本)9798350301298

Human multimodal emotion recognition (MER) aims to perceive human emotions via language, visual and acoustic modalities. Despite the impressive performance of previous MER approaches, the inherent multimodal heterogeneities still haunt and the contribution of different modalities varies significantly. In this work, we mitigate this issue by proposing a decoupled multimodal distillation (DMD) approach that facilitates flexible and adaptive crossmodal knowledge distillation, aiming to enhance the discriminative features of each modality. Specially, the representation of each modality is decoupled into two parts, i.e., modality-irrelevant/-exclusive spaces, in a self-regression manner. DMD utilizes a graph distillation unit (GD-Unit) for each decoupled part so that each GD can be performed in a more specialized and effective manner. A GD-Unit consists of a dynamic graph where each vertice represents a modality and each edge indicates a dynamic knowledge distillation. Such GD paradigm provides a flexible knowledge transfer manner where the distillation weights can be automatically learned, thus enabling diverse crossmodal knowledge transfer patterns. Experimental results show DMD consistently obtains superior performance than state-of-the-art MER methods. Visualization results show the graph edges in DMD exhibit meaningful distributional patterns w.r.t. the modality-irrelevant/-exclusive feature spaces. Codes are released at https://***/mdswyz/DMD.

关键词： body gesture Humans: Face movement pose

来源：评论

学校读者我要写书评

暂无评论

Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification

Language in a Bottle: Language Model Guided Concept Bottlene...

引用

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

作者： Yang, Yue Panagopoulou, Artemis Zhou, Shenghao Jin, Daniel Callison-Burch, Chris Yatskar, Mark Univ Penn Philadelphia PA 19104 USA

ISBN: (纸本)9798350301298

Concept Bottleneck Models (CBM) are inherently interpretable models that factor model decisions into human-readable concepts. They allow people to easily understand why a model is failing, a critical feature for high-stakes applications. CBMs require manually specified concepts and often under-perform their black box counterparts, preventing their broad adoption. We address these shortcomings and are first to show how to construct high-performance CBMs without manual specification of similar accuracy to black box models. Our approach, Language Guided Bottlenecks (LaBo), leverages a language model, GPT-3, to define a large space of possible bottlenecks. Given a problem domain, LaBo uses GPT-3 to produce factual sentences about categories to form candidate concepts. LaBo efficiently searches possible bottlenecks through a novel submodular utility that promotes the selection of discriminative and diverse information. Ultimately, GPT-3's sentential concepts can be aligned to images using CLIP, to form a bottleneck layer. Experiments demonstrate that LaBo is a highly effective prior for concepts important to visual recognition. In the evaluation with 11 diverse datasets, LaBo bottlenecks excel at few-shot classification: they are 11.7% more accurate than black box linear probes at 1 shot and comparable with more data. Overall, LaBo demonstrates that inherently interpretable models can be widely applied at similar, or better, performance than black box approaches.(1)

关键词： Explainable computer vision

来源：评论

学校读者我要写书评

暂无评论

Cross-Domain Image Captioning with Discriminative Finetuning

Cross-Domain Image Captioning with Discriminative Finetuning

引用

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

作者： Dessi, Roberto Bevilacqua, Michele Gualdoni, Eleonora Carraz Rakotonirina, Nathanael Franzon, Francesca Baroni, Marco UPF Meta AI Barcelona Spain Samaya AI Mountain View CA USA UPF Barcelona Spain UPF ICREA Barcelona Spain

ISBN: (纸本)9798350301298

Neural captioners are typically trained to mimic human-generated references without optimizing for any specific communication goal, leading to problems such as the generation of vague captions. In this paper, we show that fine-tuning an out-of-the-box neural captioner with a self-supervised discriminative communication objective helps to recover a plain, visually descriptive language that is more informative about image contents. Given a target image, the system must learn to produce a description that enables an out-of-the-box text-conditioned image retriever to identify such image among a set of candidates. We experiment with the popular ClipCap captioner, also replicating the main results with BLIP. In terms of similarity to ground-truth human descriptions, the captions emerging from discriminative finetuning lag slightly behind those generated by the non-finetuned model, when the latter is trained and tested on the same caption dataset. However, when the model is used without further tuning to generate captions for out-of-domain datasets, our discriminatively-finetuned captioner generates descriptions that resemble human references more than those produced by the same captioner wihtout finetuning. We further show that, on the Conceptual Captions dataset, discriminatively finetuned captions are more helpful than either vanilla ClipCap captions or ground-truth captions for human annotators tasked with an image discrimination task.(1)

关键词： language reasoning vision

来源：评论

学校读者我要写书评

暂无评论

RMLVQA: A Margin Loss Approach For Visual Question Answering with Language Biases

RMLVQA: A Margin Loss Approach For Visual Question Answering...

引用

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

作者： Basu, Abhipsa Addepalli, Sravanti Babu, R. Venkatesh Indian Inst Sci Vis & AI Lab Bangalore India

ISBN: (纸本)9798350301298

Visual Question Answering models have been shown to suffer from language biases, where the model learns a correlation between the question and the answer, ignoring the image. While early works attempted to use question-only models or data augmentations to reduce this bias, we propose an adaptive margin loss approach having two components. The first component considers the frequency of answers within a question type in the training data, which addresses the concern of the class-imbalance causing the language biases. However, it does not take into account the answering difficulty of the samples, which impacts their learning. We address this through the second component, where instance-specific margins are learnt, allowing the model to distinguish between samples of varying complexity. We introduce a bias-injecting component to our model, and compute the instance-specific margins from the confidence of this component. We combine these with the estimated margins to consider both answer-frequency and task-complexity in the training loss. We show that, while the margin loss is effective for out-of-distribution (ood) data, the bias-injecting component is essential for generalising to in-distribution (id) data. Our proposed approach, Robust Margin Loss for Visual Question Answering (RMLVQA) (1) improves upon the existing state-of-the-art results when compared to augmentation-free methods on benchmark VQA datasets suffering from language biases, while maintaining competitive performance on id data, making our method the most robust one among all comparable methods.

关键词： language reasoning vision

来源：评论

学校读者我要写书评

暂无评论

Watch or Listen: Robust Audio-Visual Speech recognition with Visual Corruption Modeling and Reliability Scoring

Watch or Listen: Robust Audio-Visual Speech Recognition with...

引用

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

作者： Hong, Joanna Kim, Minsu Choi, Jeongsoo Ro, Yong Man Korea Adv Inst Sci & Technol Image & Video Syst Lab Daejeon South Korea

ISBN: (纸本)9798350301298

This paper deals with Audio-Visual Speech recognition (AVSR) under multimodal input corruption situations where audio inputs and visual inputs are both corrupted, which is not well addressed in previous research directions. Previous studies have focused on how to complement the corrupted audio inputs with the clean visual inputs with the assumption of the availability of clean visual inputs. However, in real life, clean visual inputs are not always accessible and can even be corrupted by occluded lip regions or noises. Thus, we firstly analyze that the previous AVSR models are not indeed robust to the corruption of multimodal input streams, the audio and the visual inputs, compared to uni-modal models. Then, we design multimodal input corruption modeling to develop robust AVSR models. Lastly, we propose a novel AVSR framework, namely Audio-Visual Reliability Scoring module (AV-RelScore), that is robust to the corrupted multimodal inputs. The AV-RelScore can determine which input modal stream is reliable or not for the prediction and also can exploit the more reliable streams in prediction. The effectiveness of the proposed method is evaluated with comprehensive experiments on popular benchmark databases, LRS2 and LRS3. We also show that the reliability scores obtained by AV-RelScore well reflect the degree of corruption and make the proposed model focus on the reliable multimodal representations.

关键词： Multi-modal learning

来源：评论

学校读者我要写书评

暂无评论

Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning

Class Balanced Adaptive Pseudo Labeling for Federated Semi-S...

引用

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

作者： Li, Ming Li, Qingli Wang, Yan East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China

ISBN: (纸本)9798350301298

This paper focuses on federated semi-supervised learning (FSSL), assuming that few clients have fully labeled data (labeled clients) and the training datasets in other clients are fully unlabeled (unlabeled clients). Existing methods attempt to deal with the challenges caused by not independent and identically distributed data (Non-IID) setting. Though methods such as sub-consensus models have been proposed, they usually adopt standard pseudo labeling or consistency regularization on unlabeled clients which can be easily influenced by imbalanced class distribution. Thus, problems in FSSL are still yet to be solved. To seek for a fundamental solution to this problem, we present Class Balanced Adaptive Pseudo Labeling (CBAFed), to study FSSL from the perspective of pseudo labeling. In CBAFed, the first key element is a fixed pseudo labeling strategy to handle the catastrophic forgetting problem, where we keep a fixed set by letting pass information of unlabeled data at the beginning of the unlabeled client training in each communication round. The second key element is that we design class balanced adaptive thresholds via considering the empirical distribution of all training data in local clients, to encourage a balanced training process. To make the model reach a better optimum, we further propose a residual weight connection in local supervised training and global model aggregation. Extensive experiments on five datasets demonstrate the superiority of CBAFed. Code will be available at https://***/minglllli/CBAFed.

关键词： vision applications and systems

来源：评论

学校读者我要写书评

暂无评论

EfficientViT: Memory Efficient vision Transformer with Cascaded Group Attention

EfficientViT: Memory Efficient Vision Transformer with Casca...

引用

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

作者： Liu, Xinyu Peng, Houwen Zheng, Ningxin Yang, Yuqing Hu, Han Yuan, Yixuan Chinese Univ Hong Kong Hong Kong Peoples R China Microsoft Res Redmond WA USA

ISBN: (纸本)9798350301298

vision transformers have shown great success due to their high model capabilities. However, their remarkable performance is accompanied by heavy computation costs, which makes them unsuitable for real-time applications. In this paper, we propose a family of high-speed vision transformers named EfficientViT. We find that the speed of existing transformer models is commonly bounded by memory inefficient operations, especially the tensor reshaping and element-wise functions in MHSA. Therefore, we design a new building block with a sandwich layout, i.e., using a single memory-bound MHSA between efficient FFN layers, which improves memory efficiency while enhancing channel communication. Moreover, we discover that the attention maps share high similarities across heads, leading to computational redundancy. To address this, we present a cascaded group attention module feeding attention heads with different splits of the full feature, which not only saves computation cost but also improves attention diversity. Comprehensive experiments demonstrate EfficientViT outperforms existing efficient models, striking a good trade-off between speed and accuracy. For instance, our EfficientViT-M5 surpasses MobileNetV3-Large by 1.9% in accuracy, while getting 40.4% and 45.2% higher throughput on Nvidia V100 GPU and Intel Xeon CPU, respectively. Compared to the recent efficient model MobileViT-XXS, EfficientViT-M2 achieves 1.8% superior accuracy, while running 5.8x/3.7x faster on the GPU/CPU, and 7.4x faster when converted to ONNX format. Code and models are available at here.

关键词： Efficient and scalable vision

来源：评论

学校读者我要写书评

暂无评论

Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation

Extracting Motion and Appearance via Inter-Frame Attention f...

引用

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

作者： Zhang, Guozhen Zhu, Yuhan Wang, Haonan Chen, Youxin Wu, Gangshan Wang, Limin Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Shanghai AI Lab Shanghai Peoples R China Samsung Elect China R&D Ctr Beijing Peoples R China

ISBN: (纸本)9798350301298

Effectively extracting inter-frame motion and appearance information is important for video frame interpolation (VFI). Previous works either extract both types of information in a mixed way or devise separate modules for each type of information, which lead to representation ambiguity and low efficiency. In this paper, we propose a new module to explicitly extract motion and appearance information via a unified operation. Specifically, we rethink the information process in inter-frame attention and reuse its attention map for both appearance feature enhancement and motion information extraction. Furthermore, for efficient VFI, our proposed module could be seamlessly integrated into a hybrid CNN and Transformer architecture. This hybrid pipeline can alleviate the computational complexity of inter-frame attention as well as preserve detailed low-level structure information. Experimental results demonstrate that, for both fixed- and arbitrary-timestep interpolation, our method achieves state-of-the-art performance on various datasets. Meanwhile, our approach enjoys a lighter computation overhead over models with close performance. The source code and models are available at https://***/MCG-NJU/EMA-VFI.

关键词： Low-level vision

来源：评论

学校读者我要写书评

暂无评论

Blind Image Quality Assessment via vision-Language Correspondence: A Multitask Learning Perspective

Blind Image Quality Assessment via Vision-Language Correspon...

引用

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

作者： Zhang, Weixia Zhai, Guangtao Wei, Ying Yang, Xiaokang Ma, Kede Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Hong Kong Peoples R China

ISBN: (纸本)9798350301298

We aim at advancing blind image quality assessment (BIQA), which predicts the human perception of image quality without any reference information. We develop a general and automated multitask learning scheme for BIQA to exploit auxiliary knowledge from other tasks, in a way that the model parameter sharing and the loss weighting are determined automatically. Specifically, we first describe all candidate label combinations (from multiple tasks) using a textual template, and compute the joint probability from the cosine similarities of the visual-textual embeddings. Predictions of each task can be inferred from the joint distribution, and optimized by carefully designed loss functions. Through comprehensive experiments on learning three tasks - BIQA, scene classification, and distortion type identification, we verify that the proposed BIQA method 1) benefits from the scene classification and distortion type identification tasks and outperforms the state-of-the-art on multiple IQA datasets, 2) is more robust in the group maximum differentiation competition, and 3) realigns the quality annotations from different IQA datasets more effectively. The source code is available at https://***/zwx8981/LIQE.

关键词： Datasets and evaluation

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 << < 107 108 109 110 111 112 113 114 115 116 > >>

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：