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

共 23198 条记录，以下是261-270 订阅

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Comparison of CoModGANs, LaMa and GLIDE for Art Inpainting Completing M.C Escher's Print Gallery

Comparison of CoModGANs, LaMa and GLIDE for Art Inpainting C...

引用

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

作者： Cipolina-Kun, Lucia Caenazzo, Simone Mazzei, Gaston ML Collect San Francisco CA 94117 USA Riskcare Ltd London England Univ Paris Saclay CNRS LISN VENISE Team Orsay France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Digital art restoration has benefited from inpainting models to correct the degradation or missing sections of a painting. This work compares three current state-of-the art models for inpainting of large missing regions. We provide qualitative and quantitative comparison of the performance by CoModGANs, LaMa and GLIDE in inpainting of blurry and missing sections of images. We use Escher's incomplete painting Print Gallery as our test study since it presents several of the challenges commonly present in restorative inpainting.

关键词： Degradation Image resolution Computational modeling conferences Digital art Image restoration pattern recognition

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DNAS:A Decoupled Global Neural Architecture Search Method

DNAS:A Decoupled Global Neural Architecture Search Method

引用

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

作者： Xu, Kepeng He, Gang Xidian Univ Xian Peoples R China Kuaishou Technol Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Neural Architecture Search (NAS) can automatically design model architecture with better performance. Current researchers have searched for local architecture similar to block, then stacked to construct entire models, or searched the entire model based on a manually designed benchmark module. There is no method to directly search the architecture of the global(entire) model at the operation level. The purpose of this article is to search the entire model directly in the operation level search space. We analyzed the search space of past methods which searching for local architectures, then a working mode for global model architecture search named CAM is proposed. Proposed CAM decouples the architectural parameters of the entire model which can complete the entire model architecture search with few architecture parameters. In the experiment, the test error 2.68 % in CIFAR-10 is obtained by the proposed method at the global architecture level, which can compare with the stage-of-art local architecture search methods.

关键词： Analytical models computer vision Search methods conferences computer architecture Benchmark testing pattern recognition

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Fast-n-Squeeze: towards real-time spectral reconstruction from RGB images

Fast-n-Squeeze: towards real-time spectral reconstruction fr...

引用

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

作者： Agarla, Mirko Bianco, Simone Buzzelli, Marco Celona, Luigi Schettini, Raimondo Univ Milano Bicocca Dept Informat Syst & Commun Viale Sarca 336Bldg U14 Milan Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present an efficient method for the reconstruction of multispectral information from RGB images, as part of the NTIRE 2022 Spectral Reconstruction Challenge. Given an input image, our method determines a global RGB-to-spectral linear transformation matrix, based on a search through optimal matrices from training images that share low-level features with the input. The resulting spectral signatures are then adjusted by a global scaling factor, determined through a lightweight SqueezeNet-inspired neural network. By combining the efficiency of linear transformation matrices with the data-driven effectiveness of convolutional neural networks, we are able to achieve superior performance than winners of the previous editions of the challenge.

关键词： Training Measurement Image resolution Neural networks Real-time systems pattern recognition Convolutional neural networks

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Reconstructing Spectral Images from RGB-Images using a Convolutional Neural Network 31

Reconstructing Spectral Images from RGB-Images using a Convo...

引用

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

作者： Stiebel, Tarek Koppers, Simon Seltsam, Philipp Merhof, Dorit Rhein Westfal TH Aachen Inst Imaging & Comp Vis Aachen Germany

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Recovering high-dimensional spectral images taken with spectrally low-dimensional camera systems, in the extreme case RGB-images, has been of great interest for a variety of applications. An accurate spectral reconstruction is typically required to either achieve a better color accuracy or to improve object recognition/classification tasks. Almost all published work to date aims at performing a mapping from individual camera signals towards the corresponding spectrum. However, it might be beneficial to consider not only single pixels, but also contextual information. Here, we propose a convolutional neural network architecture that learns a mapping from RGB- to spectral images. We trained the network on the largest hyper-spectral data set available to date [;'I and analyzed the influence of different error metrics as loss functions. An objective evaluation of the performance in comparison to state of the art spectral reconstruction techniques is given by participating in the NTIRE 2018 challenge on spectral reconstruction [1].

关键词： computer vision pattern recognition conferences

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Stargazer: A Transformer-based Driver Action Detection System for Intelligent Transportation

Stargazer: A Transformer-based Driver Action Detection Syste...

引用

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

作者： Liang, Junwei Zhu, He Zhang, Enwei Zhang, Jun Tencent Youtu Lab Shenzhen Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Distracted driver actions can be dangerous and cause severe accidents. Thus, it is important to detect and eliminate distracted driving behaviors on the road to save lives. To this end, we study driver action detection using videos captured inside the vehicle. We propose Stargazer, an efficient, transformer-based system exploiting rich temporal features about the human behavioral information, with a simple yet effective action temporal localization framework. The core of our system contains an improved version of the multi-scale vision transformer network, which learns a hierarchy of robust representations. We then use a slidingwindow classification strategy to facilitate temporal localization of actions-of-interest. The proposed system wins the second place in the Naturalistic Driving Action recognition of AI City Challenge 2022 (Track 3)(1). The code and models are released(2).

关键词： Location awareness Roads Urban areas Transformer cores Transformers pattern recognition Behavioral sciences

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SAM: Pushing the Limits of Saliency Prediction Models 31

SAM: Pushing the Limits of Saliency Prediction Models

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

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

引用

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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Parallel Generative Adversarial Network for Third-person to First-person Image Generation

Parallel Generative Adversarial Network for Third-person to ...

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

作者： Liu, Gaowen Latapie, Hugo Kilic, Ozkan Lawrence, Adam Cisco Res Emerging Technol & Incubat San Jose CA 95134 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Cross-view image generation has been recently proposed to generate images of one view from another dramatically different view. In this paper, we investigate third-person (exocentric) view to first-person (egocentric) view image generation. This is a challenging task since egocentric view sometimes is remarkably different from exocentric view. Thus, transforming the appearances across the two views is a non-trivial task. To this end, we propose a novel Parallel Generative Adversarial Network (P-GAN) with a novel cross-cycle loss to learn the shared information for generating egocentric images from exocentric view. We also incorporate a novel contextual feature loss in the learning procedure to capture the contextual information in images. Extensive experiments on the Exo-Ego datasets [5] show that our model outperforms the state-of-the-art approaches.

关键词： computer vision Image synthesis conferences Computational modeling Generative adversarial networks pattern recognition Task analysis

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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...

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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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PaintInStyle: One-Shot Discovery of Interpretable Directions by Painting

PaintInStyle: One-Shot Discovery of Interpretable Directions...

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

作者： Doner, Berkay Balcioglu, Elif Sema Barin, Merve Rabia Kocasari, Umut Tiftikci, Mert Yanardag, Pinar Bogazici Univ Istanbul Turkey

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The search for interpretable directions in latent spaces of pre-trained Generative Adversarial Networks (GANs) has become a topic of interest. These directions can be utilized to perform semantic manipulations on the GAN generated images. The discovery of such directions is performed either in a supervised way, which requires manual annotation or pre-trained classifiers, or in an unsupervised way, which requires the user to interpret what these directions represent. In this work, we propose a framework that finds a specific manipulation direction using only a single simple sketch drawn on an image. Our method finds directions consisting of channels in the style space of the StyleGAN2 architecture responsible for the desired edits and performs image manipulations comparable with state-of-the-art methods.

关键词： computer vision Annotations conferences Semantics Manuals computer architecture Generative adversarial networks

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