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vision-Based Muscular Fatigue Detection for Safe Human-robot Collaboration: Evaluation of a Pilot Study 16th

Vision-Based Muscular Fatigue Detection for Safe Human-Robo...

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16th International conference on Social robotics, ICSR + AI 2024

作者： Jirak, Doreen Belgiovine, Giulia Eldardeer, Omar Sciutti, Alessandra Rea, Francesco Via Enrico Melen Genoa16152 Italy

ISBN: (纸本)9789819635245

As industrial automation progresses, collaborative human-robot interaction (cHRI) is becoming more prevalent, making it essential to understand the factors influencing these interactions. One key factor is physical human fatigue, which reduces muscular force capacity and impacts both productivity and safety. While recent research emphasizes wearable sensors for direct measurement, non-intrusive camera-based approaches remain underexplored, despite their potential for more natural fatigue tracking. this paper examines visual cues, such as body posture and facial expressions, to assess their effectiveness in classifying fatigue states in cHRI. We demonstrate that a simple long short-term memory (LSTM) network performs well, particularly with posture data, and that facial features provide further insights into fatigue onset. Our findings suggest a social dimension, where robots that recognize and respond to human fatigue could enhance collaboration in industrial environments. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Industrial robots

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Causal-SETR: A SEgmentation TRansformer Variant Based on Causal Intervention 1

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16th Asian conference on computer vision (ACCV)

作者： Li, Wei Li, Zhixin Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China

ISBN: (数字)9783031262937

ISBN: (纸本)9783031262920;9783031262937

We present a novel SEgmentaion TRansformer variant based on causal intervention. It serves as an improved vision encoder for semantic segmentation. Many studies have proved that vision transformers (ViT) can achieve a competitive benchmark on these downstream tasks, which shows that they can learn feature representations well. In other words, it is good at observing the instance from the image. However, in the human visual system, to recognize the objects in the scene, it is necessary to observe the objects themselves and introduce some prior knowledge for producing higher confidence results. Inspired by this, we introduced a structural causal model (SCM) to model images, category labels, and context. Beyond observing, we propose a causal intervention method by removing the confounding bias of global context and plugging it in the ViT encoder. Unlike other sequence-to-sequence prediction tasks, we use causal intervention instead of likelihood. Besides, the proxy training objective of the framework is to predict the contextual objects of a region. Finally, we combine this encoder with the segmentation decoder. Experiments show that our proposed method is flexible and effective.

关键词： Causal intervention vision transformer Semantic segmentation

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Fourth Workshop on Recommender Systems in Fashion and Retail-fashionXrecsys2022 22

Fourth Workshop on Recommender Systems in Fashion and Retail...

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16th ACM conference on Recommender Systems (RecSys)

作者： Pampin, Humberto Jesus Corona Shirvany, Reza Spotify Amsterdam Netherlands Zalando SE Berlin Germany

ISBN: (纸本)9781450392785

Online Fashion retailers have significantly increased in popularity over the last decade, making it possible for customers to explore hundreds of thousands of products without the need to visit multiple stores or stand in long queues for checkout. Recommender Systems are often used to solve different complex problems in this domain, such as social fashion-aware recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. Moreover, the research interest on this area is increasing, demonstrated by the success of the past three editions of the fashionXrecsys Workshops 2019-21. the Fourth edition of the workshop aims at providing an avenue for continuing the discussion of novel approaches and applications of recommendation systems in fashion and e-commerce with a particular focus on pandemic era events and their short and long lasting effects on e-commerce and Fashion.

关键词： Recommendations Fashion E-Commerce Social Networks Deep Learning computer vision Outfit Sizing Fit

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EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network 16th

EPSANet: An Efficient Pyramid Squeeze Attention Block on Con...

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16th Asian conference on computer vision (ACCV)

作者： Zhang, Hu Zu, Keke Lu, Jian Zou, Yuru Meng, Deyu Shenzhen Univ Coll Math & Stat Shenzhen Key Lab Adv Machine Learning & Applicat Shenzhen Peoples R China Natl Ctr Appl Math Shenzhen NCAMS Shenzhen Peoples R China Xi An Jiao Tong Univ Xian Peoples R China Macau Univ Sci & Technol Macau Peoples R China

ISBN: (纸本)9783031263125;9783031263132

Recently, it has been demonstrated that the performance of a deep convolutional neural network can be effectively improved by embedding an attention module into it. In this work, a novel lightweight and effective attention method named Pyramid Squeeze Attention (PSA) module is proposed. By replacing the 3x3 convolution with the PSA module in the bottleneck blocks of the ResNet, a novel representational block named Efficient Pyramid Squeeze Attention (EPSA) is obtained. the EPSA block can be easily added as a plug-and-play component into a well-established backbone network, and significant improvements on model performance can be achieved. Hence, a simple and efficient backbone architecture named EPSANet is developed in this work by stacking these ResNet-style EPSA blocks. Correspondingly, a stronger multi-scale representation ability can be offered by the proposed EPSANet for various computer vision tasks including but not limited to, image classification, object detection, instance segmentation, etc. Without bells and whistles, the performance of the proposed EPSANet outperforms most of the state-of-the-art channel attention methods. As compared to the SENet-50, the Top-1 accuracy is improved by 1.93% on ImageNet dataset, a larger margin of +2.7 box AP for object detection and an improvement of +1.7 mask AP for instance segmentation by using the Mask-RCNN on MS-COCO dataset are obtained.

关键词： computer vision Attention module

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A²: Adaptive Augmentation for Effectively Mitigating Dataset Bias 16th

A<SUP>2</SUP>: Adaptive Augmentation for Effectively Mitigat...

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16th Asian conference on computer vision (ACCV)

作者： An, Jaeju Kim, Taejune Ko, Donggeun Lee, Sangyup Woo, Simon S. Sungkyunkwan Univ Dept Comp Sci & Engn Seoul South Korea Sungkyunkwan Univ Dept Artificial Intelligence Seoul South Korea Sungkyunkwan Univ Dept Appl Artificial Intelligence Seoul South Korea Sungkyunkwan Univ Dept Appl Data Sci Seoul South Korea

ISBN: (纸本)9783031262920;9783031262937

Recently, deep neural networks (DNNs) have become the de facto standard to achieve outstanding performances and demonstrate significant impact on various computer vision tasks for real-world scenarios. However, the trained networks can often suffer from overfitting issues due to the unintended bias in a dataset causing inaccurate, unreliable, and untrustworthy results. thus, recent studies have attempted to remove bias by augmenting the bias-conflict samples to address this challenge. Yet, it still remains a challenge since generating bias-conflict samples without human supervision is generally difficult. To tackle this problem, we propose a novel augmentation framework, Adaptive Augmentation (A(2)), based on a generative model that help classifiers learn debiased representations. Our framework consists of three steps: 1) extracting bias-conflict samples from a biased dataset in an unsupervised manner, 2) training a generative model with the biased dataset and adapting the learned biased distribution to the extracted bias-conflict samples' distribution, and 3) augmenting bias-conflict samples by translating bias-align samples. therefore, our classifier can effectively learn the debiased representation without human supervision. Our extensive experimental results demonstrate that A(2) effectively augments bias-conflict samples, mitigating widespread bias issues. the code is available in here (https://***/anjaeju/A(2)- AdaptiveAugmentation-for-Effectively-Mitigating-Dataset-Bias).

关键词： computer vision Debiasing Image translation

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Design of Programmable Parallel vision Processor 16

Design of Programmable Parallel Vision Processor

引用

16th IEEE International conference on Solid-State and Integrated Circuit Technology, ICSICT 2022

作者： Yu, Shuangming Liu, Liyuan Wu, Nanjian Institute of Semiconductors Chinese Academy of Sciences State Key Laboratory of Superlattices and Microstructures Beijing100083 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (数字)9781665469067

ISBN: (纸本)9781665469067

vision processor is favorable for edge computing scenarios due to its advantages of high computing efficiency and low power consumption. It can operate various image processing algorithms on a single chip. this paper firstly overviews the progress of vision processor researches in decades. then, the paper introduces a design of programmable parallel vision processor architecture, which can flexibly implement both computer vision algorithms and deep learning neural network algorithms. A vision processing enhanced RISC instruction set is designed to accelerate the execution of deep learning algorithms. Moreover, this paper introduces a set of algorithm optimization methods from an algorithm/hardware co-design perspective, and deploys algorithms on the vision processor efficiently. © 2022 IEEE.

关键词： Deep learning

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Patch Embedding as Local Features: Unifying Deep Local and Global Features via vision Transformer for Image Retrieval 16th

Patch Embedding as Local Features: Unifying Deep Local and...

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16th Asian conference on computer vision, ACCV 2022

作者： Phan, Lam Nguyen, Hiep thi Hong Warrier, Harikrishna Gupta, Yogesh HCL Technologies Hanoi Viet Nam

ISBN: (纸本)9783031262838

Image retrieval is the task of finding all images in the data-base that are similar to a query image. Two types of image representations have been studied to address this task: global and local image features. those features can be extracted separately or jointly in a single model. State-of-the-art methods usually learn them with Convolutional Neural Networks (CNNs) and perform retrieval with multi-scale image representation. this paper’s main contribution is to unify global and local features with vision Transformers (ViTs) and multi-atrous convolutions for high-performing retrieval. We refer to the new model as ViTGaL, standing for vision Transformer based Global and Local features (ViTGaL). Specifically, we add a multi-atrous convolution to the output of the transformer encoder layer of ViTs to simulate the image pyramid used in standard image retrieval algorithms. We use class attention to aggregate the token embeddings output from the multi-atrous layer to get both global and local features. the entire network can be learned end-to-end, requiring only image-level labels. Extensive experiments show the proposed method outperforms the state-of-the-art methods on the Revisited Oxford and Paris datasets. Our code is available at here © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image representation

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Clothing Parsing using Extended U-Net 16

Clothing Parsing using Extended U-Net

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16th International Joint conference on computer vision, Imaging and computer Graphics theory and Applications (VISIGRAPP) / 16th International conference on computer vision theory and Applications (VISAPP)

作者： Vozarikova, Gabriela Stana, Richard Semanisin, Gabriel Pavol Jozef Safarik Univ Kosice Inst Comp Sci Jesenna 5 Kosice Slovakia

ISBN: (纸本)9789897584886

this paper focuses on the task of clothing parsing, which is a special case of the more general object segmentation task well known in the field of computer vision. Each pixel is to be assigned to one of the clothing categories or background. Due to complexity of the problem and lack of data (until recently) performance of the modern state-of-the-art clothing parsing models expressed in terms of mean Intersection over Union metric (IoU) does not exceed 55%. In this paper, we propose a novel multitask network by extending fully-convolutional neural network U-Net with two side branches - one solves a multilabel classification task and the other predicts bounding boxes of clothing instances. We trained this network using a large-scaled iMaterialist dataset (Visipedia, 2019), which we refined. Compared to well performing segmentation architectures FPN, DeepLabV3, DeepLabV3+ and plain U-Net, our model achieves the best experimental results.

关键词： U-Net Clothing Parsing Segmentation computer vision Multitask Learning Deep Learning Fully-convolutional Network

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Building A Face Database Of Arab Faces Toward Evaluating Bias In Facial Analysis Systems 17

Building A Face Database Of Arab Faces Toward Evaluating Bia...

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17th International conference on Interfaces and Human computer Interaction 2023, IHCI 2023, the 17th International conference on computer Graphics, Visualization, computer vision and Image Processing 2023, CGVCVIP 2023 and 16th International conference on Game and Entertainment Technologies 2023, GET 2023

作者： Khalil, Ashraf Glal, Suha Ahmed, Khider Khan, Sana Zeb Abdulgani, Aysha College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates Mechatronics Engineering Graduate Program American university of Sharjah Sharjah United Arab Emirates Department of Computer Science and Engineering American university of Sharjah Sharjah United Arab Emirates United Arab Emirates University Al Ain United Arab Emirates

ISBN: (纸本)9789898704498

Machine learning algorithms are fundamentally driven by the data provided by humans;consequently, the decisions made by those algorithms are not free from human bias. this is particularly evident in the case of facial analysis systems that employ machine learning algorithms. Recent studies have shown that the decisions made by many of the commercially available facial analysis systems are prejudiced against certain groups of race, ethnicity, age, gender and culture. Further studies have identified that the underlying reason for such biased decisions is that the open source material available for facial image databases which are used in commerce and academia to train the algorithms has meager diversity in these categories. To compound this issue, facial analysis technology is promoted by influential companies and artificial intelligence service providers without affirming the fairness and accuracy of the decisions given by these systems. To minimize bias and ensure representation of the Middle Eastern population in the imminent growth of this technology, we propose the development of two Arab face databases along with an algorithmic audit involving seven commercially available facial analysis systems. Of the databases, the first, Arab-LEANA, will include 300 Arab subjects' face images with variation in lighting, expression, accessory, nationality and age (LEANA). the second, Arab Public Figures Faces (APFF), will contain images and videos of 300 Arab public figures captured "in the wild". Faces for APFF will be selected manually from the internet since manual selection of faces will result in a high degree of variability in scale, pose, expression, illumination, age, occlusion and make-up. these databases will provide the worldwide community of face recognition researchers with a large-scale, diverse collection of Arab face images for training and evaluating algorithms toward developing a more representative, and therefore more robust, capacity for facial analysis. T

关键词： Machine learning

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A Binocular vision Based Intelligent Upper Limb Exoskeleton for Grasp Assisting 16th

A Binocular Vision Based Intelligent Upper Limb Exoskeleton ...

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16th International conference on Intelligent robotics and Applications, ICIRA 2023

作者： Li, Yiyou Zhao, Liugang Huang, Sen Chen, Xinzhili Yuan, Dong Liu, Minchao Qin, Xiaodong Hu, Fangcao Wang, Changhong Zhao, Qiusheng Li, Bo Chongqing University of Technology NJ Chongqing400000 China

ISBN: (纸本)9789819964857

Assisting patients with upper limb motor dysfunction to complete activities of daily living can improve their quality of life. this paper develops a binocular vision-based upper limb exoskeleton to assist grasping. the mechanism adopts a modular design to reduce the complexity of the joint. the cable-driven actuators were used to improve system compliance. A binocular vision-based detection and location system was developed, which adopted a depth camera and deep learning detection algorithm to identify and locate objects. the frame of the target object detected by the master computer was sent to the trajectory controller, then the trajectory controller controlled the cable-driven actuator to approach the target object by calculating the inverse kinematics of the multi-joint mechanism. Grasping experiments demonstrated that the system could effectively assist patients with upper limb dysfunction in recognizing and locating target objects. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Deep learning

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