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检索条件"任意字段=IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops"

共 8962 条记录，以下是731-740 订阅

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ALPS: Adaptive Quantization of Deep Neural Networks with GeneraLized PositS

ALPS: Adaptive Quantization of Deep Neural Networks with Gen...

引用

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

作者： Langroudi, Hamed F. Karia, Vedant Carmichael, Zachariah Zyarah, Abdullah Pandit, Tej Gustafson, John L. Kudithipudi, Dhireesha Univ Texas San Antonio Neuromorph AI Lab San Antonio TX 78249 USA Rochester Inst Technol Rochester NY 14623 USA Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781665448994

In this paper, a new adaptive quantization algorithm for generalized posit format is presented, to optimally represent the dynamic range and distribution of deep neural network parameters. Adaptation is achieved by minimizing the intra-layer posit quantization error with a compander. The efficacy of the proposed quantization algorithm is studied within a new low-precision framework, ALPS, on ResNet-50 and EfficientNet models for classification tasks. Results assert that the accuracy and energy dissipation of low-precision DNNs using generalized posits outperform other well-known numerical formats, including standard posits.

关键词： Deep learning Quantization (signal) Adaptive systems Upper bound Numerical analysis Heuristic algorithms pattern recognition

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Dancing under the stars: video denoising in starlight

Dancing under the stars: video denoising in starlight

引用

2022 ieee/CVF conference on computer vision and pattern recognition, CVPR 2022

作者： Monakhova, Kristina Richter, Stephan R. Waller, Laura Koltun, Vladlen Uc Berkeley United States Intel Labs

Imaging in low light is extremely challenging due to low photon counts. Using sensitive CMOS cameras, it is currently possible to take videos at night under moonlight (0.05-0.3 lux illumination). In this paper, we dem... 详细信息

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

关键词： Semiconductor device modeling Noise reduction Lighting Stars Cameras Quality assessment pattern recognition

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Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing

Dressing in Order: Recurrent Person Image Generation for Pos...

引用

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

作者： Cui, Aiyu McKee, Daniel Lazebnik, Svetlana Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9781665448994

We propose a flexible person generation framework called Dressing in Order (DiOr), which supports 2D pose transfer, virtual try-on, and several fashion editing tasks. The key to DiOr is a novel recurrent generation pipeline to sequentially put garments on a person, so that trying on the same garments in different orders will result in different looks. Our system can produce dressing effects not achievable by existing work, including different interactions of garments (e.g., wearing a top tucked into the bottom or over it), as well as layering of multiple garments of the same type (e.g., jacket over shirt over t-shirt). DiOr explicitly encodes the shape and texture of each garment, enabling these elements to be edited separately. Extensive evaluations show that DiOr outperforms other recent methods like ADGAN [18] in terms of output quality, and handles a wide range of editing functions for which there is no direct supervision.

关键词： computer vision Shape Image synthesis conferences Clothing Pipelines pattern recognition

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Color Me Good: Branding in the Coloring Style of Movie Posters

Color Me Good: Branding in the Coloring Style of Movie Poste...

引用

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

作者： Agrawal, Rishabh Sivaprasad, Sarath Pedanekar, Niranjan TCS Res Tata Consultancy Serv Pune 411013 Maharashtra India

ISBN: (纸本)9781665448994

Brand logos are often rendered in a different style based on a context such as an event promotion. For example, Warner Bros. uses a different variety of their brand logo for different movies for promotion and aesthetic appeal. In this paper, we propose an automated method to render brand logos in the coloring style of branding material such as movie posters. For this, we adopt a photo-realistic neural style transfer method using movie posters as the style source. We propose a color-based image segmentation and matching method to assign style segments to logo segments. Using these, we render the well-known Warner Bros. logo in the coloring style of 141 movie posters. We also present survey results where 287 participants rate the machine-stylized logos for their representativeness and visual appeal.

关键词： Image segmentation Visualization computer vision Image color analysis conferences Semantics Brightness

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Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation

Dealing with Missing Modalities in the Visual Question Answe...

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

作者： Cho, Jae Won Kim, Dong-Jin Choi, Jinsoo Jung, Yunjae Kweon, In So Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665448994

In this work, we address the issues of the missing modalities that have arisen from the Visual Question Answer-Difference prediction task and find a novel method to solve the task at hand. We address the missing modality-the ground truth answers-that are not present at test time and use a privileged knowledge distillation scheme to deal with the issue of the missing modality. In order to efficiently do so, we first introduce a model, the "Big" Teacher, that takes the image/question/answer triplet as its input and outperforms the baseline, then use a combination of models to distill knowledge to a target network (student) that only takes the image/question pair as its inputs. We experiment our models on the VizWiz and VQA-V2 Answer Difference datasets and show through extensive experimentation and ablation the performance of our method and a diverse possibility for future research.

关键词： Visualization computer vision conferences Knowledge discovery pattern recognition Task analysis Bars

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MVFuseNet: Improving End-to-End Object Detection and Motion Forecasting through Multi-View Fusion of LiDAR Data

MVFuseNet: Improving End-to-End Object Detection and Motion ...

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

作者： Laddha, Ankit Gautam, Shivam Palombo, Stefan Pandey, Shreyash Vallespi-Gonzalez, Carlos Aurora Innovat Mountain View CA 94043 USA

ISBN: (纸本)9781665448994

In this work, we propose MVFuseNet, a novel end-to-end method for joint object detection and motion forecasting from a temporal sequence of LiDAR data. Most existing methods operate in a single view by projecting data in either range view (RV) or bird's eye view (BEV). In contrast, we propose a method that effectively utilizes both RV and BEV for spatio-temporal feature learning as part of a temporal fusion network as well as for multi-scale feature learning in the backbone network. Further, we propose a novel sequential fusion approach that effectively utilizes multiple views in the temporal fusion network. We show the benefits of our multi-view approach for the tasks of detection and motion forecasting on two large-scale self-driving data sets, achieving state-of-the-art results. Furthermore, we show that MVFusenet scales well to large operating ranges while maintaining real-time performance.

关键词： computer vision Laser radar conferences Object detection Real-time systems pattern recognition Forecasting

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Shot in the Dark: Few-Shot Learning with No Base-Class Labels

Shot in the Dark: Few-Shot Learning with No Base-Class Label...

引用

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

作者： Chen, Zitian Maji, Subhransu Learned-Miller, Erik Univ Massachusetts Amherst Amherst MA 01003 USA

ISBN: (纸本)9781665448994

Few-shot learning aims to build classifiers for new classes from a small number of labeled examples and is commonly facilitated by access to examples from a distinct set of 'base classes'. The difference in data distribution between the test set (novel classes) and the base classes used to learn an inductive bias often results in poor generalization on the novel classes. To alleviate problems caused by the distribution shift, previous research has explored the use of unlabeled examples from the novel classes, in addition to labeled examples of the base classes, which is known as the transductive setting. In this work, we show that, surprisingly, off-the-shelf self-supervised learning outperforms transductive few-shot methods by 3.9% for 5-shot accuracy on miniImageNet without using any base class labels. This motivates us to examine more carefully the role of features learned through self-supervision in few-shot learning. Comprehensive experiments are conducted to compare the transferability, robustness, efficiency, and the complementarity of supervised and self-supervised features.

关键词： computer vision conferences Supervised learning Robustness pattern recognition

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Dual Contrastive Learning for Unsupervised Image-to-Image Translation

Dual Contrastive Learning for Unsupervised Image-to-Image Tr...

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

作者： Han, Junlin Shoeiby, Mehrdad Petersson, Lars Armin, Mohammad Ali DATA61 CSIRO Canberra ACT Australia Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9781665448994

Unsupervised image-to-image translation tasks aim to find a mapping between a source domain X and a target domain Y from unpaired training data. Contrastive learning for Unpaired image-to-image Translation (CUT) yields state-of-the-art results in modeling unsupervised image-to-image translation by maximizing mutual information between input and output patches using only one encoder for both domains. In this paper, we propose a novel method based on contrastive learning and a dual learning setting (exploiting two encoders) to infer an efficient mapping between unpaired data. Additionally, while CUT suffers from mode collapse, a variant of our method efficiently addresses this issue. We further demonstrate the advantage of our approach through extensive ablation studies demonstrating superior performance comparing to recent approaches in multiple challenging image translation tasks. Lastly, we demonstrate that the gap between unsupervised methods and supervised methods can be efficiently closed.

关键词： computer vision conferences Training data pattern recognition Task analysis Mutual information

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NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results

NTIRE 2022 Challenge on Stereo Image Super-Resolution: Metho...

引用

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

作者： Wang, Longguang Guo, Yulan Wang, Yingqian Li, Juncheng Gu, Shuhang Timofte, Radu Chen, Liangyu Chu, Xiaojie Yu, Wenqing Jin, Kai Wei, Zeqiang Guo, Sha Yang, Angulia Zhou, Xiuzhuang Guo, Guodong Dai, Bin Peng, Feiyue Xiao, Huaxin Yan, Shen Liu, Yuxiang Cai, Hanxiao Cao, Pu Nie, Yang Yang, Lu Song, Qing Hu, Xiaotao Xu, Jun Xu, Mai Jing, Junpeng Deng, Xin Xing, Qunliang Qiao, Minglang Guan, Zhenyu Guo, Wenlong Peng, Chenxu Chen, Zan Chen, Junyang Li, Hao Chen, Junbin Li, Weijie Yang, Zhijing Li, Gen Li, Aijin Sun, Lei Zhang, Dafeng Liu, Shizhuo Zhang, Jiangtao Qu, Yanyun Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Liang, Qiaohui Lin, Jianxin Wang, Yijun Yin, Lianying Zhang, Rongju Zhao, Wei Xiao, Peng Xu, Rongjian Zhang, Zhilu Zuo, Wangmeng Guo, Hansheng Gao, Guangwei Zeng, Tieyong Pi, Huicheng Zhang, Shunli Kim, Joohyeok Kim, HyeonA Park, Eunpil Sim, Jae-Young Zhai, Jucai Zeng, Pengcheng Liu, Yang Ma, Chihao Huang, Yulin Chen, Junying Natl Univ Defense Technol Changsha Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Univ Sydney Sydney NSW 2006 Australia Univ Wurzburg Wurzburg Germany Swiss Fed Inst Technol Zurich Switzerland MEGVII Technol Beijing Peoples R China Peking Univ Beijing Peoples R China Bigo Technol Pte Ltd Singapore Singapore Beijing Univ Posts & Telecommun Smart Healthcare Innovat Lab Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Baidu Res Inst Deep Learning Beijing Peoples R China Natl Univ Defense Technol Coll Syst Engn Changsha Peoples R China Natl Univ Defense Technol Coll Liberal Arts & Sci Changsha Peoples R China Beijing Univ Posts & Telecommun Pattern Recognit & Intelligent Vis Lab Beijing Peoples R China Nankai Univ Coll Comp Sci Tianjin Peoples R China Nankai Univ Sch Stat & Data Sci Tianjin Peoples R China Beihang Univ Beijing Peoples R China Zhejiang Univ Technol Hangzhou Zhejiang Peoples R China Guangdong Univ Technol Guangzhou Guangdong Peoples R China Tencent OVBU Wuhu Peoples R China SRC B Beijing Peoples R China Xiamen Univ Xiamen Peoples R China Natl Taiwan Univ Dept Elect Engn Taipei Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei Taiwan Hunan Univ Coll Comp Sci & Elect Engn Changsha Hunan Peoples R China Harbin Inst Technol Harbin Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Nanjing Univ Posts & Telecommun Nanjing Peoples R China Ulsan Natl Inst Sci & Technol Dept Elect Engn Ulsan South Korea Ulsan Natl Inst Sci & Technol Grad Sch Artificial Intelligence Ulsan South Korea Beijing Jiaotong Univ Beijing Peoples R China City Univ Hong Kong Hong Kong Peoples R China South China Univ Technol Guangzhou Guangdong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new solutions and results. This challenge has 1 track aiming at the stereo image super-resolution problem under a standard bicubic degradation. In total, 238 participants were successfully registered, and 21 teams competed in the final testing phase. Among those participants, 20 teams successfully submitted results with PSNR (RGB) scores better than the baseline. This challenge establishes a new benchmark for stereo image SR.

关键词： Degradation computer vision conferences Superresolution Benchmark testing pattern recognition Image restoration

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Engineering Sketch Generation for computer-Aided Design

Engineering Sketch Generation for Computer-Aided Design

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

作者： Willis, Karl D. D. Jayaraman, Pradeep Kumar Lambourne, Joseph G. Chu, Hang Pu, Yewen Autodesk Res Shanghai Peoples R China

ISBN: (纸本)9781665448994

Engineering sketches form the 2D basis of parametric computer-Aided Design (CAD), the foremost modeling paradigm for manufactured objects. In this paper we tackle the problem of learning based engineering sketch generation as a first step towards synthesis and composition of parametric CAD models. We propose two generative models, CurveGen and TurtleGen, for engineering sketch generation. Both models generate curve primitives without the need for a sketch constraint solver and explicitly consider topology for downstream use with constraints and 3D CAD modeling operations. We find in our perceptual evaluation using human subjects that both CurveGen and TurtleGen produce more realistic engineering sketches when compared with the current state-of-the-art for engineering sketch generation.

关键词： Solid modeling Design automation Three-dimensional displays Computational modeling conferences Solids Topology

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