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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...

引用

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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NPBG++: Accelerating Neural Point-Based Graphics

NPBG++: Accelerating Neural Point-Based Graphics

引用

2022 ieee/cvf conference on computer vision and pattern recognition, CVPR 2022

作者： Rakhimov, Ruslan Ardelean, Andrei-Timotei Lempitsky, Victor Burnaev, Evgeny Skolkovo Institute of Science and Technology Russia Yandex Russia Artificial Intelligence Research Institute Russia

ISBN: (纸本)9781665469463

We present a new system $(NPBG++)$ for the novel view synthesis (NVS) task that achieves high rendering realism with low scene fitting time. Our method efficiently lever-ages the multiview observations and the point cloud of a static scene to predict a neural descriptor for each point, improving upon the pipeline of Neural Point-Based Graph-ics [1] in several important ways. By predicting the descrip-tors with a single pass through the source images, we lift the requirement of per-scene optimization while also making the neural descriptors view-dependent and more suit-able for scenes with strong non-Lambertian effects. In our comparisons, the proposed system outperforms previous NVS approaches in terms of fitting and rendering runtimes while producing images of similar quality. Project page: https://***/npbgpp/. © 2022 ieee.

关键词： Rendering (computer graphics)

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Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and Report

Efficient Deep Models for Real-Time 4K Image Super-Resolutio...

引用

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

作者： Marcos V. Conde Eduard Zamfir Radu Timofte Daniel Motilla Cen Liu Zexin Zhang Yunbo Peng Yue Lin Jiaming Guo Xueyi Zou Yuyi Chen Yi Liu Jia Hao Youliang Yan Yuanfan Zhang Gen Li Lei Sun Lingshun Kong Haoran Bai Jinshan Pan Jiangxin Dong Jinhui Tang Mustafa Ayazoglu Bahri Batuhan Bilecen Mingxi Li Yuhang Zhang Xianjun Fan Yankai Sheng Long Sun Zibin Liu Weiran Gou Shaoqing Li Ziyao Yi Yan Xiang Dehui Kong Ke Xu Ganzorig Gankhuyag Kihwan Yoon Jin Zhang Gaocheng Yu Feng Zhang Hongbin Wang Zhou Zhou Jiahao Chao Hongfan Gao Jiali Gong Zhengfeng Yang Zhenbing Zeng Chengpeng Chen Zichao Guo Anjin Park Yuqing Liu Qi Jia Hongyuan Yu Xuanwu Yin Kunlong Zuo Dongyang Zhang Ting Fu Zhengxue Cheng Shiai Zhu Dajiang Zhou Weichen Yu Lin Ge Jiahua Dong Yajun Zou Zhuoyuan Wu Binnan Han Xiaolin Zhang Heng Zhang Ben Shao Shaolong Zheng Daheng Yin Baijun Chen Mengyang Liu Marian-Sergiu Nistor Yi-Chung Chen Zhi-Kai Huang Yuan-Chun Chiang Wei-Ting Chen Hao-Hsiang Yang Hua-En Chang I-Hsiang Chen Chia-Hsuan Hsieh Sy-Yen Kuo Tu Vo Qingsen Yan Yun Zhu Jinqiu Su Yanning Zhang Cheng Zhang Jiaying Luo Youngsun Cho Nakyung Lee Computer Vision Lab University of Wurzburg Germany SIE FTG USA

This paper introduces a novel benchmark for efficient up-scaling as part of the NTIRE 2023 Real-Time Image Super-Resolution (RTSR) Challenge, which aimed to upscale images from 720p and 1080p resolution to native 4K (×2 and ×3 factors) in real-time on commercial GPUs. For this, we use a new test set containing diverse 4K images ranging from digital art to gaming and photography. We assessed the methods devised for 4K SR by measuring their runtime, parameters, and FLOPs, while ensuring a minimum PSNR fidelity over Bicubic interpolation. Out of the 170 participants, 25 teams contributed to this report, making it the most comprehensive benchmark to date and showcasing the latest advancements in real-time SR.

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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...

引用

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 ...

引用

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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Video Action Detection: Analysing Limitations and Challenges

Video Action Detection: Analysing Limitations and Challenges

引用

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

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Beyond possessing large enough size to feed data hungry machines (eg, transformers), what attributes measure the quality of a dataset? Assuming that the definitions of such attributes do exist, how do we quantify among their relative existences? Our work attempts to explore these questions for video action detection. The task aims to spatio-temporally localize an actor and assign a relevant action class. We first analyze the existing datasets on video action detection and discuss their limitations. Next, we propose a new dataset, Multi Actor Multi Action (MAMA) which overcomes these limitations and is more suitable for real world applications. In addition, we perform a biasness study which analyzes a key property differentiating videos from static images: the temporal aspect. This reveals if the actions in these datasets really need the motion information of an actor, or whether they predict the occurrence of an action even by looking at a single frame. Finally, we investigate the widely held assumptions on the importance of temporal ordering: is temporal ordering important for detecting these actions? Such extreme experiments show existence of biases which have managed to creep into existing methods inspite of careful modeling.

关键词： pattern recognition conferences computer vision

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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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Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise

Manifold DivideMix: A Semi-Supervised Contrastive Learning F...

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

作者： Fahimeh Fooladgar Minh Nguyen Nhat To Parvin Mousavi Purang Abolmaesumi Department of Electrical and Computer Engineering University of British Columbia CA School of Computing Queen’s University CA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep neural networks have proven to be highly effective when large amounts of data with clean labels are available. However, their performance degrades when training data contains noisy labels, leading to poor generalization on the test set. Real-world datasets contain noisy label samples that either have similar visual semantics to other classes (in-distribution) or have no semantic relevance to any class (out-of-distribution) in the dataset. Most state-of-the-art methods leverage ID labeled noisy samples as unlabeled data for semi-supervised learning, but OOD labeled noisy samples cannot be used in this way because they do not belong to any class within the dataset. Hence, in this paper, we propose incorporating the information from all the training data by leveraging the benefits of self-supervised training. Our method aims to extract a meaningful and generalizable embedding space for each sample regardless of its label. Then, we employ a simple yet effective K-nearest neighbor method to remove portions of out-of-distribution samples. By discarding these samples, we propose an iterative "Manifold DivideMix" algorithm to find clean and noisy samples, and train our model in a semi-supervised way. In addition, we propose "MixEMatch", a new algorithm for the semi-supervised step that involves mixup augmentation at the input and final hidden representations of the model. This will extract better representations by interpolating both in the input and manifold spaces. Extensive experiments on multiple synthetic-noise image benchmarks and real-world web-crawled datasets demonstrate the effectiveness of our proposed framework. Code is available at https://***/Fahim-F/ManifoldDivideMix.

关键词： Manifolds Representation learning Noise Semantics Training data Clustering algorithms Contrastive learning

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NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results

NTIRE 2024 Challenge on Short-form UGC Video Quality Assessm...

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

作者： Xin Li Kun Yuan Yajing Pei Yiting Lu Ming Sun Chao Zhou Zhibo Chen Radu Timofte Wei Sun Haoning Wu Zicheng Zhang Jun Jia Zhichao Zhang Linhan Cao Qiubo Chen Xiongkuo Min Weisi Lin Guangtao Zhai JianHui Sun Tianyi Wang Lei Li Han Kong Wenxuan Wang Bing Li Cheng Luo Haiqiang Wang Xiangguang Chen Wenhui Meng Xiang Pan Huiying Shi Han Zhu Xiaozhong Xu Lei Sun Zhenzhong Chen Shan Liu Fangyuan Kong Haotian Fan Yifang Xu Haoran Xu Mengduo Yang Jie Zhou Jiaze Li Shijie Wen Mai Xu Da Li Shunyu Yao Jiazhi Du Wangmeng Zuo Zhibo Li Shuai He Anlong Ming Huiyuan Fu Huadong Ma Yong Wu Fie Xue Guozhi Zhao Lina Du Jie Guo Yu Zhang Huimin Zheng Junhao Chen Yue Liu Dulan Zhou Kele Xu Qisheng Xu Tao Sun Zhixiang Ding Yuhang Hu

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i.e., Kuaishou/Kwai Platform. The KVQ database is divided into three parts, including 2926 videos for training, 420 videos for validation, and 854 videos for testing. The purpose is to build new benchmarks and advance the development of S-UGC VQA. The competition had 200 participants and 13 teams submitted valid solutions for the final testing phase. The proposed solutions achieved state-of-the-art performances for S-UGC VQA. The project can be found at https://***/lixinustc/KVQ-Challenge-CVPR-NTIRE2024.

关键词： Training computer vision Reviews Databases conferences Benchmark testing Quality assessment

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Generative Flows as a General Purpose Solution for Inverse Problems

Generative Flows as a General Purpose Solution for Inverse P...

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

作者： José A. Chá vez San Pablo Catholic University Peru

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Due to the success of generative flows to model data distributions, they have been explored in inverse problems. Given a pre-trained generative flow, previous work proposed to minimize the 2-norm of the latent variables as a regularization term. The intuition behind it was to ensure high likelihood latent variables that produce the closest restoration. However, high-likelihood latent variables may generate unrealistic samples as we show in our experiments. We therefore propose a solver to directly produce high-likelihood reconstructions. We hypothesize that our approach could make generative flows a general purpose solver for inverse problems. Furthermore, we propose 1 × 1 coupling functions to introduce permutations in a generative flow. It has the advantage that its inverse does not require to be calculated in the generation process. Finally, we evaluate our method for denoising, deblurring, inpainting, and colorization. We observe a compelling improvement of our method over prior works.

关键词： Couplings computer vision Inverse problems conferences Noise reduction Data models pattern recognition

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