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检索条件"任意字段=2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006"

共 6858 条记录，以下是231-240 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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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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Adaptive Bitrate Quantization Scheme Without Codebook for Learned Image Compression

Adaptive Bitrate Quantization Scheme Without Codebook for Le...

引用

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

作者： Loehdefink, Jonas Sitzmann, Jonas Baer, Andreas Fingscheidt, Tim Tech Univ Carolo Wilhelmina Braunschweig Braunschweig Germany

ISBN: (纸本)9781665487399

We propose a generic approach to quantization without codebook in learned image compression called onehot max (OHM, O) quantization. It reorganizes the feature space resulting in an additional dimension, along which vector quantization yields one-hot vectors by comparing activations. Furthermore, we show how to integrate Omega quantization into a compression system with bitrate adaptation, i.e., full control over bitrate during inference. We perform experiments on both MNIST and Kodak and report on rate-distortion trade-offs comparing with the integer rounding reference. For low bitrates (< 0.4 bpp), our proposed quantizer yields better performance while exhibiting also other advantageous training and inference properties. Code is available at https://***/ifnspaml/OHMQ.

关键词： Training computer vision Image coding Codes Vector quantization conferences Bit rate

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AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation

AutoGPart: Intermediate Supervision Search for Generalizable...

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

作者： Liu, Xueyi Xu, Xiaomeng Rao, Anyi Gan, Chuang Yi, Li Tsinghua Univ Beijing Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China MIT IBM Watson AI Lab Cambridge MA USA Shanghai Qi Zhi Inst Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Training a generalizable 3D part segmentation network is quite challenging but of great importance in real-world applications. To tackle this problem, some works design task-specific solutions by translating human understanding of the task to machine's learning process, which faces the risk of missing the optimal strategy since machines do not necessarily understand in the exact human way. Others try to use conventional task-agnostic approaches designed for domain generalization problems with no task prior knowledge considered. To solve the above issues, we propose AutoGPart, a generic method enabling training generalizable 3D part segmentation networks with the task prior considered. AutoGPart builds a supervision space with geometric prior knowledge encoded, and lets the machine to search for the optimal supervisions from the space for a specific segmentation task automatically. Extensive experiments on three generalizable 3D part segmentation tasks are conducted to demonstrate the effectiveness and versatility of AutoGPart. We demonstrate that the performance of segmentation networks using simple backbones can be significantly improved when trained with supervisions searched by our method.

关键词： Training computer vision Three-dimensional displays Machine learning pattern recognition Task analysis Faces

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Self Supervised Scanpath Prediction Framework for Painting Images

Self Supervised Scanpath Prediction Framework for Painting I...

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

作者： Tliba, Marouane Kerkouri, Mohamed Amine Chetouani, Aladine Bruno, Alessandro Univ Orleans Orleans France Bournemouth Univ Poole Dorset England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In our paper, we propose a novel strategy to learn distortion invariant latent representation from painting pictures for visual attention modelling downstream task. In further detail, we design an unsupervised framework that jointly maximises the mutual information over different painting styles. To show the effectiveness of our approach, we firstly propose a lightweight scanpath baseline model and compare its performance to some state-of-the-art methods. Secondly, we train the encoder of our baseline model on large-scale painting images to study the efficiency of the proposed self-supervised strategy. The lightweight decoder proves effective in learning from the self-supervised pre-trained encoder with better performances than the end-to-end fine-tuned supervised baseline on two painting datasets, including a proposed new visual attention modelling dataset.

关键词： Visualization Self-supervised learning Predictive models Observers Quality assessment pattern recognition Task analysis

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RDONet: Rate-Distortion Optimized Learned Image Compression with Variable Depth

RDONet: Rate-Distortion Optimized Learned Image Compression ...

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

作者： Brand, Fabian Fischer, Kristian Kopte, Alexander Windsheimer, Marc Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg Multimedia Commun & Signal Proc Cauerstr 7 D-91058 Erlangen Germany

ISBN: (纸本)9781665487399

Rate-distortion optimization (RDO) is responsible for large gains in image and video compression. While RDO is a standard tool in traditional image and video coding, it is not yet widely used in novel end-to-end trained neural methods. The major reason is that the decoding function is trained once and does not have free parameters. In this paper, we present RDONet, a network containing state-of-the-art components, which is perceptually optimized and capable of rate-distortion optimization. With this network, we are able to outperform VVC Intra on MS-SSIM and two different perceptual LPIPS metrics. This paper is part of the CLIC challenge, where we participate under the team name RDONet FAU.

关键词： Measurement Video coding Training Image coding Rate-distortion Video compression pattern recognition

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Boundary-aware Image Inpainting with Multiple Auxiliary Cues

Boundary-aware Image Inpainting with Multiple Auxiliary Cues

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

作者： Yamashita, Yohei Shimosato, Kodai Ukita, Norimichi Toyota Technol Inst Nagoya Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Image inpainting (a.k.a. image completion) allows us to remove unexpected foreground objects from an observed image and to restore the removed region with background pixels. The performance of image inpainting is improved by auxiliary cues such as edge boundaries and segmentation regions. As a new auxiliary cue, this paper focuses on a depth image that is estimated from an input RGB image by monocular depth estimation. In the depth image, boundaries between different objects (e.g., objects located in different distances) with similar pixel values might be available, while those boundaries are difficult to be detected by edge detection and segmentation. Our proposed method employs those boundaries in the edge and depth images as auxiliary cues. Experiments demonstrate that our proposed method augmented by the depth image outperforms its baseline quantitatively (i.e., 1.17dB and 0.74dB PSNR gains on the Paris-StreetView and Places datasets, respectively) and qualitatively.

关键词： Image segmentation computer vision Image edge detection conferences Estimation pattern recognition Image restoration

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Towards efficient feature sharing in MIMO architectures

Towards efficient feature sharing in MIMO architectures

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

作者： Sun, Remy Rame, Alexandre Masson, Clement Thome, Nicolas Cord, Matthieu Sorbonne Univ MLIA ISIR Paris France Conservatoire Natl Arts & Metiers CEDRIC Vertigo Paris France Thales Land & Air Syst Elancourt France Valeo Ai Paris France

ISBN: (纸本)9781665487399

Multi-input multi-output architectures propose to train multiple subnetworks within one base network and then average the subnetwork predictions to benefit from ensembling for free. Despite some relative success, these architectures are wasteful in their use of parameters. Indeed, we highlight in this paper that the learned subnetwork fail to share even generic features which limits their applicability on smaller mobile and AR/VR devices. We posit this behavior stems from an ill-posed part of the multi-input multi-output framework. To solve this issue, we propose a novel unmixing step in MIMO architectures that allows subnetworks to properly share features. Preliminary experiments on CIFAR 100 show our adjustments allow feature sharing and improve model performance for small architectures.

关键词： Performance evaluation computer vision Impedance matching conferences Neural networks computer architecture pattern recognition

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Residual Feature Pyramid Network for Enhancement of Vascular patterns

Residual Feature Pyramid Network for Enhancement of Vascular...

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

作者： Kotwal, Ketan Marcel, Sebastien Idiap Res Inst Martigny Switzerland Univ Lausanne Lausanne Switzerland

ISBN: (纸本)9781665487399

The accuracy of finger vein recognition systems gets degraded due to low and uneven contrast between veins and surroundings, often resulting in poor detection of vein patterns. We propose a finger-vein enhancement technique, ResFPN (Residual Feature Pyramid Network), as a generic preprocessing method agnostic to the recognition pipeline. A bottom-up pyramidal architecture using the novel Structure Detection block (SDBlock) facilitates extraction of veins of varied widths. Using a feature aggregation module (FAM), we combine these vein-structures, and train the proposed ResFPN for detection of veins across scales. With enhanced presentations, our experiments indicate a reduction upto 5% in the average recognition errors for commonly used recognition pipeline over two publicly available datasets. These improvements are persistent even in cross-dataset scenario where the dataset used to train the ResFPN is different from the one used for recognition.

关键词： Histograms Protocols Veins Pipelines Fingers computer architecture Network architecture

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Self-Supervised Variable Rate Image Compression using Visual Attention

Self-Supervised Variable Rate Image Compression using Visual...

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

作者： Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Space Applicat Ctr Signal & Image Proc Grp Ahmadabad Gujarat India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The recent success of self-supervised learning relies on its ability to learn the representations from self-defined pseudo-labels that are applied to several downstream tasks. Motivated by this ability, we present a deep image compression technique, which learns the lossy reconstruction of raw images from the self-supervised learned representation of SimCLR ResNet-50 architecture. Our framework uses a feature pyramid to achieve the variable rate compression of the image using a self-attention map for the optimal allocation of bits. The paper provides an overview to observe the effects of contrastive self-supervised representations and the self-attention map on the distortion and perceptual quality of the reconstructed image. The experiments are performed on a different class of images to show that the proposed method outperforms the other variable rate deep compression models without compromising the perceptual quality of the images.

关键词： Visualization Image coding Image analysis conferences Self-supervised learning Distortion pattern recognition

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