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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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NTIRE 2024 Restore Any Image Model (RAIM) in the Wild Challenge

NTIRE 2024 Restore Any Image Model (RAIM) in the Wild Challe...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jie Liang Radu Timofte Qiaosi Yi Shuaizheng Liu Lingchen Sun Rongyuan Wu Xindong Zhang Hui Zeng Lei Zhang Yibin Huang Shuai Liu Yongqiang Li Chaoyu Feng Xiaotao Wang Lei Lei Yuxiang Chen Xiangyu Chen Qiubo Chen Fengyu Sun Mengying Cui Jiaxu Chen Zhenyu Hu Jingyun Liu Wenzhuo Ma Ce Wang Hanyou Zheng Wanjie Sun Zhenzhong Chen Ziwei Luo Fredrik K. Gustafsson Zheng Zhao Jens Sjölund Thomas B. Schön Xiong Dun Pengzhou Ji Yujie Xing Xuquan Wang Zhanshan Wang Xinbin Cheng Jun Xiao Chenhang He Xiuyuan Wang Zhi-Song Liu Zimeng Miao Zhicun Yin Ming Liu Wangmeng Zuo Shuai Li

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we review the NTIRE 2024 challenge on Restore Any Image Model (RAIM) in the Wild. The RAIM challenge constructed a benchmark for image restoration in the wild, including real-world images with/without reference ground truth in various scenarios from real applications. The participants were required to restore the real-captured images from complex and unknown degradation, where generative perceptual quality and fidelity are desired in the restoration result. The challenge consisted of two tasks. Task one employed real referenced data pairs, where quantitative evaluation is available. Task two used unpaired images, and a comprehensive user study was conducted. The challenge attracted more than 200 registrations, where 39 of them submitted results with more than 400 submissions. Top-ranked methods improved the state-of-the-art restoration performance and obtained unanimous recognition from all 18 judges. The proposed datasets are available at https : //***/file/d/1DqbxUoiUqkAIkExu3jZAqoElr_nu1IXb/view?usp=sharing and the homepage of this challenge is at https : //***/competitions/17632.

关键词： Degradation computer vision Reviews conferences Computational modeling Benchmark testing Image restoration

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Clothed Human Performance Capture with a Double-layer Neural Radiance Fields

Clothed Human Performance Capture with a Double-layer Neural...

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2023 ieee/cvf conference on computer vision and pattern recognition, CVPR 2023

作者： Wang, Kangkan Zhang, Guofeng Cong, Suxu Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China

This paper addresses the challenge of capturing performance for the clothed humans from sparse-view or monocular videos. Previous methods capture the performance of full humans with a personalized template or recover the garments from a single frame with static human poses. However, it is inconvenient to extract cloth semantics and capture clothing motion with one-piece template, while single frame-based methods may suffer from instable tracking across videos. To address these problems, we propose a novel method for human performance capture by tracking clothing and human body motion separately with a double-layer neural radiance fields (NeRFs). Specifically, we propose a double-layer NeRFs for the body and garments, and track the densely deforming template of the clothing and body by jointly optimizing the deformation fields and the canonical double-layer NeRFs. In the optimization, we introduce a physics-aware cloth simulation network which can help generate physically plausible cloth dynamics and body-cloth interactions. Compared with existing methods, our method is fully differentiable and can capture both the body and clothing motion robustly from dynamic videos. Also, our method represents the clothing with an independent NeRFs, allowing us to model implicit fields of general clothes feasibly. The experimental evaluations validate its effectiveness on real multi-view or monocular videos. ©2023 ieee.

关键词： Multilayer neural networks

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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2023 ieee/cvf conference on computer vision and pattern recognition, CVPR 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 ieee.

关键词： Autonomous driving

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Selective Multi-View Deep Model for 3D Object Classification

Selective Multi-View Deep Model for 3D Object Classification

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Mona Alzahrani Muhammad Usman Saeed Anwar Tarek Helmy Department of Information & Computer Science KFUPM Dhahran Saudi Arabia College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence KFUPM Dhahran Saudi Arabia Center for Intelligent Secure Systems KFUPM Dhahran Saudi Arabia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

3D object classification has emerged as a practical technology with applications in various domains, such as medical image analysis, automated driving, intelligent robots, and crowd surveillance. Among the different approaches, multi-view representations for 3D object classification have shown the most promising results, achieving state-of-the-art performance. However, there are certain limitations in current view-based 3D object classification methods. One observation is that using all captured views for classification can confuse the classifier and lead to misleading results for certain classes. Additionally, some views may contain more discriminative information for object classification than others. These observations motivate the development of smarter and more efficient selective multi-view classification models. In this work, we propose a Selective Multi-View Deep Model that extracts multi-view images from 3D data representations and selects the most influential view by assigning importance scores using the cosine similarity method based on visual features detected by a pre-trained CNN. The proposed method is evaluated on the ModelNet40 dataset for the task of 3D classification. The results demonstrate that the proposed model achieves an overall accuracy of 88.13% using only a single view when employing a shading technique for rendering the views, pre-trained ResNet-152 as the backbone CNN for feature extraction, and a Fully Connected Network (FCN) as the classifier.

关键词： Training Solid modeling Visualization Three-dimensional displays Image analysis Surveillance Feature extraction

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Exploring and Utilizing pattern Imbalance

Exploring and Utilizing Pattern Imbalance

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conference on computer vision and pattern recognition (CVPR)

作者： Shibin Mei Chenglong Zhao Shengchao Yuan Bingbing Ni Shanghai Jiao Tong University Shanghai China

In this paper, we identify pattern imbalance from several aspects, and further develop a new training scheme to avert pattern preference as well as spurious correlation. In contrast to prior methods which are mostly concerned with category or domain granularity, ignoring the potential finer structure that existed in datasets, we give a new definition of seed category as an appropriate optimization unit to distinguish different patterns in the same category or domain. Extensive experiments on domain generalization datasets of diverse scales demonstrate the effectiveness of the proposed method.

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How Suboptimal is Training rPPG Models with Videos and Targets from Different Body Sites?

How Suboptimal is Training rPPG Models with Videos and Targe...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Björn Braun Daniel McDuff Christian Holz Department of Computer Science ETH Zürich Switzerland Paul G. Allen School of Computer Science University of Washington

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Remote camera measurement of the blood volume pulse via photoplethysmography (rPPG) is a compelling technology for scalable, low-cost, and accessible assessment of cardiovascular information. Neural networks currently provide the state-of-the-art for this task and supervised training or fine-tuning is an important step in creating these models. However, most current models are trained on facial videos using contact PPG measurements from the fingertip as targets/labels. One of the reasons for this is that few public datasets to date have incorporated contact PPG measurements from the face. Yet there is copious evidence that the PPG signals at different sites on the body have very different morphological features. Is training a facial video rPPG model using contact measurements from another site on the body suboptimal? Using a recently released unique dataset with synchronized contact PPG and video measurements from both the hand and face, we can provide precise and quantitative answers to this question. We obtain up to 40% lower mean squared errors between the waveforms of the predicted and the ground truth PPG signals using state-of-the-art neural models when using PPG signals from the forehead compared to using PPG signals from the fingertip. We also show qualitatively that the neural models learn to predict the morphology of the ground truth PPG signal better when trained on the forehead PPG signals. However, while models trained from the forehead PPG produce a more faithful waveform, models trained from a finger PPG do still learn the dominant frequency (i.e., the heart rate) well.

关键词： Training Wrist Forehead Current measurement Volume measurement Estimation Predictive models

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A case for using rotation invariant features in state of the art feature matchers

A case for using rotation invariant features in state of the...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Georg Bö kman Fredrik Kahl Chalmers University of Technology

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

The aim of this paper is to demonstrate that a state of the art feature matcher (LoFTR) can be made more robust to rotations by simply replacing the backbone CNN with a steerable CNN which is equivariant to translations and image rotations. It is experimentally shown that this boost is obtained without reducing performance on ordinary illumination and viewpoint matching sequences.

关键词： computer vision conferences Lighting pattern matching

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DepthVoting: A Few-Shot Point Cloud Classification Model Incorporating a Projection-Based Voting Mechanism

DepthVoting: A Few-Shot Point Cloud Classification Model Inc...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yunhui Zhu Jiajing Chen Senem Velipasalar Electrical Engineering and Computer Science Dept. Syracuse University Syracuse NY USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Despite the significant progress in few-shot 2D image classification, few-shot 3D point cloud classification remains relatively under-explored, particularly in addressing the challenges posed by missing points in 3D point clouds. Most existing methods for few-shot 3D point cloud classification are point-based, and thus, highly sensitive to missing points. Despite recent attempts, such as ViewNet, which introduce projection-based backbones to increase robustness against missing points, the reliance on max pooling, to extract information from multiple images simultaneously, makes them prone to information loss. To address these limitations, we introduce DepthVoting, a novel projection-based approach, for few-shot 3D point cloud classification. Instead of extracting features from multiple projection images simultaneously, DepthVoting captures features from pairs of projection images (obtained from opposite view angles) separately, enhancing the extraction of more comprehensive information. These features are sent to multiple few-shot heads, which share parameters. To further refine predictions, DepthVoting incorporates a voting mechanism, allowing contribution and incorporating information from different pairs. We conduct extensive experiments on three datasets, namely ModelNet40, ModelNet40-C, and ScanObjectNN, along with cross-validation. Our proposed method consistently outperforms the state-of-the-art baselines on all datasets in terms of average accuracy with even higher margins on the challenging ScanObjectNN dataset.

关键词： Point cloud compression Solid modeling Three-dimensional displays Head Accuracy Feature extraction Data augmentation

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A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception

A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Asude Aydin Mathias Gehrig Daniel Gehrig Davide Scaramuzza Robotics and Perception Group University of Zurich Switzerland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Spiking Neural Networks (SNNs) are a class of bio-inspired neural networks that promise to bring low-power and low-latency inference to edge-devices through the use of asynchronous and sparse processing. However, being temporal models, SNNs depend heavily on expressive states to generate predictions on par with classical artificial neural networks (ANNs). These states converge only after long transient time periods, and quickly decay in the absence of input data, leading to higher latency, power consumption, and lower accuracy. In this work, we address this issue by initializing the state with an auxiliary ANN running at a low rate. The SNN then uses the state to generate predictions with high temporal resolution until the next initialization phase. Our hybrid ANN-SNN model thus combines the best of both worlds: It does not suffer from long state transients and state decay thanks to the ANN, and can generate predictions with high temporal resolution, low latency, and low power thanks to the SNN. We show for the task of event-based 2D and 3D human pose estimation that our method consumes 88% less power with only a 4% decrease in performance compared to its fully ANN counterparts when run at the same inference rate. Moreover, when compared to SNNs, our method achieves a 74% lower error. This research thus provides a new understanding of how ANNs and SNNs can be used to maximize their respective benefits. Code: https://***/uzh-rpg/hybrid_ann_snn

关键词： Accuracy Three-dimensional displays Pose estimation computer architecture Spiking neural networks Predictive models Low latency communication

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Imaging Signal Recovery Using Neural Network Priors Under Uncertain Forward Model Parameters

Imaging Signal Recovery Using Neural Network Priors Under Un...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xiwen Chen Wenhui Zhu Peijie Qiu Abolfazl Razi School of Computing Clemson University School of Computing and Augmented Intelligence Arizona State University McKeley School of Engineering Washington University in St. Louis

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Inverse imaging problems (IIPs) arise in various applications, with the main objective of reconstructing an image from its compressed measurements. This problem is often ill-posed for being under-determined with multiple interchangeably consistent solutions. The best solution inherently depends on prior knowledge or assumptions, such as the sparsity of the image. Furthermore, the reconstruction process for most IIPs relies significantly on the imaging (i.e. forward model) parameters, which might not be fully known, or the measurement device may undergo calibration drifts. These uncertainties in the forward model create substantial challenges, where inaccurate reconstructions usually happen when the postulated parameters of the forward model do not fully match the actual ones. In this work, we devoted to tackling accurate reconstruction under the context of a set of possible forward model parameters that exist. Here, we propose a novel Moment-Aggregation (MA) framework that is compatible with the popular IIP solution by using a neural network prior. Specifically, our method can reconstruct the signal by considering all candidate parameters of the forward model simultaneously during the update of the neural network. We theoretically demonstrate the convergence of the MA framework, which has a similar complexity with reconstruction under the known forward model parameters. Proof-of-concept experiments demonstrate that the proposed MA achieves performance comparable to the forward model with the known precise parameter in reconstruction across both compressive sensing and phase retrieval applications, with a PSNR gap of 0.17 to 1.94 over various datasets, including MNIST, X-ray, Glas, and MoNuseg. This highlights our method’s significant potential in reconstruction under an uncertain forward model.

关键词： Uncertainty Image coding Computational modeling Neural networks Imaging pattern recognition Image reconstruction

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