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检索条件"任意字段=IEEE Workshop on Visualization and Machine Vision"

共 120 条记录，以下是1-10 订阅

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Neural Network Interpretability with Layer-Wise Relevance Propagation: Novel Techniques for Neuron Selection and visualization 15

Neural Network Interpretability with Layer-Wise Relevance Pr...

引用

15th ieee Annual Computing and Communication workshop and Conference, CCWC 2025

作者： Bhati, Deepshikha Neha, Fnu Amiruzzaman, Md. Guercio, Angela Shukla, Deepak Kumar Ward, Ben Kent State University KentOH United States West Chester University West ChesterPA United States Rutgers University NewarkNJ United States

ISBN: (纸本)9798331507695

Interpreting complex neural networks is crucial for understanding their decision-making processes, particularly in applications where transparency and accountability are essential. This proposed method addresses this need by focusing on Layer-wise Relevance Propagation (LRP), a technique used in explain-able artificial intelligence (XAI) to attribute neural network outputs to input features through backpropagated relevance scores. Existing LRP methods often struggle with precision in evaluating individual neuron contributions. To overcome this limitation, we present a novel approach that improves the parsing of selected neurons during LRP backward propagation, using the Visual Geometry Group 16 (VGG16) architecture as a case study. Our method creates neural network graphs to highlight critical paths and visualizes these paths with heatmaps, optimizing neuron selection through accuracy metrics like Mean Squared Error (MSE) and Symmetric Mean Absolute Percentage Error (SMAPE). Additionally, we utilize a deconvolutional visualization technique to reconstruct feature maps, offering a comprehensive view of the network's inner workings. Extensive experiments demonstrate that our approach enhances interpretability and supports the development of more transparent artificial in-telligence (AI) systems for computer vision applications. This advancement has the potential to improve the trustworthiness of AI models in real-world machine vision applications, thereby increasing their reliability and effectiveness. © 2025 ieee.

关键词： Mean square error

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Immersive Auditory-Visual Real-Time Avatar System of ANA Avatar XPRIZE Finalist Avatar-Hubo

Immersive Auditory-Visual Real-Time Avatar System of ANA Ava...

引用

ieee International Conference on Advanced Robotics and Its Social Impacts (ARSO)

作者： Vaz, Jean Chagas Dave, Akshay Kassai, Nathan Kosanovic, Nicolas Oh, Paul Y. Univ Nevada Las Vegas UNLV Dept Mech Engn Las Vegas NV 89119 USA Univ Nevada Las Vegas UNLV Fac Mech Engn Las Vegas NV 89119 USA

ISBN: (纸本)9781665479660

The need for real-time high-quality audio-visual systems skyrocketed with the advent of the ANA Avatar XPRIZE, a worldwide Avatar Robotic System competition. Avatar-Hubo scored 6th amongst teams worldwide;due to its humanoid characteristics, an operator is able to embody the robot. Therefore through vision, audio, and motion, one may transport their presence to another location. However, many telepresence setups suffer from high latency and poor depth perception. In this paper, both the latency and visual acuity of LiDAR and dual-camera stereoscopic vision systems were analyzed for Avatar-Hubo's ANA Avatar XPRIZE semifinals setup. This analysis found that the LiDAR camera significantly underperformed in terms of Mean Data Transmission Rate when compared to the humanoid's newly proposed vision-audio system. Additionally, trials with 17 participants during several System Usability Scale (SUS) testing found that Avatar-Hubo's real-time auditory-visual system produced effective depth perception, allowing users to determine object proximity.

关键词： visualization Laser radar Telepresence Avatars Stereo image processing machine vision Humanoid robots

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Neural Network Interpretability with Layer-Wise Relevance Propagation: Novel Techniques for Neuron Selection and visualization

Neural Network Interpretability with Layer-Wise Relevance Pr...

引用

ieee Annual Computing and Communication workshop and Conference (CCWC)

作者： Deepshikha Bhati Fnu Neha Md Amiruzzaman Angela Guercio Deepak Kumar Shukla Ben Ward Kent State University Kent Ohio USA West Chester University West Chester PA USA Rutgers University Newark NJ USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

关键词： Measurement Heating systems Geometry visualization Explainable AI machine vision Neurons Focusing Reliability Biological neural networks

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Using machine vision for Fault Detection of Dry Resist Top Film Peeling : Topic/category: Advanced Process Control

Using Machine Vision for Fault Detection of Dry Resist Top F...

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ieee/SEMI Conference and workshop on Advanced Semiconductor Manufacturing

作者： Cameron Wian Richard Good Tobias Kraeusslein Joerg Paufler Richard Maxwell GlobalFoundries/ WPI GlobalFoundries

ISBN: (数字)9798331531850

ISBN: (纸本)9798331531867

While peeling the polyethylene film off a dry film resist, a common fault is that the film tears and some of the film remains on the wafer. Such a fault is often not identified by existing tool sensors so that costly and time-consuming visual verification and/or macro-inspection metrology must be used. This paper discusses a fault detection approach which uses a low-cost video stream along with machine vision technology to identify peeler faults. Furthermore, machine vision is used to infer tool events so that process sensorization and robust fault detection is possible without extensive and risky equipment integration.

关键词： Fault diagnosis visualization Polyethylene machine vision Fault detection Process control Resists Streaming media Semiconductor device manufacture Sensors

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Hyperdimensional Representation for Adaptive Information Association and Memorization

Hyperdimensional Representation for Adaptive Information Ass...

引用

ieee workshop on Applications of Computer vision (WACV)

作者： Zhuowen Zou Prathyush Poduval Narayan Srinivasa Mohsen Imani University of California Irvine Irvine CA Intel Labs Thousand Oaks California

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Many computer vision applications rely on interpretable machine learning algorithms to analyze the data collected from various sources. We leverage Hyperdimensional Computing (HDC) as an innovative computational model that mimics key brain functionalities to achieve efficient and robust cognitive learning. We propose HDlm, a novel HDC-based cognitive representation capable of adaptive information association and memorization. HDlm first theoretically expands the HDC mathematics to support selective information association and adaptive memorization. Then, it exploits the proposed operations to support cognitive operations, including set membership, information retrieval, and item comparison. We evaluated our solution for a selection of applications related to visual data representation and sequence matching analysis. Our evaluation shows that HDlm provides more adaptive similarity metrics between objects that lead to better task performance.

关键词： Measurement Computer vision visualization Sequential analysis machine learning algorithms MIMICs Memory Genomics Information retrieval Mathematical models

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Compression of Self-Supervised Representations for machine vision

Compression of Self-Supervised Representations for Machine V...

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ieee workshop on Multimedia Signal Processing

作者： Zhihao Duan Fengqing M. Zhu Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette IN U.S.A.

ISBN: (数字)9798350387254

ISBN: (纸本)9798350387261

Coding for machines is an emerging research topic aiming at compressing visual signals for machine analysis. Most existing research on it consider supervised feature compression, i.e., compressing features for a particular task and dataset. This paper explores a more flexible scenario in which the machine vision task (e.g., the classes to predict) is unknown during training and encoding (i.e., only decided during decoding). To achieve this goal, we compress self-supervised learning (SSL) features, which are not tied to a particular dataset but can be used for various tasks without re-training. Empirical studies are provided to analyze and derive an SSL feature compression system. Despite its simplicity, we show that linear transform coding achieves comparable or better rate-accuracy performance for SSL features compared to more advanced techniques. Although SSL feature compression performs slightly worse than its supervised counterpart, it generalizes well for out-of-distribution datasets. We highlight the use cases of various feature compression schemes and provide insights into developing future work. The code is made publicly available 1 1 https://***/viper-purdue/dino-compress.

关键词： Training visualization Quantization (signal) machine vision Conferences Transform coding Self-supervised learning Distortion Encoding Decoding

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People, cracks, stairs, and doors: vision-based semantic mapping with a quadruped robot supporting first responders in Search & Rescue

People, cracks, stairs, and doors: vision-based semantic map...

引用

ieee International workshop on Robot and Human Communication (ROMAN)

作者： Zoe Betta Carmine Tommaso Recchiuto Antonio Sgorbissa RICE Lab University of Genova Genova Italy RAISE Ecosystem Genova Italy

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

This study introduces a system implemented on a legged robot, designed to generate a multi-layered map that incorporates semantic information, specifically tailored for Search & Rescue robotics. The article discusses the development of a machine Learning model based on visual data for recognizing people and environmental features, and its integration into a mapping and navigation architecture. The system was tested in two different locations using the Spot robot by Boston Dynamics, equipped with an external ZED2 depth camera. Tests are described in detail and results analyzed.

关键词： Legged locomotion visualization Semantics Robot vision systems machine learning Stairs Robustness Quadrupedal robots Robots Testing

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Detective Networks: Enhancing Disaster Recognition in Images Through Attention Shifting Using Optimal Masking

Detective Networks: Enhancing Disaster Recognition in Images...

引用

ieee workshop on Applications of Computer vision (WACV)

作者： Narongthat Thanyawet Photchara Ratsamee Yuki Uranishi Haruo Takemura Osaka University Osaka Japan Osaka Institute of Technology Osaka Japan

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Aerial investigation is used for surveying damage and identifying post-disaster events through imagery data. However, the challenge lies in detecting disaster-related areas within aerial or shipborne images, as these can appear as minor regions, making recognition difficult. To address this challenge, we introduce the Detective Network (DeNet), designed to optimally mask images, thereby shifting the attention of machine learning models towards these small yet crucial regions. Utilizing the concepts of patch and anchor box, DeNet incorporates a masking candidate layer and a masking layer to facilitate optimal masking. Our experimental findings are compelling; by preprocessing images with DeNet before analysis using an image captioning model, we achieved a remarkable accuracy of 92.91% in landslide detection from side-view image captions and 87.50% for shipborne view detection. The result demonstrates the efficacy of DeNet in enhancing the recognition of disaster-related areas in challenging imaging conditions.

关键词： visualization Computer vision Image recognition Accuracy Target recognition Disasters Imaging machine learning Terrain factors Decoding

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Advancing Chart Question Answering with Robust Chart Component Recognition

Advancing Chart Question Answering with Robust Chart Compone...

引用

ieee workshop on Applications of Computer vision (WACV)

作者： Hanwen Zheng Sijia Wang Chris Thomas Lifu Huang Virginia Tech University of California Davis

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Chart comprehension presents significant challenges for machine learning models due to the diverse and intricate shapes of charts. Existing multimodal methods often over-look these visual features or fail to integrate them effectively for Chart Question Answering. To address this, we introduce CHARTFORMER, a unified framework that enhances chart component recognition by accurately identifying and classifying components such as bars, lines, pies, titles, legends, and axes. Additionally, we propose a novel Question-guided Deformable Co-Attention (QDCAt) mechanism, which fuses chart features encoded by Chart-former with the given question, leveraging the question's guidance to ground the correct answer. Extensive experiments demonstrate a 3.2% improvement in mAP over the baselines for chart component recognition. For ChartQA and OpenCQA tasks, our approach achieves improvements of 15.4% in accuracy and 0.8 in BLEU score, respectively, underscoring the robustness of our solution for detailed visual data interpretation across various applications. 1 1 The source code and dataset are publicly available at https://***/VT-NLP/chartQA

关键词： Instance segmentation visualization Computer vision Shape Source coding Computer network reliability machine learning Feature extraction Question answering (information retrieval) Robustness

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Design-O-Meter: Towards Evaluating and Refining Graphic Designs

Design-O-Meter: Towards Evaluating and Refining Graphic Desi...

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ieee workshop on Applications of Computer vision (WACV)

作者： Sahil Goyal Abhinav Mahajan Swasti Mishra Prateksha Udhayanan Tripti Shukla K J Joseph Balaji Vasan Srinivasan IIT Roorkee IIIT Bangalore Adobe Research

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Graphic designs are an effective medium for visual communication. They range from greeting cards to corporate flyers and beyond. Off-late, machine learning techniques are able to generate such designs, which accelerates the rate of content production. An automated way of evaluating their quality becomes critical. Towards this end, we introduce Design-o-meter, a data-driven methodology to quantify the goodness of graphic designs. Further, our approach can suggest modifications to these designs to improve its visual appeal. To the best of our knowledge, Design-o-meter is the first approach that scores and refines designs in a unified framework despite the inherent subjectivity and ambiguity of the setting. Our exhaustive quantitative and qualitative analysis of our approach against baselines adapted for the task (including recent Multimodal LLM-based approaches) brings out the efficacy of our methodology. We hope our work will usher more interest in this important and pragmatic problem setting. Project Page: ***/Design-o-meter.

关键词： visualization Computer vision Visual communication Refining Layout Production machine learning Pragmatics

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