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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961-970 订阅

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(ASNA) An Attention-based Siamese-Difference Neural Network with Surrogate Ranking Loss function for Perceptual Image Quality Assessment

(ASNA) An Attention-based Siamese-Difference Neural Network ...

引用

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

作者： Ayyoubzadeh, Seyed Mehdi Royat, Ali McMaster Univ Hamilton ON Canada Sharif Univ Technol Tehran Iran

ISBN: (纸本)9781665448994

Recently, deep convolutional neural networks (DCNN) that leverage the adversarial training framework for image restoration and enhancement have significantly improved the processed images' sharpness. Surprisingly, although these DCNNs produced crispier images than other methods visually, they may get a lower quality score when popular measures are employed for evaluating them. Therefore it is necessary to develop a quantitative metric to reflect their performances, which is well-aligned with the perceived quality of an image. Famous quantitative metrics such as Peak signal-to-noise ratio (PSNR), The structural similarity index measure (SSIM), and Perceptual Index (PI) are not well-correlated with the mean opinion score (MOS) for an image, especially for the neural networks trained with adversarial loss functions. This paper has proposed a convolutional neural network using an extension architecture of the traditional Siamese network so-called Siamese-Difference neural network. We have equipped this architecture with the spatial and channel-wise attention mechanism to increase our method's performance. Finally, we employed an auxiliary loss function to train our model. The suggested additional cost function surrogates ranking loss to increase Spearman's rank correlation coefficient while it is differentiable concerning the neural network parameters. Our method achieved superior performance in NTIRE 2021 Perceptual Image Quality Assessment Challenge. The implementations of our proposed method are publicly available.(1 2)

关键词： Image quality Training Visualization Technological innovation PSNR Neural networks computer architecture

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Expectation-Maximization Attention Cross Residual Network for Single Image Super-resolution

Expectation-Maximization Attention Cross Residual Network fo...

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

作者： Du, Xiaobiao Niu, Jie Liu, Chongjin Jilin Univ Zhuhai Coll Zhuhai Peoples R China Unit 61212 Peoples Liberat Army Beijing Peoples R China

ISBN: (纸本)9781665448994

The depth of deep convolution neural network and self-attention mechanism is widely used for the single image super-resolution (SISR) task. Nevertheless, we observed that the deeper network was more hard to train and the self-attention mechanism is computationally consuming. Residual learning has been widely recognized as a common approach to improve network performance for deep learning, but most existing methods did not make the best of the learning ability of deep CNN, thus hindering the ability of representative CNN. In order to tackle these problems, we introduce a deep learning network namely expectation-maximization attention cross residual network (EACRN) to tackle the super-resolution task. Particularly, we propose a cross residual in cross residual (CRICR) structure that makes up very deep networks consisting of multiple cross residual groups (CRG) with global residual skip connections. Every cross residual group (CRG) consists of some cross residual blocks with cross short skip connections. At the same time, CRICR allows network focused on capturing high frequency patterns by connecting rich low frequency patterns to be bypassed and several short skip connections. In addition, we introduce various convolution kernel size so that adaptive capture the image pattern in different scales, which make these features get the more efficacious image information through interacting with each other. The introduced Expectation-Maximization Attention (EMA) module is robust to the variance of input and is also friendly in memory and computation. Extensive experiments demonstrate our EACRN obtains superior performance and visual effect relative to the most advanced algorithm.

关键词： Deep learning Convolution Computational modeling Superresolution Neural networks Visual effects pattern recognition

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Thermal Image Super-Resolution Challenge Results - PBVS 2024

Thermal Image Super-Resolution Challenge Results - PBVS 2024

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Rafael E. Rivadeneira Angel D. Sappa Chenyang Wang Junjun Jiang Zhiwei Zhong Peilin Chen Shiqi Wang Escuela Superior Politécnica del Litoral ESPOL Guayaquil Ecuador Computer Vision Center Campus UAB Barcelona Spain

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper outlines the advancements and results of the Fifth Thermal Image Super-Resolution challenge, hosted at the Perception Beyond the Visible Spectrum cvpr 2024 workshop. The challenge employed a novel benchmark cross-spectral dataset consisting of 1000 thermal images, each paired with its corresponding registered RGB image. The challenge featured two tracks: Track-1 focused on Single Thermal Image Super-Resolution with an ×8 upscale factor, while Track-2 extended its evaluation to include both ×8 and ×16 scaling factors, utilizing high-resolution RGB images to guide the super-resolution process for low-resolution thermal images. The participation of over 175 teams highlights the research community’s strong engagement and dedication to enhancing image resolution techniques across both single and cross-spectral methodologies. This year’s challenge sets new benchmarks and provides valuable insights into future directions for research in thermal image super-resolution.

关键词： Image quality computer vision Thermal factors conferences Superresolution Benchmark testing Transformers

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Assessing the Performance of Efficient Face Anti-Spoofing Detection Against Physical and Digital Presentation Attacks

Assessing the Performance of Efficient Face Anti-Spoofing De...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Luis S. Luevano Yoanna Martínez-Díaz Heydi Méndez-Vázquez Miguel González-Mendoza Davide Frey Inria CNRS IRISA Univ Rennes Rennes France Advanced Technologies Application Center (CENATAV) Havana Cuba School of Engineering and Sciences Tecnologico de Monterrey N.L. Mexico

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we examine how pre-processing and training methods impact on the performance of Lightweight CNNs through evaluations on MobileNetV3 with a spoofing detection head, dubbed "MobileNetV3-Spoof". Using the UniAttackData dataset from the 5th Face Anti-Spoofing Challenge@cvpr2024, which covers a broad spectrum of spoofing scenarios including deepfake and adversarial attack samples, we assess how well the model performs over different setups, including pre-trained models and models trained from scratch with or without initial face detection and alignment. Our results show that pre-processing steps significantly boost the model’s ability to identify spoof samples, especially against complex attacks. Through detailed comparisons, we offer insights that could guide data curation and the creation of more effective and efficient anti-spoofing techniques suitable for real-world use in the era of digital face attacks. We make our code publicly available at: https://***/Inria-CENATAV-Tec/Assessing-Efficient-FAS-cvpr2024

关键词： Training Deepfakes Accuracy Sensitivity Face recognition Computational modeling conferences

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Lifting Monocular Events to 3D Human Poses

Lifting Monocular Events to 3D Human Poses

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

作者： Scarpellini, Gianluca Morerio, Pietro Del Bue, Alessio Ist Italiano Tecnol Pattern Anal & Comp Vis Genoa Italy Univ Genoa Genoa Italy Ist Italiano Tecnol Visual Geometry & Modelling Genoa Italy

ISBN: (纸本)9781665448994

This paper presents a novel 3D human pose estimation approach using a single stream of asynchronous events as input. Most of the state-of-the-art approaches solve this task with RGB cameras, however struggling when subjects are moving fast. On the other hand, event-based 3D pose estimation benefits from the advantages of event-cameras, especially their efficiency and robustness to appearance changes. Yet, finding human poses in asynchronous events is in general more challenging than standard RGB pose estimation, since little or no events are triggered in static scenes. Here we propose the first learning-based method for 3D human pose from a single stream of events. Our method consists of two steps. First, we process the event-camera stream to predict three orthogonal heatmaps per joint;each heatmap is the projection of of the joint onto one orthogonal plane. Next, we fuse the sets of heatmaps to estimate 3D localisation of the body joints. As a further contribution, we make available a new, challenging dataset for event-based human pose estimation by simulating events from the RGB Human3.6m dataset. Experiments demonstrate that our method achieves solid accuracy, narrowing the performance gap between standard RGB and event-based vision. The code is freely available at https://***/lifting_events_to_3d_hpe.

关键词： Heating systems Learning systems Three-dimensional displays Fuses Pose estimation Solids Robustness

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Phenology Alignment Network: A Novel Framework for Cross-Regional Time Series Crop Classification

Phenology Alignment Network: A Novel Framework for Cross-Reg...

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

作者： Wang, Ziqiao Zhang, Hongyan He, Wei Zhang, Liangpei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China RIKEN Ctr Adv Intelligence Project AIP Tokyo 1030027 Japan

ISBN: (纸本)9781665448994

Timely and accurate crop type classification plays an essential role in the study of agricultural application. However, large area or cross-regional crop classification confronts huge challenges owing to dramatic phenology discrepancy among training and test regions. In this work, we propose a novel framework to address these challenges based on deep recurrent network and unsupervised domain adaptation (DA). Specifically, we firstly propose a Temporal Spatial Network (TSNet) for pixelwise crop classification, which contains stacked RNN and self-attention module to adaptively extract multi-level features from crop samples under various planting conditions. To deal with the cross-regional challenge, an unsupervised DA-based framework named Phenology Alignment Network (PAN) is proposed. PAN consists of two branches of two identical TSNet pre-trained on source domain;one branch takes source samples while the other takes target samples as input. Through aligning the hierarchical deep features extracted from two branches, the discrepancy between two regions is decreased and the pre-trained model is adapted to the target domain without using target label information. As another contribution, a time series dataset based on Sentinel-2 was annotated containing winter crop samples collected on three study sites of China. Cross-regional experiments demonstrate that TSNet shows comparable accuracy to state-of-the-art methods, and PAN further improves the overall accuracy by 5.62%, and macro average F1 score by 0.094 unsupervisedly.

关键词： Training computer vision Adaptation models conferences Time series analysis Feature extraction Agriculture

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Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection

Pseudo-IoU: Improving Label Assignment in Anchor-Free Object...

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

作者： Li, Jiachen Cheng, Bowen Feris, Rogerio Xiong, Jinjun Huang, Thomas S. Hwu, Wen-Mei Shi, Humphrey UIUC Champaign IL 61820 USA MIT IBM Watson AI Lab Cambridge MA USA IBM TJ Watson Res Ctr Ossining NY USA NVIDIA Santa Clara CA USA Univ Oregon Eugene OR 97403 USA Picsart AI Res PAIR Champaign IL USA

ISBN: (纸本)9781665448994

Current anchor-free object detectors are quite simple and effective yet lack accurate label assignment methods, which limits their potential in competing with classic anchor-based models that are supported by well-designed assignment methods based on the Intersection-over-Union (IoU) metric. In this paper, we present Pseudo-Intersection-over-Union (Pseudo-IoU): a simple metric that brings more standardized and accurate assignment rule into anchor-free object detection frameworks without any additional computational cost or extra parameters for training and testing, making it possible to further improve anchor-free object detection by utilizing training samples of good quality under effective assignment rules that have been previously applied in anchor-based methods. By incorporating Pseudo-IoU metric into an end-to-end single-stage anchor-free object detection framework, we observe consistent improvements in their performance on general object detection benchmarks such as PASCAL VOC and MSCOCO. Our method (single-model and single-scale) also achieves comparable performance to other recent state-of-the-art anchor-free methods without bells and whistles. Our code is based on mmdetection toolbox and will be made publicly available at https://***/SHILabs/Pseudo-IoU-for-Anchor-Free-Object-Detection.

关键词： Measurement Training Location awareness computer vision Computational modeling conferences Object detection

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Geometric empirical Bayesian model for classification of functional data under diverse sampling regimes

Geometric empirical Bayesian model for classification of fun...

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

作者： Matuk, James Bharath, Karthik Chkrebtii, Oksana Kurtek, Sebastian Ohio State Univ Columbus OH 43210 USA Univ Nottingham Nottingham England

ISBN: (纸本)9781665448994

Functional data analysis (FDA) is focused on various statistical tasks, including inference, for observations that vary over a continuum, which are not effectively addressed by multivariate methods. A feature of these functional observations is the presence of two distinct forms of variability: amplitude that describes differences in magnitudes of features, e.g., extrema, and phase that describes differences in timings of amplitude features. One area of focus in FDA is the classification of new observations based on previously observed training data that has been split into pre-defined classes. Existing methods fail to directly account for both phase and amplitude variability, and work under the restrictive assumption that functional observations are measured on a common, fine grid over the input domain. In this work, we address these issues directly by formulating a Bayesian hierarchical model for irregular, fragmented or sparsely sampled functional observations, where training data from different classes are available. Our approach builds on a recently developed inferential framework for incomplete functional observations and the elastic FDA framework for characterizing amplitude and phase variability. The approach operates by inferring individual parameters that separately track amplitude and phase, which can be combined to infer complete functions underlying each observation, and a class parameter, which can be used to discern the class membership of an observation based on the training data. We validate the proposed framework using simulation studies and real data applications, and showcase the advantages of this perspective when both amplitude and phase variability are present in the data.

关键词： computer vision Phase measurement Computational modeling conferences Training data Data models Bayes methods

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Boosting Co-teaching with Compression Regularization for Label Noise

Boosting Co-teaching with Compression Regularization for Lab...

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

作者： Chen, Yingyi Shen, Xi Hu, Shell Xu Suykens, Johan A. K. Katholieke Univ Leuven ESAT STADIUS Leuven Belgium UPE Ecole Ponts LIGM UMR 8049 Champs Sur Marne France Upload AI LLC Houston TX USA

ISBN: (纸本)9781665448994

In this paper, we study the problem of learning image classification models in the presence of label noise. We revisit a simple compression regularization named Nested Dropout [22]. We find that Nested Dropout [22], though originally proposed to perform fast information retrieval and adaptive data compression, can properly regularize a neural network to combat label noise. Moreover, owing to its simplicity, it can be easily combined with Co-teaching [5] to further boost the performance. Our final model remains simple yet effective: it achieves comparable or even better performance than the state-of-the-art approaches on two real-world datasets with label noise which are Clothing1M [28] and ANIMAL-10N [24]. On Clothing1M [28], our approach obtains 74.9% accuracy which is slightly better than that of DivideMix [12]. On ANIMAL-10N [24], we achieve 84.1% accuracy while the best public result by PLC [30] is 83.4%. We hope that our simple approach can be served as a strong baseline for learning with label noise. Our implementation is available at https://***/yingyichen-cyy/Nested-Co-teaching.

关键词： Adaptation models computer vision Image coding conferences Neural networks Data compression Information retrieval

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Differential Morph Face Detection using Discriminative Wavelet Sub-bands

Differential Morph Face Detection using Discriminative Wavel...

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

作者： Chaudhary, Baaria Aghdaie, Poorya Soleymani, Sobhan Dawson, Jeremy Nasrabadi, Nasser M. West Virginia Univ Morgantown WV 26506 USA

ISBN: (纸本)9781665448994

Face recognition systems are extremely vulnerable to morphing attacks, in which a morphed facial reference image can be successfully verified as two or more distinct identities. In this paper, we propose a morph attack detection algorithm that leverages an undecimated 2D Discrete Wavelet Transform (DWT) for identifying morphed face images. The core of our framework is that artifacts resulting from the morphing process that are not discernible in the image domain can be more easily identified in the spatial frequency domain. A discriminative wavelet sub-band can accentuate the disparity between a real and a morphed image. To this end, multi-level DWT is applied to all images, yielding 48 mid and high-frequency sub-bands each. The entropy distributions for each sub-band are calculated separately for both bona fide and morph images. For some of the sub-bands, there is a marked difference between the entropy of the sub-band in a bona fide image and the identical sub-band's entropy in a morphed image. Consequently, we employ Kullback-Liebler Divergence (KLD) to exploit these differences and isolate the sub-bands that are the most discriminative. We measure how discriminative a sub-band is by its KLD value and the 22 sub-bands with the highest KLD values are chosen for network training. Then, we train a deep Siamese neural network using these 22 selected sub-bands for differential morph attack detection. We examine the efficacy of discriminative wavelet sub-bands for morph attack detection and show that a deep neural network trained on these sub-bands can accurately identify morph imagery.

关键词： Training Wavelet domain Protocols Face recognition Frequency-domain analysis Neural networks Entropy

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