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Multi-Modal Large Language Model Driven Augmented Reality Situated Visualization: The Case of Wine recognition

Multi-Modal Large Language Model Driven Augmented Reality Si...

引用

ieee Winter Applications and computer vision workshops (WACVW)

作者： Vincenzo Armandi Andrea Loretti Lorenzo Stacchio Pasquale Cascarano Gustavo Marfia Department of the Arts University of Bologna Bologna Italy Department of Political Sciences Communication and International Relations University of Macerata Macerata Italy

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Situated Visualizations (SV) and reality-based information retrieval systems, enhanced by Mixed Reality (MR) and Augmented Reality (AR), enable the overlay of digital information onto real-world objects, providing context-aware content through computer vision. Despite their potential, these systems face significant challenges in scalability and adaptability, particularly for domains like wine recognition, where diverse label designs, frequent updates, and limited historical databases complicate automated analysis. SOLLAMA (SOmmeLier LlAMA) is a novel wine recognition framework designed to address the scalability and adaptability challenges of AR systems in recognizing diverse wine labels. Leveraging Multimodal Large Language Models (MLLMs), SOLLAMA integrates visual and textual cues for accurate label interpretation, bypassing the need for extensive image datasets and traditional OCR methods. Built on the Augmented Wine recognition (AWR) system, it replaces the OCR module with LLAMA 3.2 for advanced text recognition and contextual understanding. Key benefits include scalability across diverse designs and simplified, server-free deployment. Experimental validation on a dataset of wine labels from Italy's Emilia-Romagna region highlights the system's effectiveness and its potential to transform wine recognition in AR-based applications.

关键词： Visualization computer vision Accuracy Scalability Large language models Face recognition Optical character recognition Information retrieval Robustness Augmented reality

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FX-MAD: Frequency-Domain Explainability and Explainability-Driven Unsupervised Detection of Face Morphing Attacks

FX-MAD: Frequency-Domain Explainability and Explainability-D...

引用

ieee Winter Applications and computer vision workshops (WACVW)

作者： Marco Huber Pedro C. Neto Ana F. Sequeira Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science Technische Universität Darmstadt Germany INESC TEC Porto Portugal Faculry of Engineering University of Porto Portugal

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Face recognition (FR) systems are vulnerable to morphing attacks, which refer to face images created by morphing the facial features of two different identities into one face image to create an image that can match both identities, allowing serious security breaches. In this work, we apply a frequency-based explanation method from the area of explainable face recognition to shine a light on how FR models behave when processing a bona fide or attack pair from a frequency perspective. In extensive experiments, we used two different state-of-the-art FR models and six different morphing attacks to investigate possible differences in behavior. Our results show that FR models rely differently on different frequency bands when making decisions for bona fide pairs and morphing attacks. In the following step, we show that this behavioral difference can be used to detect morphing attacks in an unsupervised setup solely based on the observed frequency-importance differences in a generalizable manner.

关键词： computer vision Correlation Face recognition Soft sensors Frequency-domain analysis conferences Benchmark testing Security Facial features

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Zero-Shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark

Zero-Shot Hazard Identification in Autonomous Driving: A Cas...

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ieee Winter Applications and computer vision workshops (WACVW)

作者： Lukas Picek Vojtěch Čermák Marek Hanzl PiVa AI University of West Bohemia INRIA Czech Technical University in Prague

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

This paper presents our submission to the COOOL com-petition, a novel benchmark for detecting and classifying out-of-label hazards in autonomous driving. Our approach integrates diverse methods across three core tasks: (i) driver reaction detection, (ii) hazard object identification, and (iii) hazard captioning. We propose kernel-based change point detection on bounding boxes and optical flow dynamics for driver reaction detection to analyze motion patterns. For hazard identification, we combined a naive proximity-based strategy with object classification using a pre-trained ViT model. At last, for hazard captioning, we used the Molmo vision-language model with tailored prompts to generate precise and context-aware descriptions of rare and low-resolution hazards. The proposed pipeline outperformed the baseline methods by a large margin, re-ducing the relative error by 33%, and scored 2 nd on the final leaderboard consisting of 32 teams.

关键词： computer vision Adaptation models Computational modeling Pipelines Benchmark testing Hazards Object recognition Optical flow Autonomous vehicles Tuning

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ieee computer Society conference on computer vision and pattern recognition workshops

IEEE Computer Society Conference on Computer Vision and Patt...

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31st Meeting of the ieee/cvf conference on computer vision and pattern recognition workshops, CVPRW 2018

ISBN: (纸本)9781538661000

The proceedings contain 307 papers. The topics discussed include: deep features for recognizing disguised faces in the wild;unconstrained fingerphoto database;hybrid user-independent and user-dependent offline signature verification with a two-channel CNN;it takes two to tango: cascading off-the-shelf face detectors;time analysis of pulse-based face anti-spoofing in visible and NIR;a deep face identification network enhanced by facial attributes prediction;gait recognition by deformable registration;and fusion of handcrafted and deep learning features for large-scale multiple iris presentation attack detection.

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Segment Anything in Medical Images with nnUNet

Segment Anything in Medical Images with nnUNet

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International Challenge on Segment Anything in Medical Images on Laptop held in conjunction with the ieee/cvf conference on computer vision and pattern recognition, CVPR 2024

作者： Stock, Raphael Kirchhoff, Yannick Rokuss, Maximilian R. Ravindran, Ashis Maier-Hein, Klaus Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031818530

In this paper, we present an enhanced medical image segmentation approach leveraging the nnUNet framework, specifically tailored to integrate bounding box prompts for improved segmentation accuracy in resource-constrained environments. By incorporating these prompts as binary masks in an additional input channel, we enable more precise and context-aware segmentation. Our methodology employs a 2D slice-wise approach optimized for CPU-based inference through just-in-time (JIT) compiled functions, ensuring efficient processing on standard clinical equipment. Our solution demonstrates robust performance, achieving an average Dice Similarity Coefficient (DSC) of 80.98% and a Normalized Surface Dice (NSD) of 83.23% across multiple modalities in the validation set. This indicates its practical applicability and effectiveness in real-world clinical settings, where computational resources may be limited. By focusing on both accuracy and efficiency, our approach makes advanced segmentation technology accessible to a broader range of healthcare providers, facilitating enhanced clinical decision-making and patient care. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Unified Face Matching and Physical-Digital Spoofing Attack Detection

Unified Face Matching and Physical-Digital Spoofing Attack D...

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ieee Winter Applications and computer vision workshops (WACVW)

作者： Arun Kunwar Ajita Rattani Dept. of Computer Science and Engineering University of North Texas Denton USA

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Face recognition technology has dramatically trans-formed the landscape of security, surveillance, and authentication systems, offering a user-friendly and non-invasive biometric solution. However, despite its significant advantages, face recognition systems face increasing threats from physical and digital spoofing attacks. Current research typically treats face recognition and attack detection as distinct classification challenges. This approach necessitates the implementation of separate models for each task, leading to considerable computational complexity, particularly on devices with limited resources. Such inefficiencies can stifle scalability and hinder performance. In response to these challenges, this paper introduces an innovative unified model designed for face recognition and detection of physical and digital attacks. By leveraging the advanced Swin Transformer backbone and incorporating Hil:o attention in a convolutional neural network framework, we address unified face recognition and spoof attack detection more effectively. Moreover, we introduce augmentation techniques that replicate the traits of physical and digital spoofing cues, significantly enhancing our model ro-bustness. Through comprehensive experimental evaluation across various datasets, we showcase the effectiveness of our model in unified face recognition and spoof detection. Additionally, we confirm its resilience against unseen physical and digital spoofing attacks, underscoring its potential for real-world applications.

关键词： Performance evaluation Face recognition Computational modeling Surveillance Scalability conferences Transformers Security Convolutional neural networks Resilience

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SSTAR: Skeleton-Based Spatio-Temporal Action recognition for Intelligent Video Surveillance and Suicide Prevention in Metro Stations

SSTAR: Skeleton-Based Spatio-Temporal Action Recognition for...

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ieee Winter Applications and computer vision workshops (WACVW)

作者： Safwen Naimi Wassim Bouachir Guillaume-Alexandre Bilodeau Brian Mishara Data Science Laboratory University of Québec (TÉLUQ) LITIV lab. Polytechnique Montréal Psychology Department University of Quebéc at Montréal

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Recent advances in computer vision greatly improved human behavior analysis in videos, particularly for developing smart surveillance systems that can detect and understand critical human actions in real-world situations. An important potential public health and public safety use for these systems is to automatically identify pre-suicidal behaviors, allowing for quick interventions to prevent these tragedies. In this paper, we propose SSTAR, a novel Skeleton-based Spatio-Temporal Action recognition model. SSTAR incorporates a 1D-Swin Transformer to capitalize on its hierarchical and multi-scale representation capabilities for efficient spatial feature extraction. For temporal modeling, we employ an sLSTM block to effectively capture long-term dependencies and dynamic motion patterns. We also propose a new real- world dataset capturing passenger actions associated with pre-suicidal behaviors, named ARMM. Extensive experiments on two public benchmark datasets, JHMDB and NW-UCLA, demonstrate the state-of-the-art performance as well as the high computational efficiency of our proposed approach. Moreover, we demonstrate the effectiveness of SSTAR in identifying action patterns for suicide prevention on our new ARMM dataset, achieving 91.34% accuracy and outperforming other skeleton-based action recognition methods. The code and dataset are available at https://***/SafwenNaimi/SSTAR.

关键词： computer vision Accuracy Prevention and mitigation Computational modeling Video surveillance Transformers Public security Real-time systems Behavioral sciences Videos

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Applying computer vision to Analyze Self-Injurious Behaviors in Children with Autism Spectrum Disorder

Applying Computer Vision to Analyze Self-Injurious Behaviors...

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ieee Winter Applications and computer vision workshops (WACVW)

作者： Ayda Eghbalian Md Mushfiqur Azam Katie Holloway Leslie Neely Kevin Desai The University of Texas San Antonio

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

computer vision has immense potential to advance healthcare and behavioral research, particularly for conditions such as autism spectrum disorder (ASD), which is characterized by early-emerging social communication deficits and repetitive sensory-motor behaviors. Early detection of these behavioral patterns is crucial for timely diagnosis and intervention. While existing studies primarily focus on facial expressions and eye-tracking in children with ASD, the study of body gestures remains underexplored, largely due to the lack of datasets capturing full-body movements. In this work, we present a novel dataset of videos capturing full-body gestures in children with ASD, recorded during behavioral therapy sessions. The dataset specifically includes frames capturing severe behaviors such as head hitting and head banging. To establish benchmarks, we conducted baseline experiments using state-of-the-art image classification models on these action-specific frames. Furthermore, we evaluated kinematic pose estimation models on the same frames, analyzing their performance and highlighting the unique challenges faced in applying computer vision techniques to children with ASD. This dataset and our findings aim to bridge critical gaps in current research, providing a foundational resource and insights for advancing ASD-focused behavioral assessments through computer vision.

关键词： computer vision Autism Head Image recognition Computational modeling Pose estimation Benchmark testing Magnetic heads Image classification Videos

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A Comparative Analysis of OCR Models on Diverse Datasets: Insights from Memes and Hiertext Dataset

A Comparative Analysis of OCR Models on Diverse Datasets: In...

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ieee Winter Applications and computer vision workshops (WACVW)

作者： Iknoor Singh Miguel Colom Kalina Bontcheva University of Sheffield UK Centre Borelli ENS Paris-Saclay

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Optical Character recognition (OCR) plays a critical role in various text extraction applications, yet its performance varies significantly across languages, visual appearances, and document types. This study benchmarks several state-of-the-art OCR models on two distinct and challenging datasets: MemeDataset, which contains informal, meme-based images, and HierText, featuring multilingual text in natural scenes and documents. We comprehensively evaluate a range of OCR models on these datasets, highlighting their strengths and limitations. While large multi-modal models excel in complex text extraction, they are hindered by slower processing times and irrelevant text generation in challenging scenarios. Also, we examine the models' ability to handle multilingual content, finding that some newer models perform well in certain languages but struggle in others. Our results emphasise the need for further enhancements in open-sourced large multimodal models to improve efficiency and accuracy, particularly in multilingual scenarios. In contrast, other open-source models such as EasyOCR deliver faster inference times with reasonably competitive performance, making them more suitable for time-sensitive applications. The findings offer valuable insights for researchers and practitioners in selecting the right models for diverse use cases.

关键词： Analytical models Visualization computer vision Accuracy Computational modeling conferences Optical character recognition Multilingual Internet Optimization

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Attrivision: Advancing Generalization in Pedestrian Attribute recognition using CLIP

AttriVision: Advancing Generalization in Pedestrian Attribut...

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ieee Winter Applications and computer vision workshops (WACVW)

作者： Mehran Adibi Sedeh Assia Benbihi Romain Martin Marianne Clausel Cédric Pradalier Two-i IAEM Université de Lorraine CIIRC Czech Technical University Two-i IECL Université de Lorraine GeorgiaTech Lorraine

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Pedestrian Attribute recognition (PAR) is a critical task in computer vision that identifies semantic attributes such as gender, age, clothing, and accessories from images of individuals. This task is essential in applications such as surveillance, smart city infrastructure, and security systems. Despite significant advances in deep learning, PAR remains challenging due to strong imbalances in the attribute classes and the need for robust generalization across different datasets and environments. In this work, we address these two limitations with Attrivision, a novel approach that adopts the generic CLIP features to make PAR better generalize and introduces a new Focal Cross-Entropy (FCE) loss function to handle the inherent class imbalance in PAR datasets. FCE improves the model's robustness by giving more weight to difficult-to-classify samples. Our method also demonstrates remarkable transferability to other attribute recognition tasks, such as vehicle attributes, without any architectural modifications. This transferability makes Attrivision a powerful and versatile tool for attribute recognition. We validate our approach on the Unified Pedestrian Attribute recognition (UPAR) dataset that integrates data from several sources including PA100K, PETA, RAPv2, and Market1501. Attrivision achieves new state-of-the-art results on UPAR, with a mean accuracy of 89.4% and an F1 score of 91.9%. These results demonstrate the model's effectiveness in handling real-world variability, including differences in image sensors, viewing conditions, and person densities, making it highly suitable for a wide range of real-world applications.

关键词： Image sensors computer vision Pedestrians Image recognition Smart cities Surveillance Image retrieval Semantics Robustness Security

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