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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Yanwei Huang Yan Miao Di Weng Adam Perer Yingcai Wu State Key Lab of CAD&CG Zhejiang University Hangzhou Zhejiang China School of Software Technology Zhejiang University Ningbo Zhejiang China Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA

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Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks

arXiv

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arXiv 2022年

作者： Haghpanah, Mohammad Amin Saeedizade, Ehsan Masouleh, Mehdi Tale Kalhor, Ahmad Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Computer Science and Engineering University of Nevada Reno United States

This paper presents a lightweight algorithm for feature extraction, classification of seven different emotions, and facial expression recognition in a real-time manner based on static images of the human face. In this regard, a Multi-Layer Perceptron (MLP) neural network is trained based on the foregoing algorithm. In order to classify human faces, first, some pre-processing is applied to the input image, which can localize and cut out faces from it. In the next step, a facial landmark detection library is used, which can detect the landmarks of each face. Then, the human face is split into upper and lower faces, which enables the extraction of the desired features from each part. In the proposed model, both geometric and texture-based feature types are taken into account. After the feature extraction phase, a normalized vector of features is created. A 3-layer MLP is trained using these feature vectors, leading to 96% accuracy on the test set. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Incremental Matrix Factorization for Recommender Systems 25

Incremental Matrix Factorization for Recommender Systems

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25th International computer Conference, computer Society of Iran, CSICC 2020

作者： Eslami, Ghazalak Ghaderi, Foad Tarbiat Modares University Human Computer Interaction Lab. Tehran Iran

ISBN: (纸本)9781728159379

With the help of recommender systems, users can find information they do not know about, but they are interested in. These systems are a specific type of intelligent systems that track each user's behavior and discover their behavioral patterns. Among different approaches for implementing recommender systems, Matrix-Factorization (MF) based methods are so popular due to their high accuracy and scalability. However, in real world applications that new ratings are continuously coming, processing the huge amount of data is a computationally expensive task. In this paper, we present a novel incremental matrix factorization method to learn only parts of the data that have been changed or added recently. This way, there is no need to train the system from the scratch. The input data to the proposed recommender system is of two types, batch data and stream data. Batch data is the rating data that is already saved in the system and relates to the activities of users in the past. Stream data is the rating data that enters the system in each time interval. The method is evaluated on two versions of popular MovieLens dataset from GroupLens research. The experimental results confirm that our method reduces the execution time significantly while keeping the prediction error intact. © 2020 IEEE.

关键词： Recommender systems

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StarGAN Based Facial Expression Transfer for Anime Characters 25

StarGAN Based Facial Expression Transfer for Anime Character...

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25th International computer Conference, computer Society of Iran, CSICC 2020

作者： Mobini, Majid Ghaderi, Foad Tarbiat Modares University Human Computer Interaction Lab. Tehran Iran

ISBN: (数字)9781728159379

ISBN: (纸本)9781728159379

human facial expression transfer has been well explored using Generative Adversarial Networks. Also, in case of anime style images, several successful attempts have been made to generate high-quality anime face images using GAN approach. However, the task of anime facial expression transfer is not well studied yet due to the lack of a clean labeled anime dataset. We address this issue from both data and model perspectives, by providing a clean labeled anime dataset and leveraging the use of the StarGAN image-to-image translation framework. Our collected dataset consists of about 5k high-quality anime face images including five major emotions collected from online image boards. We preprocessed our dataset by CARN super-resolution technique to improve quality of the images, and applied tuned StarGAN model to learn the mapping of an input anime image with arbitrary expression to the target expression. We evaluate our work by visually comparing the output translated results with the baseline model. Moreover, we provide a quantitative analysis of our proposed approach by computing the confusion matrix of expression transfer accuracy. © 2020 IEEE.

关键词： Generative adversarial networks

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DRDM: A Disentangled Representations Diffusion Model for Synthesizing Realistic Person Images

DRDM: A Disentangled Representations Diffusion Model for Syn...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Enbo Huang Yuan Zhang Faliang Huang Guangyu Zhang Yang Liu Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer and Information Engineering Nanning Normal University Nanning China College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Person image synthesis with controllable body poses and appearances is an essential task owing to the practical needs in the context of virtual try-on, image editing and video production. However, existing methods face significant challenges with details missing, limbs distortion and the garment style deviation. To address these issues, we propose a Disentangled Representations Diffusion Model (DRDM) to generate photorealistic images from source portraits in specific desired poses and appearances. First, a pose encoder is responsible for encoding pose features into a high-dimensional space to guide the generation of person images. Second, a body-part subspace decoupling block (BSDB) disentangles features from the different body parts of a source figure and feeds them to the various layers of the noise prediction block, thereby supplying the network with rich disentangled features for generating a realistic target image. Moreover, during inference, we develop a parsing map-based disentangled classifier-free guided sampling method, which amplifies the conditional signals of texture and pose. Extensive experimental results on the Deepfashion dataset demonstrate the effectiveness of our approach in achieving pose transfer and appearance control. The associated project can be found at https://***/lovemusiceb/DRDM

关键词： Limbs Image synthesis Shape Clothing Semantics Production Feature extraction Diffusion models Sampling methods Speech processing

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Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy

arXiv

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arXiv 2023年

作者： Marinov, Zdravko Jäger, Paul F. Egger, Jan Kleesiek, Jens Stiefelhagen, Rainer The Computer Vision for Human-Computer Interaction Lab Department of Informatics Karlsruhe Institute of Technology Adenauerring 10 Karlsruhe76131 Germany Girardetstraße 2 Essen45131 Germany Heidelberg Interactive Machine Learning Group Im Neuenheimer Feld 223 Heidelberg69120 Germany The Helmholtz Imaging DKFZ Im Neuenheimer Feld 223 Heidelberg69120 Germany

Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback takes the form of clicks, scribbles, or masks and allows for iterative refinement of the model output so as to efficiently guide the system towards the desired behavior. In recent years, deep learning-based approaches have propelled results to a new level causing a rapid growth in the field with 121 methods proposed in the medical imaging domain alone. In this review, we provide a structured overview of this emerging field featuring a comprehensive taxonomy, a systematic review of existing methods, and an in-depth analysis of current practices. Based on these contributions, we discuss the challenges and opportunities in the field. For instance, we find that there is a severe lack of comparison across methods which needs to be tackled by standardized baselines and benchmarks. Appendixes are here. © 2023, CC BY.

关键词： Medical imaging

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ExplainitAI: When do we trust artificial intelligence? The influence of content and explainability in a cross-cultural comparison

arXiv

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arXiv 2025年

作者： Kang, Sora Potinteu, Andreea-Elena Said, Nadia Human-Computer Interaction+Design Lab Seoul National University Seoul Korea Republic of Perception and Action Lab Leibniz-Institut für Wissensmedien Tübingen Germany Applied Cognitive Psychology Department of Psychology University of Tübingen Tübingen Germany

This study investigates cross-cultural differences in the perception of AI-driven chatbots between Germany and South Korea, focusing on topic dependency and explainability. Using a custom AI chat interface, ExplainitAI, we systematically examined these factors with quota-based samples from both countries (N = 297). Our findings revealed significant cultural distinctions: Korean participants exhibited higher trust, more positive user experience ratings, and more favorable perception of AI compared to German participants. Additionally, topic dependency was a key factor, with participants reporting lower trust in AI when addressing societally debated topics (e.g., migration) versus health or entertainment topics. These perceptions were further influenced by interactions among cultural context, content domains, and explainability conditions. The result highlights the importance of integrating cultural and contextual nuances into the design of AI systems, offering actionable insights for the development of culturally adaptive and explainable AI tailored to diverse user needs and expectations across domains. © 2025, CC BY-NC-SA.

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Design Optimisation of Electronic Nose with Multichannel Gas Sensor

Design Optimisation of Electronic Nose with Multichannel Gas...

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International Conference on Sensing Technology, ICST

作者： Yugal Pachori Richa Thakur Avinash Prashant D. Kulkarni Dinesh Singh Aditya Nigam Shubhajit Roy Chowdhury Biomedical Systems Lab School of Computing and Electrical Engineering Indian Institute of Technology Mandi Centre for Human Computer Interaction Indian Institute of Technology Mandi Multimedia Analytics Networks and Systems Lab School of Computing and Electrical Engineering Indian Institute of Technology Mandi

ISBN: (数字)9798350374827

ISBN: (纸本)9798350374834

human nose detects every kind of odors and our brain responds to every kind of smell that we are inhaling, so in the same way artificial nose or the electronic nose is a device that detects the smell and can enable the scope of digital smell technology. So, this paper proposes to optimize the design of an electronic nose that will eventually bring modification to the various digital platforms like internet, computers, mobile, television and tablets. We focused on design optimization of electronic nose which is built on a multichannel gas sensor array. The proposed electronic nose will enable the transmission or reception of smell over the internet and other digital platforms. The device can detect the smells of vapors coming out of volatile organic compounds. The sensing system is 3D printed in the model of a human nose. The sensing system works with an accuracy of detection of 90.45%. The response time of the electronic nose is 60 to 90 seconds, sometimes it is lesser than 60 seconds and it varies from substance to substance.

关键词： Volatile organic compounds Solid modeling TV Nose Electronic noses Three-dimensional printing Sensors Time factors Gas detectors Sensor arrays

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CUDA and Applications to Task-based Programming

CUDA and Applications to Task-based Programming

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42nd Annual Conference on European Association for computer Graphics, EUROGRAPHICS 2021

作者： Kenzel, M. Kerbl, B. Winter, M. Steinberger, M. Saarland University Computer Graphics Lab Germany TU Wien Institute of Visual Computing and Human-Centered Technology Austria Graz University of Technology Institute of Computer Graphics and Vision Austria

ISBN: (纸本)9783038681359

Since its inception, the CUDA programming model has been continuously evolving. Because the CUDA toolkit aims to consistently expose cutting-edge capabilities for general-purpose compute jobs to its users, the added features in each new version reflect the rapid changes that we observe in GPU architectures. Over the years, the changes in hardware, growing scope of built-in functions and libraries, as well as an advancing C++ standard compliance have expanded the design choices when coding for CUDA, and significantly altered the directives to achieve peak performance. In this tutorial, we give a thorough introduction to the CUDA toolkit, demonstrate how a contemporary application can benefit from recently introduced features and how they can be applied to task-based GPU scheduling in particular. For instance, we will provide detailed examples of use cases for independent thread scheduling, cooperative groups, and the CUDA standard library, libcu++, which are certain to become an integral part of clean coding for CUDA in the near future. © 2021 The Author(s).

关键词： Scheduling

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Magic NeRF Lens: Interactive Fusion of Neural Radiance Fields for Virtual Facility Inspection

arXiv

引用

arXiv 2023年

作者： Li, Ke Schmidt, Susanne Rolff, Tim Bacher, Reinhard Leemans, Wim Steinicke, Frank The accelerator division The Deutsche Elektronen Synchrotron DESY Notkestraße 85 Hamburg22607 Germany The Human Computer Interaction group Hamburg University Vogt-Kölln-Straße 30 Hamburg22527 Germany The Computer Vision group Hamburg University Vogt-Kölln-Straße 30 Hamburg22527 Germany

Large industrial facilities such as particle accelerators and nuclear power plants are critical infrastructures for scientific research and industrial processes. These facilities are complex systems that not only require regular maintenance and upgrades but are often inaccessible to humans due to various safety hazards. Therefore, a virtual reality (VR) system that can quickly replicate real-world remote environments to provide users with a high level of spatial and situational awareness is crucial for facility maintenance planning. However, the exact 3D shapes of these facilities are often too complex to be accurately modeled with geometric primitives through the traditional rasterization pipeline. In this work, we develop Magic NeRF Lens, an interactive framework to support facility inspection in immersive VR using neural radiance fields (NeRF) and volumetric rendering. We introduce a novel data fusion approach that combines the complementary strengths of volumetric rendering and geometric rasterization, allowing a NeRF model to be merged with other conventional 3D data, such as a computer-aided design model. We develop two novel 3D magic lens effects to optimize NeRF rendering by exploiting the properties of human vision and context-aware visualization. We demonstrate the high usability of our framework and methods through a technical benchmark, a visual search user study, and expert reviews. In addition, the source code of our VR NeRF framework is made publicly available for future research and development. © 2023, CC BY-NC-SA.

关键词： Virtual reality

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