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

3,915 篇 会议
2 篇 期刊文献

馆藏范围

3,917 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

3,011 篇 工学
- 2,919 篇 计算机科学与技术...
- 216 篇 软件工程
- 141 篇 机械工程
- 133 篇 光学工程
- 42 篇 生物工程
- 28 篇 信息与通信工程
- 25 篇 电气工程
- 17 篇 控制科学与工程
- 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
1,559 篇 医学
- 1,558 篇 临床医学
- 3 篇 基础医学(可授医学...
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
14 篇 管理学
- 7 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

2,408 篇 computer vision
1,085 篇 training
1,043 篇 pattern recognit...
805 篇 conferences
709 篇 computational mo...
543 篇 visualization
491 篇 computer archite...
446 篇 three-dimensiona...
410 篇 semantics
409 篇 benchmark testin...
383 篇 codes
331 篇 transformers
290 篇 deep learning
277 篇 feature extracti...
260 篇 neural networks
256 篇 task analysis
238 篇 shape
216 篇 image segmentati...
204 篇 measurement
202 篇 object detection

机构

72 篇 tsinghua univ pe...
59 篇 univ sci & techn...
55 篇 zhejiang univ pe...
55 篇 chinese univ hon...
51 篇 carnegie mellon ...
51 篇 peng cheng lab p...
49 篇 swiss fed inst t...
47 篇 sensetime res pe...
46 篇 shanghai ai lab ...
42 篇 univ hong kong p...
38 篇 huawei noahs ark...
35 篇 univ chinese aca...
35 篇 shanghai jiao to...
33 篇 alibaba grp peop...
32 篇 tech univ munich...
31 篇 stanford univ st...
30 篇 peking univ peop...
30 篇 swiss fed inst t...
29 篇 adobe res san jo...
29 篇 google res mount...

作者

63 篇 timofte radu
30 篇 van gool luc
19 篇 yang yi
19 篇 qiao yu
18 篇 loy chen change
16 篇 zhang lei
16 篇 radu timofte
15 篇 sun jian
14 篇 liu yang
14 篇 liu shuaicheng
14 篇 tao dacheng
13 篇 li xin
13 篇 fan haoqiang
13 篇 chen wei-ting
12 篇 luo ping
12 篇 chen dongdong
12 篇 wang kai
12 篇 wang xinchao
12 篇 torralba antonio
12 篇 ghanem bernard

语言

3,916 篇 英文
1 篇 其他

检索条件"任意字段=2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022"

共 3917 条记录，以下是3511-3520 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：

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods ...

引用

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

作者： Yawei Li Kai Zhang Radu Timofte Luc Van Gool Fangyuan Kong Mingxi Li Songwei Liu Zongcai Du Ding Liu Chenhui Zhou Jingyi Chen Qingrui Han Zheyuan Li Yingqi Liu Xiangyu Chen Haoming Cai Yu Qiao Chao Dong Long Sun Jinshan Pan Yi Zhu Zhikai Zong Xiaoxiao Liu Zheng Hui Tao Yang Peiran Ren Xuansong Xie Xian-Sheng Hua Yanbo Wang Xiaozhong Ji Chuming Lin Donghao Luo Ying Tai Chengjie Wang Zhizhong Zhang Yuan Xie Shen Cheng Ziwei Luo Lei Yu Zhihong Wen Qi Wul Youwei Li Haoqiang Fan Jian Sun Shuaicheng Liu Yuanfei Huang Meiguang Jin Hua Huang Jing Liu Xinjian Zhang Yan Wang Lingshun Long Gen Li Yuanfan Zhang Zuowei Cao Lei Sun Panaetov Alexander Yucong Wang Minjie Cai Li Wang Lu Tian Zheyuan Wang Hongbing Ma Jie Liu Chao Chen Yidong Cai Jie Tang Gangshan Wu Weiran Wang Shirui Huang Honglei Lu Huan Liu Keyan Wang Jun Chen Shi Chen Yuchun Miao Zimo Huang Lefei Zhang Mustafa Ayazoğ lu Wei Xiong Chengyi Xiong Fei Wang Hao Li Ruimian Wen Zhijing Yang Wenbin Zou Weixin Zheng Tian Ye Yuncheng Zhang Xiangzhen Kong Aditya Arora Syed Waqas Zamir Salman Khan Munawar Hayat Fahad Shahbaz Khan Dandan Gao Dengwen Zhou Qian Ning Jingzhu Tang Han Huang Yufei Wang Zhangheng Peng Haobo Li Wenxue Guan Shenghua Gong Xin Li Jun Liu Wanjun Wang Kun Zeng Hanjiang Lin Xinyu Chen Jinsheng Fang Computer Vision Lab ETH Zurich

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution.

关键词： Measurement Runtime Convolution conferences Superresolution Memory management Graphics processing units

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results

NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution:...

引用

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

作者： Zhang, Kai Gu, Shuhang Timofte, Radu Shang, Taizhang Dai, Qiuju Zhu, Shengchen Yang, Tong Guo, Yandong Jo, Younghyun Yang, Sejong Kim, Seon Joo Zha, Lin Jiang, Jiande Gao, Xinbo Lu, Wen Liu, Jing Yoon, Kwangjin Jeon, Taegyun Akita, Kazutoshi Ooba, Takeru Ukita, Norimichi Luo, Zhipeng Yao, Yuehan Xu, Zhenyu He, Dongliang Wu, Wenhao Ding, Yukang Li, Chao Li, Fu Wen, Shilei Li, Jianwei Yang, Fuzhi Yang, Huan Fu, Jianlong Kim, Byung-Hoon Baek, JaeHyun Ye, Jong Chul Fan, Yuchen Huang, Thomas S. Lee, Junyeop Lee, Bokyeung Min, Jungki Kim, Gwantae Lee, Kanghyu Park, Jaihyun Mykhailych, Mykola Zhong, Haoyu Shi, Yukai Yang, Xiaojun Yang, Zhijing Lin, Liang Zhao, Tongtong Peng, Jinjia Wang, Huibing Jin, Zhi Wu, Jiahao Chen, Yifu Shang, Chenming Zhang, Huanrong Min, Jeongki Hrishikesh, P. S. Puthussery, Densen Jiji, C., V Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland OPPO Res Dongguan Guangdong Peoples R China Yonsei Univ Seoul South Korea Facebook Menlo Pk CA USA Qingdao Hiimage Technol Co Ltd Hisense Visual Technol Co Ltd Qingdao Peoples R China Xidian Univ Xian Peoples R China East China Normal Univ ECNU Multimedia & Comp Vis Lab Shanghai Peoples R China SI Analyt Co Ltd 441 Expo Ro Daejeon 34051 South Korea Toyota Technol Inst TTI Toyota Japan DeepBlue Technol Shanghai Co Ltd Shanghai Peoples R China Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Peking Univ Beijing Peoples R China Founder Grp State Key Lab Digital Publishing Technol Beijing Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Microsoft Res Beijing Peoples R China Korea Adv Inst Sci & Technol KAIST Daejeon South Korea Amazon Web Serv Seattle WA USA Univ Illinois Champaign IL USA Korea Univ Seoul South Korea Wix Com Ltd Tel Aviv Israel Guangdong Univ Technol Guangzhou Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Dalian Maritime Univ Dalian Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou Peoples R China Coll Engn Trivandrum Trivandrum Kerala India

ISBN: (纸本)9781728193601

This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor x16 based on a set of prior examples of low and corresponding high resolution images. The goal is to obtain a network design capable to produce high resolution results with the best perceptual quality and similar to the ground truth. The track had 280 registered participants, and 19 teams submitted the final results. They gauge the state-of-the-art in single image super-resolution.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results

NTIRE 2020 Challenge on Real Image Denoising: Dataset, Metho...

引用

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

作者： Abdelhamed, Abdelrahman Afifi, Mahmoud Timofte, Radu Brown, Michael S. Cao, Yue Zhang, Zhilu Zuo, Wangmeng Zhang, Xiaoling Liu, Jiye Chen, Wendong Wen, Changyuan Liu, Meng Lv, Shuailin Zhang, Yunchao Pan, Zhihong Li, Baopu Xi, Teng Fan, Yanwen Yu, Xiyu Zhang, Gang Liu, Jingtuo Han, Junyu Ding, Errui Yu, Songhyun Park, Bumjun Jeong, Jechang Liu, Shuai Zong, Ziyao Nan, Nan Li, Chenghua Yang, Zengli Bao, Long Wang, Shuangquan Bai, Dongwoon Lee, Jungwon Kim, Youngjung Rho, Kyeongha Shin, Changyeop Kim, Sungho Tang, Pengliang Zhao, Yiyun Zhou, Yuqian Fan, Yuchen Huang, Thomas Li, Zhihao Shah, Nisarg A. Liu, Wei Yan, Qiong Zhao, Yuzhi Mozejko, Marcin Latkowski, Tomasz Treszczotko, Lukasz Szafraniuk, Michal Trojanowski, Krzysztof Wu, Yanhong Michelini, Pablo Navarrete Hu, Fengshuo Lu, Yunhua Kim, Sujin Kim, Wonjin Lee, Jaayeon Choi, Jang-Hwan Zhussip, Magauiya Khassenov, Azamat Kim, Jong Hyun Cho, Hwechul Kansal, Priya Nathan, Sabari Ye, Zhangyu Lu, Xiwen Wu, Yaqi Yang, Jiangxin Cao, Yanlong Tang, Siliang Cao, Yanpeng Maggioni, Matteo Marras, Ioannis Tanay, Thomas Slabaugh, Gregory Yan, Youliang Kang, Myungjoo Choi, Han-Soo Song, Kyungmin Xu, Shusong Lu, Xiaomu Wang, Tingniao Lei, Chunxia Liu, Bin Gupta, Rajat Kumar, Vineet York Univ York N Yorkshire England Swiss Fed Inst Technol Zurich Switzerland Harbin Inst Technol Harbin Peoples R China Huawei Shenzhen Peoples R China Baidu Res Seattle WA USA Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China Hanyang Univ Seoul South Korea North China Univ Technol Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Samsung Semicond Inc San Jose CA USA Agcy Def Dev Seoul South Korea Beijing Univ Posts & Telecommun Beijing Peoples R China Univ Illinois Champaign IL USA Nanjing Univ Nanjing Peoples R China Indian Inst Technol Jodhpur Rajasthan India SenseTime Res Hong Kong Peoples R China TCL Res Europe Warsaw Poland BOE Artificial Intelligence & Big Data Res Inst Beijing Peoples R China Seoul Natl Univ Seoul South Korea ST Unitas Seoul South Korea Ewha Womans Univ Seoul South Korea Couger Inc Tokyo Japan Zhejiang Univ Hangzhou Peoples R China Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China Harbin Inst Technol Shenzhen Shenzhen Peoples R China Huawei Technol Res & Dev UK Ltd Noahs Ark Lab London London England Dahua Technol Hangzhou Peoples R China Indian Inst Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9781728193601

This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new version of the previous NTIRE 2019 challenge on real image denoising that was based on the SIDD benchmark. This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+. This challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer-pattern rawRGB and (2) the standard RGB (sRGB) color spaces. Each track similar to 250 registered participants. A total of 22 teams, proposing 24 methods, competed in the final phase of the challenge. The proposed methods by the participating teams represent the current state-of-the-art performance in image denoising targeting real noisy images. The newly collected SIDD+ datasets are publicly available at: https://***/siddplus_data.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

$\text{NPBG++}$: Accelerating Neural Point-Based Graphics

$\text{NPBG++}$: Accelerating Neural Point-Based Graphics

引用

conference on computer vision and pattern recognition (CVPR)

作者： Ruslan Rakhimov Andrei-Timotei Ardelean Victor Lempitsky Evgeny Burnaev Skolkovo Institute of Science and Technology Russia Yandex Russia Artificial Intelligence Research Institute Russia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469470

We present a new system $(NPBG++)$ for the novel view synthesis (NVS) task that achieves high rendering realism with low scene fitting time. Our method efficiently lever-ages the multiview observations and the point cloud of a static scene to predict a neural descriptor for each point, improving upon the pipeline of Neural Point-Based Graph-ics [1] in several important ways. By predicting the descrip-tors with a single pass through the source images, we lift the requirement of per-scene optimization while also making the neural descriptors view-dependent and more suit-able for scenes with strong non-Lambertian effects. In our comparisons, the proposed system outperforms previous NVS approaches in terms of fitting and rendering runtimes while producing images of similar quality. Project page: https://***/npbgpp/.

关键词： Point cloud compression Graphics computer vision Runtime Fitting Pipelines Rendering (computer graphics)

来源：评论

学校读者我要写书评

暂无评论

Style-ERD: Responsive and Coherent Online Motion Style Transfer

Style-ERD: Responsive and Coherent Online Motion Style Trans...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Tianxin Tao Xiaohang Zhan Zhongquan Chen Michiel van de Panne University of British Columbia University of California Davis

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469470

Motion style transfer is a common method for enriching character animation. Motion style transfer algorithms are often designed for offline settings where motions are processed in segments. However, for online animation applications, such as real-time avatar animation from motion capture, motions need to be processed as a stream with minimal latency. In this work, we realize a flexible, high-quality motion style transfer method for this setting. We propose a novel style transfer model, Style-ERD, to stylize motions in an online manner with an Encoder-Recurrent-Decoder structure, along with a novel discriminator that combines feature attention and temporal attention. Our method stylizes motions into multiple target styles with a unified model. Although our method targets online settings, it outperforms previous offline methods in motion realism and style expressiveness and provides significant gains in runtime efficiency.

关键词： computer vision Runtime Perturbation methods Motion segmentation Memory modules Animation Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Learning Canonical $\mathcal{F}$-Correlation Projection for Compact Multiview Representation

Learning Canonical $\mathcal{F}$-Correlation Projection for ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yun-Hao Yuan Jin Lil Yun Lil Jipeng Qiang Yi Zhu Xiaobo Shen Jianping Gou School of Information Engineering Yangzhou University Yangzhou China School of Computer Science Nanjing University of Science and Technology Nanjing China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469470

Canonical correlation analysis (CCA) matters in multi-view representation learning. But, CCA and its most variants are essentially based on explicit or implicit covariance matrices. It means that they have no ability to model the nonlinear relationship among features due to intrinsic linearity of covariance. In this paper, we address the preceding problem and propose a novel canonical F-correlation framework by exploring and exploiting the nonlinear relationship between different features. The framework projects each feature rather than observation into a certain new space by an arbitrary nonlinear mapping, thus resulting in more flexibility in real applications. With this frame-work as a tool, we propose a correlative covariation projection (CCP) method by using an explicit nonlinear mapping. Moreover, we further propose a multiset version of CCP dubbed MCCP for learning compact representation of more than two views. The proposed MCCP is solved by an iterative method, and we prove the convergence of this iteration. A series of experimental results on six benchmark datasets demonstrate the effectiveness of our proposed CCP and MCCP methods.

关键词： Representation learning computer vision Correlation Linearity Benchmark testing pattern recognition Iterative methods

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2021 Challenge on Video Super-Resolution

NTIRE 2021 Challenge on Video Super-Resolution

引用

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

作者： Sanghyun Son Suyoung Lee Seungjun Nah Radu Timofte Kyoung Mu Lee Kelvin C. K. Chan Shangchen Zhou Xiangyu Xu Chen Change Loy Boyuan Jiang Chuming Lin Yuchun Dong Donghao Luo Wenqing Chu Xiaozhong Ji Siqian Yang Ying Tai Chengjie Wang Jilin Li Feiyue Huang Chengpeng Chen Xiaojie Chu Jie Zhang Xin Lu Liangyu Chen Jing Lin Guodong Du Jia Hao Xueyi Zou Qi Zhang Lielin Jiang Xin Li He Zheng Fanglong Liu Dongliang He Fu Li Qingqing Dang Peng Yi Zhongyuan Wang Kui Jiang Junjun Jiang Jiayi Ma Yuxiang Chen Yutong Wang Ting Liu Qichao Sun Huanwei Liang Yiming Li Zekun Li Zhubo Ruan Fanjie Shang Chen Guo Haining Li Renjun Luo Longjie Shen Kassiani Zafirouli Konstantinos Karageorgos Konstantinos Konstantoudakis Anastasios Dimou Petros Daras Xiaowei Song Xu Zhuo Hanxi Liu Mengxi Guo Junlin Li Yu Li Ye Zhu Qing Wang Shijie Zhao Xiaopeng Sun Gen Zhan Tangxin Xie Yu Jia Yunhua Lu Wenhao Zhang Mengdi Sun Pablo Navarrete Michelini Xueheng Zhang Hao Jiang Zhiyu Chen Li Chen Zhiwei Xiong Zeyu Xiao Ruikang Xu Zhen Cheng Xueyang Fu Fenglong Song Zhipeng Luo Yuehan Yao Saikat Dutta Nisarg A. Shah Sourya Dipta Das Peng Zhao Yukai Shi Hongying Liu Fanhua Shang Yuanyuan Liu Fei Chen Fangxu Yu Ruisheng Gao Yixin Bai Jeonghwan Heo Shijie Yue Chenghua Li Jinjing Li Qian Zheng Ruipeng Gang Ruixia Song Seungwoo Wee Jechang Jeong Chen Li Geyingjie Wen Xinning Chai Li Song Seoul National University

Super-Resolution (SR) is a fundamental computer vision task that aims to obtain a high-resolution clean image from the given low-resolution counterpart. This paper reviews the NTIRE 2021 Challenge on Video Super-Resolution. We present evaluation results from two competition tracks as well as the proposed solutions. Track 1 aims to develop conventional video SR methods focusing on the restoration quality. Track 2 assumes a more challenging environment with lower frame rates, casting spatio-temporal SR problem. In each competition, 247 and 223 participants have registered, respectively. During the final testing phase, 14 teams competed in each track to achieve state-of-the-art performance on video SR tasks.

关键词： computer vision Casting conferences Superresolution Focusing pattern recognition Image restoration

来源：评论

学校读者我要写书评

暂无评论

Does text attract attention on e-commerce images: A novel saliency prediction dataset and method

Does text attract attention on e-commerce images: A novel sa...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Lai Jiang Yifei Li Shengxi Li Mai Xu Se Lei Yichen Guo Bo Huang School of Electronic and Information Engineering Beihang University Beijing China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469470

E-commerce images are playing a central role in attracting people's attention when retailing and shopping online, and an accurate attention prediction is of significant importance for both customers and retailers, where its research is yet to start. In this paper, we establish the first dataset of saliency e-commerce images (SalECI), which allows for learning to predict saliency on the e-commerce images. We then provide specialized and thorough analysis by high-lighting the distinct features of e-commerce images, e.g., non-locality and correlation to text regions. Correspondingly, taking advantages of the non-local and self-attention mechanisms, we propose a salient SWin-Transformer back-bone, followed by a multi-task learning with saliency and text detection heads, where an information flow mechanism is proposed to further benefit both tasks. Experimental results have verified the state-of-the-art performances of our work in the e-commerce scenario.

关键词： Training computer vision Head Data analysis Correlation Gaze tracking Multitasking

来源：评论

学校读者我要写书评

暂无评论

Sketch3T: Test-Time Training for Zero-Shot SBIR

Sketch3T: Test-Time Training for Zero-Shot SBIR

引用

conference on computer vision and pattern recognition (CVPR)

作者： Aneeshan Sain Ayan Kumar Bhunia Vaishnav Potlapalli Pinaki Nath Chowdhury Tao Xiang Yi-Zhe Song SketchX CVSSP University of Surrey United Kingdom iFlyTek-Surrey Joint Research Centre on Artificial Intelligence

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469470

Zero-shot sketch-based image retrieval typically asks for a trained model to be applied as is to unseen categories. In this paper, we question to argue that this setup by definition is not compatible with the inherent abstract and subjective nature of sketches – the model might transfer well to new categories, but will not understand sketches existing in different test-time distribution as a result. We thus extend ZS-SBIR asking it to transfer to both categories and sketch distributions. Our key contribution is a test-time training paradigm that can adapt using just one sketch. Since there is no paired photo, we make use of a sketch raster-vector reconstruction module as a self-supervised auxiliary task. To maintain the fidelity of the trained cross-modal joint embedding during test-time update, we design a novel meta-learning based training paradigm to learn a separation between model updates incurred by this auxiliary task from those off the primary objective of discriminative learning. Extensive experiments show our model to outperform state-of-the-arts, thanks to the proposed test-time adaption that not only transfers to new categories but also accommodates to new sketching styles.

关键词： Training Representation learning Adaptation models computer vision Machine vision Image retrieval pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Uformer: A General U-Shaped Transformer for Image Restoration

Uformer: A General U-Shaped Transformer for Image Restoratio...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zhendong Wang Xiaodong Cun Jianmin Bao Wengang Zhou Jianzhuang Liu Houqiang Li University of Science and Technology of China University of Macau Huawei Noah's Ark Lab

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469470

In this paper, we present Uformer, an effective and efficient Transformer-based architecture for image restoration, in which we build a hierarchical encoder-decoder network using the Transformer block. In Uformer, there are two core designs. First, we introduce a novel locally-enhanced window (LeWin) Transformer block, which performs non-overlapping window-based self-attention instead of global self-attention. It significantly reduces the computational complexity on high resolution feature map while capturing local context. Second, we propose a learnable multi-scale restoration modulator in the form of a multi-scale spatial bias to adjust features in multiple layers of the Uformer decoder. Our modulator demonstrates superior capability for restoring details for various image restoration tasks while introducing marginal extra parameters and computational cost. Powered by these two designs, Uformer enjoys a high capability for capturing both local and global dependencies for image restoration. To evaluate our approach, extensive experiments are conducted on several image restoration tasks, including image denoising, motion deblurring, defocus deblurring and deraining. Without bells and whistles, our Uformer achieves superior or comparable performance compared with the state-of-the-art algorithms. The code and models are available at https://***/ZhendongWang6/Uformer.

关键词： computer vision Modulation Transformer cores Transformers Image restoration pattern recognition Decoding

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392页 << < 348 349 350 351 352 353 354 355 356 357 > >>

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：