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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"

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Real Photographs Denoising with Noise Domain Adaptation and Attentive Generative Adversarial Network

Real Photographs Denoising with Noise Domain Adaptation and ...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Kai Lin Thomas H. Li Shan Liu Ge Li School of Electronic and Computer Engineering Peking University Tencent America

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Nowadays, deep convolutional neural networks (CNNs) based methods have achieved favorable performance in synthetic noisy image denoising, but they are very limited in real photographs denoising since it's hard to obtain ground truth clean image to generate paired training data. Besides, the existing training datasets for real photographs denoising are too small. To solve this problem, we construct a new dataset and obtain corresponding ground truth by averaging, and then extend them through noise domain adaptation. Furthermore, we propose a attentive generative network by injecting visual attention into the generative network. During the training, visual attention map learns noise regions. The generative network will pay more attention to noise regions, which contributes to balancing between noise removal and texture preservation. Extensive experiments show that our method outperforms several state-of-the-art methods quantitatively and qualitatively.

关键词： Noise reduction ground truth Photographs Noise cancellation Noise averaging Visual Attention Real number

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Online Reconstruction of Indoor Scenes with Local Manhattan Frame Growing

Online Reconstruction of Indoor Scenes with Local Manhattan ...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Mahdi Yazdanpour Guoliang Fan Weihua Sheng School of Electrical and Computer Engineering Oklahoma State University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

We propose an efficient approach for robust reconstruction of indoor scenes by taking advantage of the geometric relation between consecutive Manhattan keyframes and local pose refinement to improve the accuracy and fidelity of the reconstructed models. At the core of our framework, we have a Local Manhattan Frame Growing system, which finds the principal directions of the scene and aligns point clouds with the dominant plane, and a Local Pose Optimization, which refines the pose estimation for a specific range of frames. During the reconstruction process, we use Manhattan keyframes for a planar pre-alignment to provide a robust initialization for the final surface registration. All Manhattan keyframes are integrated using a frame-to-model scheme to create local models based on the refined camera poses. The final dense model is reconstructed by adopting a geometric registration between local segments and integrating them into a global frame. The experimental results show the effectiveness of our approach to reduce the cumulative registration error and overall geometric drift.

关键词： Manhattan Frame FRAMES measuring datum error Reconstructive Surgical Procedures Fidelity Clouds

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ComboGAN: Unrestrained Scalability for Image Domain Translation 31

ComboGAN: Unrestrained Scalability for Image Domain Translat...

引用

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

作者： Anoosheh, Asha Agustsson, Eirikur Timofte, Radu Van Gool, Luc Swiss Fed Inst Technol D ITET Comp Vis Lab Zurich Switzerland Swiss Fed Inst Technol D ITET Zurich Switzerland Merantix Berlin Germany Katholieke Univ Leuven ESAT Leuven Belgium

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The past year alone has seen unprecedented leaps in the area of learning-based image translation, namely CycleGAN, by Zhu et al. But experiments so far have been tailored to merely two domains at a time, and scaling them to more would require an quadratic number of models to be trained. And with two-domain models taking days to train on current hardware, the number of domains quickly becomes limited by the time and resources required to process them. In this paper, we propose a multi-component image translation model and training scheme which scales linearly - both in resource consumption and time required - with the number of domains. We demonstrate its capabilities on a dataset of paintings by 14 different artists and on images of the four different seasons in the Alps. Note that 14 data groups would need ( 14 choose 2) = 91 different CycleGAN models: a total of 182 generator/discriminator pairs;whereas our model requires only 14 generator/discriminator pairs.

关键词： Training Generators Gallium nitride Decoding computer vision Task analysis Data models

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FRESCO: Fast Radiometric Egocentric Screen Compensation

FRESCO: Fast Radiometric Egocentric Screen Compensation

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Matthew Post Paul Fieguth Mohamed A. Naiel Zohreh Azimifar Mark Lamm Vision and Image Processing Lab University of Waterloo Shiraz University Christie Digital Systems Canada Inc

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Existing radiometric compensation methods for projector-camera systems have been shown to produce compensated colours which are inconsistent to a human viewer. In this paper, a novel radiometric compensation method for projector-camera systems and textured surfaces is introduced based on the human visual system (HVS) colour response. The proposed method can extend established compensation methods to produce colours which are human-perceived to be correct (egocentric modelling). As a result, this method performs radiometric compensation which is not only consistent and precise, but also produces images which are visually accurate to an external colour reference. This method is shown to produce generally the lowest average radiometric compensation error when compared to compensation performed using only the response of a camera, demonstrated through quantitative analysis of compensated colours, and supported by qualitative results.

关键词： compensation method Radiation compensated Color Error compensation Human Cameras QUANTITATIVE ANALYSIS Inconsistent

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Text Image Super-resolution by Image Matting and Text Label Supervision

Text Image Super-resolution by Image Matting and Text Label ...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Kai Lin Yubao Liu Thomas H. Li Shan Liu Ge Li School of Electronic and Computer Engineering Peking University Tencent America

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

These days, many methods have been proposed to deal with nature image super-resolution (SR) and get impressive performance. However, these methods don't do well in text images SR due to their ignorance of the difference between nature images and text images. In this paper, we propose a matting-based dual generative adversarial network (mdGAN) for text image SR. Firstly, the input image is decomposed into text, foreground and background layers using deep image matting. Then two parallel branches are constructed to recover text boundary information and color information respectively. Furthermore, in order to improve the restoration accuracy of characters in output image, we use the input image's corresponding ground truth text label as extra supervise information to refine the two-branch networks during training. Experiments on real text images demonstrate that our method outperforms several state-of-the-art methods quantitatively and qualitatively.

关键词： texts images Image Imagery (Psychotherapy) chromatic information super-resolution

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Robust Aleatoric Modeling for Future Vehicle Localization

Robust Aleatoric Modeling for Future Vehicle Localization

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ieee/cvf conference on computer vision and pattern recognition workshops

作者： Max Hudnell True Price Jan-Michael Frahm University of North Carolina at Chapel Hill

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

The task of 2D object localization prediction, or the estimation of an object's future location and scale in an image, is a developing area of computer vision research. An accurate prediction of an object's future localization has the potential for drastically improving critical decision making systems. In particular, an autonomous driving system's collision prevention system could make better-informed decisions in the presence of accurate localization predictions for nearby objects (i.e. cars, pedestrians, and hazardous obstacles). Improving the accuracy of such localization systems is crucial to passenger/pedestrian safety. This paper presents a novel technique for determining future bounding boxes, representing the size and location of objects - and the predictive uncertainty of both aspects - in a transit setting. We present a simple feed-forward network for robust prediction as a solution of this task, which is able to generate object locality proposals by making use of an object's previous locality information. We evaluate our method against a number of related approaches and demonstrate its benefits for vehicle localization, and different from previous works, we propose to use distribution-based metrics to truly measure the predictive efficiency of the network-regressed uncertainty models.

关键词： images computer vision vehicle

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Image Caption Generation with Hierarchical Contextual Visual Spatial Attention 31

Image Caption Generation with Hierarchical Contextual Visual...

引用

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

作者： Khademi, Mahmoud Schulte, Oliver Simon Fraser Univ Burnaby BC Canada

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We present a novel context-aware attention-based deep architecture for image caption generation. Our architecture employs a Bidirectional Grid LSTM, which takes visual features of an image as input and learns complex spatial patterns based on two-dimensional context, by selecting or ignoring its input. The Grid LSTM has not been applied to image caption generation task before. Another novel aspect is that we leverage a set of local region-grounded texts obtained by transfer learning. The region-grounded texts often describe the properties of the objects and their relationships in an image. To generate a global caption for the image, we integrate the spatial features from the Grid LSTM with the local region-grounded texts, using a two-layer Bidirectional LSTM. The first layer models the global scene context such as object presence. The second layer utilizes a novel dynamic spatial attention mechanism, based on another Grid LSTM, to generate the global caption word-by-word, while considering the caption context around a word in both directions. Unlike recent models that use a soft attention mechanism, our dynamic spatial attention mechanism considers the spatial context of the image regions. Experimental results on MS-COCO dataset show that our architecture outperforms the state-of-the-art.

关键词： Feature extraction Visualization Logic gates computer architecture Task analysis Context modeling Computational modeling

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A Fully Progressive Approach to Single-Image Super-Resolution 31

A Fully Progressive Approach to Single-Image Super-Resolutio...

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

作者： Wang, Yifan Perazzi, Federico McWilliams, Brian Sorkine-Hornung, Alexander Sorkine-Hornung, Olga Schroers, Christopher Swiss Fed Inst Technol Zurich Switzerland Disney Res Zurich Switzerland Oculus Menlo Pk CA USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality. However, in each case it remains challenging to achieve high quality results for large upsampling factors. To this end, we propose a method (ProSR) that is progressive both in architecture and training: the network upsamples an image in intermediate steps, while the learning process is organized from easy to hard, as is done in curriculum learning. To obtain more photorealistic results, we design a generative adversarial network (GAN), named ProGanSR, that follows the same progressive multi-scale design principle. This not only allows to scale well to high upsampling factors (e.g., 8x) but constitutes a principled multi-scale approach that increases the reconstruction quality for all upsampling factors simultaneously. In particular ProSR ranks 2nd in terms of SSIM and 4th in terms of PSNR in the NTIRE2018 SISR challenge [35]. Compared to the top-ranking team, our model is marginally lower, but runs 5 times faster.

关键词： Training Gallium nitride Image resolution Image reconstruction Generative adversarial networks computer architecture Generators

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Pseudo-labels for Supervised Learning on Dynamic vision Sensor Data, Applied to Object Detection under Ego-motion 31

Pseudo-labels for Supervised Learning on Dynamic Vision Sens...

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

作者： Chen, Nicholas F. Y. DSO Natl Labs 12 Sci Pk Dr Singapore 118225 Singapore

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In recent years, dynamic vision sensors (DVS), also known as event-based cameras or neuromorphic sensors, have seen increased use due to various advantages over conventional frame-based cameras. Using principles inspired by the retina, its high temporal resolution overcomes motion blurring, its high dynamic range overcomes extreme illumination conditions and its low power consumption makes it ideal for embedded systems on platforms such as drones and self-driving cars. However, event-based data sets are scarce and labels are even rarer for tasks such as object detection. We transferred discriminative knowledge from a state-of-the-art frame-based convolutional neural network (CNN) to the event-based modality via intermediate pseudo-labels, which are used as targets for supervised learning. We show, for the first time, event-based car detection under ego-motion in a real environment at 100 frames per second with a test average precision of 40.3% relative to our annotated ground truth. The event-based car detector handles motion blur and poor illumination conditions despite not explicitly trained to do so, and even complements frame-based CNN detectors, suggesting that it has learnt generalized visual representations.

关键词： vision sensors Cameras Vehicle dynamics Object detection Voltage control Supervised learning Automobiles

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Scene Understanding Networks for Autonomous Driving based on Around View Monitoring System 31

Scene Understanding Networks for Autonomous Driving based on...

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

作者： Baek, JeongYeol Chelu, Ioana Veronica Iordache, Livia Paunescu, Vlad Ryu, HyunJoo Ghiuta, Alexandru Petreanu, Andrei Soh, YunSung Leica, Andrei Jeon, ByeongMoon LG Elect Convergence Ctr Seoul South Korea Arnia Software Bucharest Romania

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Modern driver assistance systems rely on a wide range of sensors (RADAR, LIDAR, ultrasound and cameras) for scene understanding and prediction. These sensors are typically used for detecting traffic participants and scene elements required for navigation. In this paper we argue that relying on camera based systems, specifically Around View Monitoring (AVM) system has great potential to achieve these goals in both parking and driving modes with decreased costs. The contributions of this paper are as follows: we present a new end-to-end solution for delimiting the safe drivable area for each frame by means of identifying the closest obstacle in each direction from the driving vehicle, we use this approach to calculate the distance to the nearest obstacles and we incorporate it into a unified end-to-end architecture capable of joint object detection, curb detection and safe drivable area detection. Furthermore, we describe the family of networks for both a high accuracy solution and a low complexity solution. We also introduce further augmentation of the base architecture with 3D object detection.

关键词： Object detection computer architecture Three-dimensional displays Cameras Task analysis Complexity theory Two dimensional displays

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