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Photoplethysmography based stratification of blood pressure using multi information fusion artificial neural network

Photoplethysmography based stratification of blood pressure ...

引用

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

作者： Dingliang Wang Xuezhi Yang Xuenan Liu Shuai Fang Likun Ma Longwei Li School of Computer and Information Hefei University of Technology Hefei China School of Software Hefei University of Technology Hefei China The First Affiliated Hospital University of Science and Technology of China Hefei China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Regular monitoring of blood pressure (BP) is an effective way to prevent cardiovascular diseases, especially for elderly people. At present, BP measurement mainly relies on cuff-based devices which are inconvenient for users and may cause discomfort. Therefore, many new approaches have been proposed to achieve cuff-less BP detection in recent years. However, the accuracy of the existing approaches still needs to be improved. In this study, holistic-based PPG and its first and second derivative features are extracted and a new multi information fusion artificial neural network (MIF-ANN) is designed to effectively fuse and exploit multiple input data. Experimental results on a public database which contains 12000 subjects show that the proposed network can model the relation between Photo-plethysmography (PPG) and BP well, achieving averagely accuracy of 91.33% for 5-category BP stratification. Additionally, this study verified that multi information fusion based on meticulously designed network plays an important role in improving the accuracy of BP detection.

关键词： Feature extraction Neural networks Blood pressure Databases Hypertension Biomedical monitoring Wavelet transforms

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ConceptHash: Interpretable Fine-Grained Hashing via Concept Discovery

ConceptHash: Interpretable Fine-Grained Hashing via Concept ...

引用

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

作者： Kam Woh Ng Xiatian Zhu Yi-Zhe Song Tao Xiang CVSSP University of Surrey iFlyTek-Surrey Joint Research Centre on Artificial Intelligence Surrey Institute for People-Centred Artificial Intelligence

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Existing fine-grained hashing methods typically lack code interpretability as they compute hash code bits holistically using both global and local features. To address this limitation, we propose ConceptHash, a novel method that achieves sub-code level interpretability. In ConceptHash, each sub-code corresponds to a human-understandable concept, such as an object part, and these concepts are automatically discovered without human annotations. Specifically, we leverage a vision Transformer architecture and introduce concept tokens as visual prompts, along with image patch tokens as model inputs. Each concept is then mapped to a specific sub-code at the model output, providing natural sub-code interpretability. To capture subtle visual differences among highly similar sub-categories (e.g., bird species), we incorporate language guidance to ensure that the learned hash codes are distinguishable within fine-grained object classes while maintaining semantic alignment. This approach allows us to develop hash codes that exhibit similarity within families of species while remaining distinct from species in other families. Extensive experiments on four fine-grained image retrieval benchmarks demonstrate that ConceptHash outperforms previous methods by a significant margin, offering unique sub-code interpretability as an additional benefit. Code at: https://***/kamwoh/concepthash.

关键词： Visualization computer vision Codes Semantics Image retrieval Manuals Inspection

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XoFTR: Cross-modal Feature Matching Transformer

XoFTR: Cross-modal Feature Matching Transformer

引用

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

作者： Önder Tuzcuoğlu Aybora Köksal Buğra Sofu Sinan Kalkan A. Aydın Alatan Dept. of Electrical and Electronics Eng. Center for Image Analysis Middle East Technical University Ankara Turkey ROKETSAN Inc. Ankara Turkey Dept. of Computer Eng. Center for Image Analysis Middle East Technical University Ankara Turkey

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce, XoFTR, a cross-modal cross-view method for local feature matching between thermal infrared (TIR) and visible images. Unlike visible images, TIR images are less susceptible to adverse lighting and weather conditions but present difficulties in matching due to significant texture and intensity differences. Current hand-crafted and learning-based methods for visible-TIR matching fall short in handling viewpoint, scale, and texture diversities. To address this, XoFTR incorporates masked image modeling pre-training and fine-tuning with pseudo-thermal image augmentation to handle the modality differences. Additionally, we introduce a refined matching pipeline that adjusts for scale discrepancies and enhances match reliability through sub-pixel level refinement. To validate our approach, we collect a comprehensive visible-thermal dataset, and show that our method outperforms existing methods on many benchmarks. Code and dataset at https://***/OnderT/XoFTR.

关键词： Learning systems Image matching Pipelines Lighting Benchmark testing Transformers Image augmentation

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Enhancing the Transferability of Adversarial Attacks with Stealth Preservation

Enhancing the Transferability of Adversarial Attacks with St...

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

作者： Xinwei Zhang Tianyuan Zhang Yitong Zhang Shuangcheng Liu School of Computer Science and Engineering Beihang University Beijing China State Key Lab of Software Development Environment Beihang University Beijing China Shen Yuan Honors College Beihang University Beijing China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep neural networks are susceptible to attacks from adversarial examples in recent years. Especially, the black-box attacks cause a more serious threat to practical applications. However, while most existing black-box attacks have achieved a high success rate in deceiving models, they have not focused on the stealthiness of adversarial examples, often exhibiting suspicious visual appearances. To address this issue, this paper proposes the Mask Momentum Iterative Attack (MMIA), which introduces a masking mechanism and adopts an optimal perturbation strategy to identify regions of an image most vulnerable to attacks. This approach effectively ensures the transferability and stealthiness of adversarial examples. Simultaneously, by integrating image enhancement techniques and temporal and spatial momentum terms into the iterative process of the attack, we prevent the attack from getting stuck in local optima, further improving the transferability of adversarial examples. To enhance the success rate of black-box attacks, we apply MMIA to a model ensemble using a joint optimization strategy. We demonstrate that adversarially trained models with a strong defense ability are also susceptible to our black-box attacks. We conduct extensive experiments on classification tasks using common vision models, and our results significantly demonstrate the superiority of our method over state-of-the-art approaches when considering both transferability and stealthiness.

关键词： Visualization Fuses Computational modeling Perturbation methods Closed box pattern recognition Iterative methods

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T2VBench: Benchmarking Temporal Dynamics for Text-to-Video Generation

T2VBench: Benchmarking Temporal Dynamics for Text-to-Video G...

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

作者： Pengliang Ji Chuyang Xiao Huilin Tai Mingxiao Huo Carnegie Mellon University ShanghaiTech University McGill University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

While text-to-video (T2V) generative models produce exceptionally realistic videos, they lack a comprehensive evaluation across the temporal dimension, with a limited focus on basic dynamics including camera transitions, movement, and event sequences. In this work, we introduce T2VBench, a comprehensive T2V evaluation benchmark enriched with temporal dynamics lexicons derived from curated temporal words on Wikipedia. T2VBench is a hierarchical evaluation framework comprising over 1,600 temporally rich prompts and 5,000 generated videos with human ratings, spanning 16 critical temporal evaluation dimensions. We assess three leading text-to-video models, including ZeroScope and Pika, to gauge their proficiency in handling temporal dynamics. Our analysis highlights the strengths and limitations of these models across various temporal aspects. Furthermore, we provide insights into future directions for enhancing text-to-video evaluation metrics and offer a detailed analysis of these models’ performance across the temporal dimensions. Overall, T2VBench is the first-of-its-kind comprehensive benchmark fully focused on temporal dynamics for text-to-video evaluation. It aims to facilitate scientific benchmarking of both generative models and automated metrics on text-to-video generation.

关键词： Measurement Analytical models Computational modeling conferences Encyclopedias Benchmark testing pattern recognition

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Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation

Unsupervised domain adaptation and super resolution on drone...

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

作者： Paul Albert Mohamed Saadeldin Badri Narayanan Brian Mac Namee Deirdre Hennessy Noel E. O’ Connor Kevin McGuinness School of Electronic Engineering Dublin City University Insight Centre for Data Analytics VistaMilk School of Computer Science University College Dublin Teagasc

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Herbage mass yield and composition estimation is an important tool for dairy farmers to ensure an adequate supply of high quality herbage for grazing and subsequently milk production. By accurately estimating herbage mass and composition, targeted nitrogen fertiliser application strategies can be deployed to improve localised regions in a herbage field, effectively reducing the negative impacts of over-fertilization on biodiversity and the environment. In this context, deep learning algorithms offer a tempting alternative to the usual means of sward composition estimation, which involves the destructive process of cutting a sample from the herbage field and sorting by hand all plant species in the herbage. The process is labour intensive and time consuming and so not utilised by farmers. Deep learning has been successfully applied in this context on images collected by high-resolution cameras on the ground. Moving the deep learning solution to drone imaging, however, has the potential to further improve the herbage mass yield and composition estimation task by extending the ground-level estimation to the large surfaces occupied by fields/paddocks. Drone images come at the cost of lower resolution views of the fields taken from a high altitude and requires further herbage ground-truth collection from the large surfaces covered by drone images. This paper proposes to transfer knowledge learned on ground-level images to raw drone images in an unsupervised manner. To do so, we use unpaired image style translation to enhance the resolution of drone images by a factor of eight and modify them to appear closer to their ground-level counterparts. We then use the enhanced drone images to train a semi-supervised algorithm that uses ground-truthed, ground-level images as the labelled data together with a large amount of unlabeled drone images. We validate our results on a small held-out drone image test set to show the validity of our approach, which opens the way for automate

关键词： Deep learning computer vision Image resolution Costs Estimation Prediction algorithms Cameras

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Medium Scale Benchmark for Cricket Excited Actions Understanding

Medium Scale Benchmark for Cricket Excited Actions Understan...

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

作者： Altaf Hussain Noman Khan Muhammad Munsif Min Je Kim Sung Wook Baik Sejong University Republic of Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The Sports Action recognition (SAR) domain is of significant importance in research, with diverse applications, ranging from aiding coaches in strategic decision-making to empowering athletes and contributing to real-time commercial entertainment. Despite the existence of extensive large-scale and small-scale datasets, the direct application of these datasets to specific sports domains, such as cricket, poses challenges. Existing datasets predominantly center around daily life actions, lacking the necessary granularity for in-depth sports analyses. Current Cricket Action Analysis (CAA) datasets have limitations, including their small scale, modality constraints, and their narrow focus on specific aspects, such as cricket batting. Recognizing the need for a more comprehensive benchmark, this article introduces the Cricket Excited Actions (CEA) dataset. Developed in collaboration with professional cricket players, the CEA dataset encompasses challenging multi-person actions within realistic cricket scenarios. The selected activity classes, such as Clean Bowled, Six, Four, and Catches, adhere to official standards and represent pivotal moments in cricket matches. Through precise annotation and empirical studies, utilizing state-of-the-art action recognition model architectures, this study provides a valuable resource for further research and makes significant contributions by offering support essential to advancing CAA within the cricket sports community. The data and code are available at https://***/Altaf-hucn/Cricket-Excited-Actions-Benchmark.

关键词： Computational modeling Refining Collaboration computer architecture Benchmark testing Real-time systems pattern recognition

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Is Synthetic Data all We Need? Benchmarking the Robustness of Models Trained with Synthetic Images

Is Synthetic Data all We Need? Benchmarking the Robustness o...

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

作者： Krishnakant Singh Thanush Navaratnam Jannik Holmer Simone Schaub-Meyer Stefan Roth Department of Computer Science TU Darmstadt Hessian.AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

A long-standing challenge in developing machine learning approaches has been the lack of high-quality labeled data. Recently, models trained with purely synthetic data, here termed synthetic clones, generated using large-scale pre-trained diffusion models have shown promising results in overcoming this annotation bottleneck. As these synthetic clone models progress, they are likely to be deployed in challenging real-world settings, yet their suitability remains understudied. Our work addresses this gap by providing the first benchmark for three classes of synthetic clone models, namely supervised, self-supervised, and multi-modal ones, across a range of robustness measures. We show that existing synthetic self-supervised and multi-modal clones are comparable to or outperform state-of-the-art real-image baselines for a range of robustness metrics – shape bias, background bias, calibration, etc. However, we also find that synthetic clones are much more susceptible to adversarial and real-world noise than models trained with real data. To address this, we find that combining both real and synthetic data further increases the robustness, and that the choice of prompt used for generating synthetic images plays an important part in the robustness of synthetic clones.

关键词： Measurement Analytical models Shape Noise Cloning Benchmark testing Robustness

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CUE-Net: Violence Detection Video Analytics with Spatial Cropping, Enhanced UniformerV2 and Modified Efficient Additive Attention

CUE-Net: Violence Detection Video Analytics with Spatial Cro...

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

作者： Damith Chamalke Senadeera Xiaoyun Yang Dimitrios Kollias Gregory Slabaugh School of Electronic Engineering and Computer Science Queen Mary University of London UK Queen Mary’s Digital Environment Research Institute (DERI) London UK Remark AI UK Limited London UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper we introduce CUE-Net, a novel architecture designed for automated violence detection in video surveillance. As surveillance systems become more prevalent due to technological advances and decreasing costs, the challenge of efficiently monitoring vast amounts of video data has intensified. CUE-Net addresses this challenge by combining spatial Cropping with an enhanced version of the UniformerV2 architecture, integrating convolutional and self-attention mechanisms alongside a novel Modified Efficient Additive Attention mechanism (which reduces the quadratic time complexity of self-attention) to effectively and efficiently identify violent activities. This approach aims to overcome traditional challenges such as capturing distant or partially obscured subjects within video frames. By focusing on both local and global spatiotemporal features, CUE-Net achieves state-of-the-art performance on the RWF-2000 and RLVS datasets, surpassing existing methods. The source code is available at 1 .

关键词： Convolutional codes Additives Attention mechanisms Visual analytics Source coding computer architecture Video surveillance

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Tracking and Counting Apples in Orchards Under Intermittent Occlusions and Low Frame Rates

Tracking and Counting Apples in Orchards Under Intermittent ...

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

作者： Gonçalo P. Matos Carlos Santiago João P. Costeira Ricardo L. Saldanha Ernesto M. Morgado SISCOG - Sistemas Congitivos SA Lisbon Portugal Institute for Systems and Robotics (ISR/IST) LARSyS Instituto Superior Técnico Univ. of Lisbon Portugal

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Estimating what will be the fruit yield in an orchard helps farmers to better plan the resources needed for harvesting, storing, and commercialising the crop, and also to take some agricultural decisions (like pruning) that may increase the quality of the yield and increase profits. Therefore, over the last years, several methods based on computer vision were proposed to automate this task, by directly counting the fruits on trees using a video camera. However, existing works and methods usually assume ideal conditions, and may fail under more challenging scenarios with unconstrained camera motion and intermittent occlusions of fruits. Here we show that combining Structure-from-Motion (SfM) with a bipartite graph matching has the potential to address those challenges. We found that our approach applied to real-world datasets, with unconstrained camera motion and low frame rates, outperforms existing methods by a large margin. Our results demonstrate that the proposed method is robust to multiple intermittent occlusions under challenging conditions, and thus suitable to be used in diverse real-world scenarios in orchards, either with a camera operated by hand or mounted on an agricultural vehicle. Although not shown here, we believe that the proposed method can also be applied to other object tracking problems besides counting fruits, under similar settings — i.e. static objects and a freely moving camera.

关键词： computer vision Visualization conferences Crops Cameras Trajectory pattern recognition

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