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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是3131-3140 订阅

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A Holistic Framework for Addressing the World using Machine Learning 31

A Holistic Framework for Addressing the World using Machine ...

引用

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

作者： Demir, Ilke Hughes, Forest Raj, Aman Dhruv, Kaunil Raskar, Ramesh Muddala, Suryanarayana Murthy Garg, Sanyam Doo, Barrett Facebook Menlo Pk CA 94025 USA MIT Media Lab Cambridge MA 02139 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Millions of people are disconnected from basic services due to lack of adequate addressing. We propose an automatic generative algorithm to create street addresses from satellite imagery. Our addressing scheme is coherent with the street topology, linear and hierarchical to follow human perception, and universal to be used as a unified geocoding system. Our algorithm starts with extracting road segments using deep learning and partitions the road network into regions. Then regions, streets, and address cells are named using proximity computations. We also extend our addressing scheme to cover inaccessible areas, to be flexible for changes, and to lead as a pioneer for a unified geodatabase.

关键词： Roads Urban areas Satellites Image segmentation computer vision conferences Topology

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Cross-domain fashion image retrieval 31

Cross-domain fashion image retrieval

引用

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

作者： Gajic, Bojana Baldrich, Ramon Univ Autonoma Barcelona Comp Vis Ctr Edifici O UAB Bellaterra Spain

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Cross domain image retrieval is a challenging task that implies matching images from one domain to their pairs from another domain. In this paper we focus on fashion image retrieval, which involves matching an image of a fashion item taken by users, to the images of the same item taken in controlled condition, usually by professional photographer. When facing this problem, we have different products in train and test time, and we use triplet loss to train the network. We stress the importance of proper training of simple architecture, as well as adapting general models to the specific task.

关键词： Task analysis Streaming media computer vision Image retrieval Biological system modeling conferences Training

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Notice of Removal: Robust Image Colorization Using Self Attention Based Progressive Generative Adversarial Network

Notice of Removal: Robust Image Colorization Using Self Atte...

引用

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

作者： Manoj Sharma Megh Makwana Avinash Upadhyay Ajay Pratap Singh Anuj Badhwar Akkshita Trivedi Anil Saini Santanu Chaudhury CSIR-CEERI Pilani CCS Computer Pvt Ltd A and N Telecom Circle BSNL DTU Delhi IIT Jodhpur

Removed.

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Removed.

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New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution 31

New Techniques for Preserving Global Structure and Denoising...

引用

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

作者： Bei, Yijie Damian, Alex Hu, Shijia Menon, Sachit Ravi, Nikhil Rudin, Cynthia Duke Univ Durham NC 27706 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This work identifies and addresses two important technical challenges in single-image super-resolution: (1) how to upsample an image without magnifying noise and (2) how to preserve large scale structure when upsampling. We summarize the techniques we developed for our second place entry in Track 1 (Bicubic Downsampling), seventh place entry in Track 2 (Realistic Adverse Conditions), and seventh place entry in Track 3 (Realistic difficult) in the 2018 NTIRE Super-Resolution Challenge. Furthermore, we present new neural network architectures that specifically address the two challenges listed above: denoising and preservation of large-scale structure.

关键词： Image resolution Noise reduction Training Task analysis Neural networks computer architecture Knowledge engineering

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Active vision Dataset Benchmark 31

Active Vision Dataset Benchmark

引用

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

作者： Ammirato, Phil Berg, Alexander C. Kosecka, Jana Univ N Carolina Chapel Hill NC 27514 USA George Mason Univ Fairfax VA 22030 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Several recent efforts in computer vision indicate a trend toward studying and understanding problems in larger scale environments, beyond single images, and focus on connections to tasks in navigation, mobile manipulation, and visual question answering. A common goal of these tasks is the capability of moving in the environment, acquiring novel views during perception and while performing a task. This capability comes easily in synthetic environments, however achieving the same effect with real images is much more laborious. We propose using the existing Active vision Dataset to form a benchmark for such problems in a real-world settings with real images. The dataset is well suited for evaluating tasks of multiview active recognition, target driven navigation, and target search, and also can be effective for studying the transfer of strategies learned in simulation to real settings.

关键词： Task analysis Training Visualization Robots Benchmark testing Navigation

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Motion Fused Frames: Data Level Fusion Strategy for Hand Gesture recognition 31

Motion Fused Frames: Data Level Fusion Strategy for Hand Ges...

引用

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

作者： Kopuklu, Okan Kose, Neslihan Rigoll, Gerhard Tech Univ Munich Inst Human Machine Commun Munich Germany

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Acquiring spatio-temporal states of an action is the most crucial step for action classification. In this paper, we propose a data level fusion strategy, Motion Fused Frames (MFFs), designed to fuse motion information into static images as better representatives of spatio-temporal states of an action. MFFs can be used as input to any deep learning architecture with very little modification on the network. We evaluate MFFs on hand gesture recognition tasks using three video datasets -Jester, ChaLearn LAP IsoGD and NVIDIA Dynamic Hand Gesture Datasets - which require capturing long-term temporal relations of hand movements. Our approach obtains very competitive performance on Jester and ChaLearn benchmarks with the classification accuracies of 96.28% and 57.4%, respectively, while achieving state-of-the-art performance with 84.7% accuracy on NVIDIA benchmark.

关键词： Optical imaging Feature extraction Gesture recognition Motion segmentation Optical sensors computer architecture Image color analysis

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Towards CNN map representation and compression for camera relocalisation 31

Towards CNN map representation and compression for camera re...

引用

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

作者： Contreras, Luis Mayol-Cuevas, Walterio Univ Bristol Dept Comp Sci Bristol Avon England

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This paper presents a study on the use of Convolutional Neural Networks for camera relocalisation and its application to map compression. We follow state of the art visual relocalisation results and evaluate the response to different data inputs. We use a CNN map representation and introduce the notion of map compression under this paradigm by using smaller CNN architectures without sacrificing relocalisation performance. We evaluate this approach in a series of publicly available datasets over a number of CNN architectures with different sizes, both in complexity and number of layers. This formulation allows us to improve relocalisation accuracy by increasing the number of training trajectories while maintaining a constant-size CNN.

关键词： Training Task analysis Cameras computer architecture Trajectory Biological neural networks

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WiCV 2018: The Fourth Women In computer vision Workshop 31

WiCV 2018: The Fourth Women In Computer Vision Workshop

引用

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

作者： Demir, Ilke Bazazian, Dena Romero, Adriana Sharmanska, Viktoriia Tchapmi, Lyne P. Facebook Menlo Pk CA 94025 USA Comp Vis Ctr Barcelona Spain Facebook AI Res Montreal PQ Canada Imperial Coll London London England Stanford Univ Stanford CA 94305 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We present WiCV 2018 - Women in computer vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration, and to provide mentorship and give opportunities to female-identifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations.

关键词： conferences computer vision Industries Object recognition Engineering profession Collaboration Machine learning

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SAM: Pushing the Limits of Saliency Prediction Models 31

SAM: Pushing the Limits of Saliency Prediction Models

引用

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

作者： Cornia, Marcella Baraldi, Lorenzo Serra, Giuseppe Cmcidara, Rita Univ Modena & Reggio Emilia Modena Italy Univ Udine Udine Italy

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The prediction of human eye fixations has been recently gaining a lot of attention thanks to the improvements shown by deep architectures. In our work, we go beyond classical feed-forward networks to predict saliency maps and propose a Saliency Attentive Model which incorporates neural attention mechanisms to iteratively refine predictions. Experiments demonstrate that the proposed strategy overcomes by a considerable margin the state of the art on the largest dataset available for saliency prediction. Here, we provide experimental results on other popular saliency datasets to confirm the effectiveness and the generalization capabilities of our model, which enable us to reach the state of the art on all considered datasets.

关键词： Predictive models Visualization Measurement Feature extraction conferences computer architecture Computational modeling

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Encapsulating the impact of transfer learning, domain knowledge and training strategies in deep-learning based architecture: A biometric based case study 31

Encapsulating the impact of transfer learning, domain knowle...

引用

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

作者： Singh, Avantika Nigam, Aditya Indian Inst Technol Mandi Mandi India

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, efforts have been made to analyze the impact of training strategies, transfer learning and domain knowledge on two biometric-based problems namely: three class oculus classification and fingerprint sensor classification. For analyzing these problems we have considered deep-learning based architecture and evaluated our results on benchmark contact-lens datasets like IIIT-D, ND, IIT-K ( our model is publicly available) and on fingerprint datasets like FVC-2002, FVC-2004, FVC-2006, IIITD-MOLF, IIT-K. In-depth feature analysis of various proposed deep-learning models has been done in order to infer that indeed training in different ways along with transfer learning and domain knowledge plays a vital role in deciding the learning ability of any network.

关键词： Lenses Iris recognition Training Biological system modeling Knowledge engineering pattern recognition Analytical models

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