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检索条件"任意字段=Conference on Optomechatronic Computer-Vision Systems II"

共 985 条记录，以下是71-80 订阅

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Consistency-based Active Learning for Object Detection

Consistency-based Active Learning for Object Detection

引用

IEEE/CVF conference on computer vision and Pattern Recognition (CVPR)

作者： Yu, Weiping Zhu, Sijie Yang, Taojiannan Chen, Chen Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Active learning aims to improve the performance of the task model by selecting the most informative samples with a limited budget. Unlike most recent works that focus on applying active learning for image classification, we propose an effective Consistency-based Active Learning method for object Detection (CALD), which fully explores the consistency between the original and augmented data. CALD has three appealing benefits. (i) CALD is systematically designed by investigating the weaknesses of existing active learning methods, which do not take the unique challenges of object detection into account. (ii) CALD unifies box regression and classification with a single metric, which is not concerned with active learning methods for classification. CALD also focuses on the most informative local region rather than the whole image, which is beneficial for object detection. (iii) CALD not only gauges individual information for sample selection but also leverages mutual information to encourage a balanced data distribution. Extensive experiments show that CALD significantly outperforms existing state-of-the-art task-agnostic and detection-specific active learning methods on general object detection datasets. Based on the Faster R-CNN detector, CALD consistently surpasses the baseline method (random selection) by 2.9/2.8/0.8 mAP on average on PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO.

关键词： Learning systems Measurement computer vision conferences Object detection Detectors Pattern recognition

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Resolution-robust Large Mask In painting with Fourier Convolutions 22

Resolution-robust Large Mask In painting with Fourier Convol...

引用

22nd IEEE/CVF Winter conference on Applications of computer vision (WACV)

作者： Suvorov, Roman Logacheva, Elizaveta Mashikhin, Anton Remizova, Anastasia Ashukha, Arsenii Silvestrov, Aleksei Kong, Naejin Goka, Harshith Park, Kiwoong Lempitsky, Victor Samsung AI Ctr Moscow Moscow Russia Samsung Res Suwon South Korea Swiss Fed Inst Technol Lausanne EPFL Lausanne Switzerland Skolkovo Inst Sci & Technol Moscow Russia

ISBN: (纸本)9781665409155

Modern image inpainting systems, despite the significant progress, often struggle with large missing areas, complex geometric structures, and high-resolution images. We find that one of the main reasons for that is the lack of an effective receptive field in both the inpainting network and the loss function. To alleviate this issue, we propose a new method called large mask inpainting (LaMa). LaMa is based on i) a new inpainting network architecture that uses fast Fourier convolutions (FFCs), which have the imagewide receptive field;ii) a high receptive field perceptual loss;iii) large training masks, which unlocks the potential of the first two components. Our inpainting network improves the state-of-the-art across a range of datasets and achieves excellent performance even in challenging scenarios, e.g. completion of periodic structures. Our model generalizes surprisingly well to resolutions that are higher than those seen at train time, and achieves this at lower parameter&time costs than the competitive baselines. The code is available at https://***/saic-mdal/lama.

关键词： Convolutional codes Training computer vision Costs Computational modeling Network architecture Periodic structures

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Deep Reinforcement Learning Approach for Emergency Response Management 5

Deep Reinforcement Learning Approach for Emergency Response ...

引用

5th International conference on Intelligent systems and computer vision, ISCV 2022

作者： Elfahim, Omar Laoula, El Mehdi Ben Youssfi, Mohamed Barakat, Oussama Mestari, Mohammed University Hassan II of Casablanca 2IACS Laboratory ENSET Mohammedia Morocco University of Franche-Comté Nanomedicine Lab Besançon France

ISBN: (纸本)9781665495585

The emergency medical services sector must take the responsibility of increasing the efficiency of the emergency response and reducing the travel time of the patient. However, most existing studies on emergency medical services (EMS) management are suffering from modeling complex emergency responses process To solve this issue. In his paper, we developed a reinforcement learning model free approach to deal with emergency response scheme for emergency response for emergency medical facilities. In practical terms, we first formulate the Markov decision process for EMS which present the characteristics of state, reward function and the action space. then, designed deep reinforcement learning to find the optimal behaviors for emergency responses management to reach the location of emergency calls and pick up the patients to hospital timely. © 2022 IEEE.

关键词： Reinforcement learning

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Vehicle Door Safety System 2

Vehicle Door Safety System

引用

2nd International conference on Computational systems and Communication, ICCSC 2023

作者： Sivasankar, Abilash Kumaran, U.K. Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665493932

Carelessness and absence of mind leading to unseen obstacles or moving objects from passenger or driver inside the vehicle will make way for accidents to happen when they try to open the side door of the vehicle which will damage the door as well as harm any person or animal. The proposed system with the use of a camera and object detection will be able to detect vehicles and stationary objects that might hit the vehicle door when the passenger or driver tries to open the door without being aware of them. The system will alert the person inside the car so they can open the door in safe manner which prevents damage to the door. The object detection YOLO model is custom trained for the proposed system. Then based on this model object detection is performed using raspberry pi and camera setup. The camera is placed on the mirrors to cover both front and back door. The raspberry pi is remotely connected to laptop for visualizing the detection of objects which will damage the door. The system uses the computer vision concept and objects detection for ensuring the safety of vehicle door. © 2023 IEEE.

关键词： Object detection

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Pixel-Level GPS Localization and Denoising using computer vision and 6G Communication Beams

Pixel-Level GPS Localization and Denoising using Computer Vi...

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2024 IEEE Global Communications conference, GLOBECOM 2024

作者： Charan, Gouranga Osman, Tawfik Alkhateeb, Ahmed Arizona State University United States

ISBN: (纸本)9798350351255

Accurate localization is crucial for various applications, including autonomous vehicles and next-generation wireless networks. However, the reliability and precision of Global Navigation Satellite systems (GNSS), such as the Global Positioning System (GPS), are compromised by multi-path errors and non-line-of-sight scenarios. This paper presents a novel approach to enhance GPS accuracy by combining visual data from RGB cameras with wireless signals captured at millimeter-wave (mmWave) and sub-terahertz (sub-THz) basestations. We propose a sensing-aided framework for (i) site-specific GPS data characterization and (ii) GPS position de-noising that utilizes multi-modal visual and wireless information. Our approach is validated in a realistic Vehicle-to-Infrastructure (V2I) scenario using a comprehensive real-world dataset, demonstrating a substantial reduction in localization error to sub-meter levels. This method represents a significant advancement in achieving precise localization, particularly beneficial for high-mobility applications in 5G and beyond networks. © 2024 IEEE.

关键词： Global positioning system

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UViM: A Unified Modeling Approach for vision with Learned Guiding Codes 36

UViM: A Unified Modeling Approach for Vision with Learned Gu...

引用

36th conference on Neural Information Processing systems (NeurIPS)

作者： Kolesnikov, Alexander Pinto, Andre Susano Beyer, Lucas Zhai, Xiaohua Harmsen, Jeremiah Houlsby, Neil Google Res Brain Team Zurich Zurich Switzerland

ISBN: (纸本)9781713871088

We introduce UViM, a unified approach capable of modeling a wide range of computer vision tasks. In contrast to previous models, UViM has the same functional form for all tasks;it requires no task-specific modifications which require extensive human expertise. The approach involves two components: (I) a base model (feed-forward) which is trained to directly predict raw vision outputs, guided by a learned discrete code and (ii) a language model (autoregressive) that is trained to generate the guiding code. These components complement each other: the language model is well-suited to modeling structured interdependent data, while the base model is efficient at dealing with high-dimensional outputs. We demonstrate the effectiveness of UViM on three diverse and challenging vision tasks: panoptic segmentation, depth prediction and image colorization, where we achieve competitive and near state-of-the-art results. Our experimental results suggest that UViM is a promising candidate for a unified modeling approach in computer vision.

关键词： computer vision

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WebUI: A Dataset for Enhancing Visual UI Understanding with Web Semantics 23

WebUI: A Dataset for Enhancing Visual UI Understanding with ...

引用

CHI conference on Human Factors in Computing systems (CHI)

作者： Wu, Jason Wang, Siyan Shen, Siman Peng, Yi-Hao Nichols, Jefrey Bigham, Jefrey P. Carnegie Mellon Univ HCI Inst Pittsburgh PA USA Wellesley Coll Wellesley MA USA Grinnell Coll Grinnell Grinnell IA USA Snooty Bird LLC San Diego CA USA

ISBN: (纸本)9781450394215

Modeling user interfaces (UIs) from visual information allows systems to make inferences about the functionality and semantics needed to support use cases in accessibility, app automation, and testing. Current datasets for training machine learning models are limited in size due to the costly and time-consuming process of manually collecting and annotating UIs. We crawled the web to construct WebUI, a large dataset of 400,000 rendered web pages associated with automatically extracted metadata. We analyze the composition ofWebUI and show that while automatically extracted data is noisy, most examples meet basic criteria for visual UI modeling. We applied several strategies for incorporating semantics found in web pages to increase the performance of visual UI understanding models in the mobile domain, where less labeled data is available: (i) element detection, (ii) screen classifcation and (iii) screen similarity.

关键词： Dataset UI Modeling computer vision Transfer Learning Web Semantics Computational Interaction

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SMART Virtual Painter and Mouse System to Improve Online Education with computer vision and Deep Learning Features 4

SMART Virtual Painter and Mouse System to Improve Online Edu...

引用

4th International conference on Emerging systems and Intelligent Computing, ESIC 2024

作者： Maharana, Swetanjali Singh, Raj Kumar Baliyar Priya, M Preeti Aswini, M. Computer Science and Engineering Institute Park Palur Hills Odisha Berhampur761008 India

ISBN: (纸本)9798350349856

The world of digitization and technological breakthroughs brings about significant changes in business, education, living styles, daily activities, the medical field, and the food industry. The author of this paper presents a novel approach for the computerized academic era in which various obstacles, such as sudden illnesses, natural disasters, and viruses, are not viewed as obstacles but rather serve as inspiration for the development of innovative techniques that improve remote learning and virtual learning. The main goal is to do away with the requirement for physical devices by employing hand gestures to manage the mouse's functions and position of the cursor. It is implemented using Python and OpenCV, and it uses a webcam as the input device. In order to manage the movement of the cursor and initiate click events, the system monitors hand gestures, extracts characteristics from the hand region, and classes these features. The goal of the suggested system is to offer a practical and affordable alternative for human-computer interaction, especially for those who are physically limited. Mouse activities are made possible by this technology, which recognizes and follows the movements of the pointer on the screen. In a similar vein, the virtual Painter's goal is to generate a virtual drawing canvas using human fingertips as colour markers and an interface that enables morphological actions between the user and the system. The following sections include the presentation of the technological components, execution, analysis of several associated research factors, and results: section 1 gives brief idea about the introduction and motivation to do this specific research, section ii describes the related methods. Section iiI presents the proposed method, section V shows the implementation of proposed work and concluding part represented by section VI. © 2024 IEEE.

关键词： E-learning

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Classification of Eye Disorders Using Deep Learning and Machine Learning Models 2nd

Classification of Eye Disorders Using Deep Learning and Mach...

引用

2nd International conference on Advances in Computing Research, ACR 2024

作者： El Harti, Manal Zaamoun, Saad Andaloussi, Said Jai Ouchetto, Ouail Department of Mathematics and Computer Science Faculty of Science Ain Chock Hassan II University Casablanca Morocco Faculty of Juridical Economic and Social Sciences Ain-Chock Hassan II University Casablanca Morocco

ISBN: (纸本)9783031569494

Early diagnosis and screening of eye disorders are crucial for effective treatment. However, with the increasing prevalence of these conditions and a shortage of ophthalmic specialists, it is imperative to employ automated image evaluation methods to ensure consistent diagnoses and address this growing concern. Previous studies have focused on identifying eye conditions such as glaucoma, diabetic retinopathy, and cataracts using a single computer vision pipeline. In our research, we propose a hybrid approach that combines a classical neural network with four machine-learning models to classify these ophthalmic disorders. Our approach uses the pre-trained VGG16 model from the ImageNet dataset as a feature extractor. We then utilize the extracted features as input for the machine-learning models. To carry out this research, we harnessed a publicly available Kaggle database comprising images from various sources, including the Indian Diabetic Retinopathy Image Dataset, Oculur recognition, retinal datasets, and others. Finally, we conducted a comprehensive comparison of the results obtained with those from the top-rated deep learning models, to select the optimal model for diagnosing dry eye disease, which is our ultimate goal. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diagnosis

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Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective

Towards Robust and Adaptive Motion Forecasting: A Causal Rep...

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IEEE/CVF conference on computer vision and Pattern Recognition (CVPR)

作者： Liu, Yuejiang Cadei, Riccardo Schweizer, Jonas Bahmani, Sherwin Alahi, Alexandre Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Learning behavioral patterns from observational data has been a de facto approach to motion forecasting. Yet, the current paradigm suffers from two shortcomings: brittle under distribution shifts and inefficient for knowledge transfer. In this work, we propose to address these challenges from a causal representation perspective. We first introduce a causal formalism of motion forecasting, which casts the problem as a dynamic process with three groups of latent variables, namely invariant variables, style confounders, and spurious features. We then introduce a learning framework that treats each group separately: (i) unlike the common practice mixing datasets collected from different locations, we exploit their subtle distinctions by means of an invariance loss encouraging the model to suppress spurious correlations;(ii) we devise a modular architecture that factorizes the representations of invariant mechanisms and style confounders to approximate a sparse causal graph;(iii) we introduce a style contrastive loss that not only enforces the structure of style representations but also serves as a self-supervisory signal for test-time refinement on the fly. Experiments on synthetic and real datasets show that our proposed method improves the robustness and reusability of learned motion representations, significantly outperforming prior state-of-the-art motion forecasting models for out-of-distribution generalization and low-shot transfer.

关键词： Representation learning Learning systems Correlation Dynamics computer architecture Predictive models Robustness

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