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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

共 6678 条记录，以下是1241-1250 订阅

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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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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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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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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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DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images 31

DeepGlobe 2018: A Challenge to Parse the Earth through Satel...

引用

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

作者： Demir, Ilke Koperski, Krzysztof Lindenbaum, David Pang, Guan Huang, Jing Bast, Saikat Hughes, Forest Tuia, Devis Raskar, Ramesh Facebook Menlo Pk CA 94025 USA DigitalGlobe Westminster CO USA CosmiQ Works Burnaby BC Canada Wageningen Univ Wageningen Netherlands MIT Media Lab Cambridge MA 02139 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We present the DeepGlobe 2018 Satellite Image Understanding Challenge, which includes three public competitions for segmentation, detection, and classification tasks on satellite images (Figure 1). Similar to other challenges in computer vision domain such as DAVIS[21] and COCO[33], DeepGlobe proposes three datasets and corresponding evaluation methodologies, coherently bundled in three competitions with a dedicated workshop co-located with cvpr 2018. We observed that satellite imagery is a rich and structured source of information, yet it is less investigated than everyday images by computer vision researchers. However, bridging modern computer vision with remote sensing data analysis could have critical impact to the way we understand our environment and lead to major breakthroughs in global urban planning or climate change research. Keeping such bridging objective in mind, DeepGlobe aims to bring together researchers from different domains to raise awareness of remote sensing in the computer vision community and vice-versa. We aim to improve and evaluate state-of-the-art satellite image understanding approaches, which can hopefully serve as reference benchmarks for future research in the same topic. In this paper, we analyze characteristics of each dataset, define the evaluation criteria of the competitions, and provide baselines for each task.

关键词： Satellites Roads Buildings Task analysis Urban areas computer vision Image segmentation

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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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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...

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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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VGAN-Based Image Representation Learning for Privacy-Preserving Facial Expression recognition 31

VGAN-Based Image Representation Learning for Privacy-Preserv...

引用

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

作者： Chen, Jiawei Konrad, Janusz Ishwar, Prakash Boston Univ Boston MA 02215 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Reliable facial expression recognition plays a critical role in human-machine interactions. However, most of the facial expression analysis methodologies proposed to date pay little or no attention to the protection of a user's privacy. In this paper, we propose a Privacy-Preserving Representation-Learning Variational Generative Adversarial Network (PPRL-VGAN) to learn an image representation that is explicitly disentangled from the identity information. At the same time, this representation is discriminative from the standpoint of facial expression recognition and generative as it allows expression-equivalent face image synthesis. We evaluate the proposed model on two public datasets under various threat scenarios. Quantitative and qualitative results demonstrate that our approach strikes a balance between the preservation of privacy and data utility. We further demonstrate that our model can be effectively applied to other tasks such as expression morphing and image completion.

关键词： Face Face recognition Gallium nitride Privacy Visualization Image recognition Generative adversarial networks

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Feature Pyramid Network for Multi-Class Land Segmentation 31

Feature Pyramid Network for Multi-Class Land Segmentation

引用

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

作者： Seferbekov, Selim Iglovikov, Vladimir Buslaev, Alexander Shvets, Alexey Veeva Syst D-60314 Frankfurt Germany Lyft Inc Engn Ctr Level5 Palo Alto CA 94304 USA Mapbox R&D Ctr Minsk 220030 BELARUS MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Semantic segmentation is in-demand in satellite imagery processing. Because of the complex environment, automatic categorization and segmentation of land cover is a challenging problem. Solving it can help to overcome many obstacles in urban planning, environmental engineering or natural landscape monitoring. In this paper, we propose an approach for automatic multi-class land segmentation based on a fully convolutional neural network of feature pyramid network (FPN) family. This network is consisted of pre-trained on ImageNet Resnet50 encoder and neatly developed decoder. Based on validation results, leader-board score and our own experience this network shows reliable results for the DEEPGLOBE - cvpr 2018 land cover classification sub-challenge. Moreover, this network moderately uses memory that allows using GTX 1080 or 1080 TI video cards to perform whole training and makes pretty fast predictions.

关键词： Image segmentation Satellites Training Feature extraction Convolution Semantics computer vision

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Large-Scale Ecological Analyses of Animals in the Wild using computer vision 31

Large-Scale Ecological Analyses of Animals in the Wild using...

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

作者： Timm, Mikayla Maji, Subhransu Fuller, Todd Univ Massachusetts Amherst MA 01003 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Camera traps are increasingly being deployed by ecologists and citizen-scientists as a cost-effective way of obtaining large amounts of animal images in the wild. In order to analyze this data, the images are labeled manually by ecologists, where they identify species of animals and more fine-grained details, such as animal sex or age, or even individual animal identities. However, with the number of camera trap images quickly outgrowing the capacity of the labelers, ecologists are unable to keep up with the wealth of data they are obtaining. Using computer vision, we can automatically generate labels for new camera trap images at the rate that they are being obtained, allowing ecologists to uncover ecological and biological information at a scale previously not possible. In this paper, we explore computer vision approaches for species identification in camera trap images and for individual jaguar identification, both of which show promising results. We make this novel dataset publicly available for future research directions and further exploration.

关键词： Animals Cameras Training computer vision Biological system modeling Task analysis Semantics

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