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25th International Conference on Human-Computer Interaction, HCII 2023

作者： Stergioulas, Andreas Chatzikonstantinou, Christos Chatzis, Theocharis Papastratis, Ilias Konstantinidis, Dimitrios Dimitropoulos, Kosmas Atzakas, Klimnis Xydopoulos, George J. Zacharopoulou, Vassia Papazachariou, Dimitris Aggelidis, Vasilis Grigoriadis, Kostas Daras, Petros Visual Computing Lab Information Technologies Institute Centre for Research and Technology Hellas Thessaloniki Greece University of Patras Patra Greece Centre of Greek Sign Language – KENG Thessaloniki Greece MLS Innovation Inc. Thessaloniki Greece

ISBN: (纸本)9783031359910

Sign languages (SLs) are an essential form of communication for hearing-impaired people. However, a communication barrier still exists between the deaf community and the hearing population due to the lack of accurate automated SL communication systems. In this work, a novel SL communication system running as a mobile application has been developed to facilitate the bi-directional communication between hearing-impaired and hearing *** proposed system utilizes Natural Language Processing (NLP) techniques, along with linguistic rules to convert between spoken language and signs, taking into account the grammatical structure of a sign language. Additionally, the system employs sign language recognition (SLR) algorithms to transform video sequences to signs, as well as hand and pose estimation algorithms to model the 3D motion of signs. Moreover, a 3D human avatar representation is employed to animate the motion of each sign in a seamless manner. Finally, a new partition of the Greek SL (GSL) dataset is formed with 1825 videos from 12 signers captured in the wild to evaluate SLR performance under realistic conditions. The proposed SL communication system and its components are validated quantitatively in GSLW as well as qualitatively by means of questionnaires, demonstrating the user satisfaction with the system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Audition

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HPSCAN: Human Perception-Based Scattered Data Clustering

arXiv

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arXiv 2023年

作者： Hartwig, Sebastian van Onzenoodt, Christian Engel, Dominik Hermosilla, Pedro Ropinski, Timo Visual Computing Group Ulm University Germany Computer Vision Lab Technical University of Wien Austria

Cluster separation is a task typically tackled by widely used clustering techniques, such as k-means or DBSCAN. However, these algorithms are based on non-perceptual metrics, and our experiments demonstrate that their output does not reflect human cluster perception. To bridge the gap between human cluster perception and machine-computed clusters, we propose HPSCAN, a learning strategy that operates directly on scattered data. To learn perceptual cluster separation on such data, we crowdsourced the labeling of 7, 320 bivariate (scatterplot) datasets to 384 human participants. We train our HPSCAN model on these human-annotated data. Instead of rendering these data as scatterplot images, we used their x and y point coordinates as input to a modified PointNet++ architecture, enabling direct inference on point clouds. In this work, we provide details on how we collected our dataset, report statistics of the resulting annotations, and investigate the perceptual agreement of cluster separation for real-world data. We also report the training and evaluation protocol for HPSCAN and introduce a novel metric, that measures the accuracy between a clustering technique and a group of human annotators. We explore predicting point-wise human agreement to detect ambiguities. Finally, we compare our approach to ten established clustering techniques and demonstrate that HPSCAN is capable of generalizing to unseen and out-of-scope data. Copyright © 2023, The Authors. All rights reserved.

关键词： Cluster analysis

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Utilizing the Neglected Back Lobe for Mobile Charging 42

Utilizing the Neglected Back Lobe for Mobile Charging

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Ren, Meixuan Wu, Die Xue, Jing Xu, Wenzheng Peng, Jian Liu, Tang Sichuan Normal University College of Computer Science Chengdu610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Lab Chengdu610068 China Sichuan University College of Computer Science Chengdu610065 China

ISBN: (纸本)9798350334142

Benefitting from the breakthrough of wireless power transfer technology, the lifetime of Wireless Sensor Networks (WSNs) can be significantly prolonged by scheduling a mobile charger (MC) to charge sensors. Compared with omnidirectional charging, the MC equipped with directional antenna can concentrate energy in the intended direction, making charging more efficient. However, all prior arts ignore the considerable energy leakage behind the directional antenna (i.e., back lobe), resulting in energy wasted in vain. To address this issue, we study a fundamental problem of how to utilize the neglected back lobe and schedule the directional MC efficiently. Towards this end, we first build and verify a directional charging model considering both main and back lobes. Then, we focus on jointly optimizing the number of dead sensors and energy usage effectiveness. We achieve these by introducing a scheduling scheme that utilizes both main and back lobes to charge multiple sensors simultaneously. Finally, extensive simulations and field experiments demonstrate that our scheme reduces the number of dead sensors by 49.5% and increases the energy usage effectiveness by 10.2% on average as compared with existing algorithms. © 2023 IEEE.

关键词： Wireless sensor networks

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Pose-Transformation and Radial Distance Clustering for Unsupervised Person Re-identification 32

Pose-Transformation and Radial Distance Clustering for Unsup...

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32nd British Machine Vision Conference, BMVC 2021

作者： Seth, Siddharth Sonth, Akash Chakraborty, Anirban Visual Computing Lab Department of Computational and Data Sciences Indian Institute of Science Bangalore India

Person re-identification (re-ID) aims to tackle the problem of matching identities across non-overlapping cameras. Supervised approaches require identity information that may be difficult to obtain and are inherently biased towards the dataset they are trained on, making them unscalable across domains. To overcome these challenges, we propose an unsupervised approach to the person re-ID setup. Having zero knowledge of true labels, our proposed method enhances the discriminating ability of the learned features via a novel two-stage training strategy. The first stage involves training a deep network on an expertly designed pose-transformed dataset obtained by generating multiple perturbations for each original image in the pose space. Next, the network learns to map similar features closer in the feature space using the proposed discriminative clustering algorithm. We introduce a novel radial distance loss, that attends to the fundamental aspects of feature learning - compact clusters with low intra-cluster and high inter-cluster variation. Extensive experiments on several large-scale re-ID datasets demonstrate the superiority of our method compared to state-of-the-art approaches. © 2021. The copyright of this document resides with its authors.

关键词： Clustering algorithms

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FairCLIP: Harnessing Fairness in Vision-Language Learning

FairCLIP: Harnessing Fairness in Vision-Language Learning

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yan Luo Min Shi Muhammad Osama Khan Muhammad Muneeb Afzal Hao Huang Shuaihang Yuan Yu Tian Luo Song Ava Kouhana Tobias Elze Yi Fang Mengyu Wang Harvard Ophthalmology AI Lab Harvard University Tandon School of Engineering New York University Multimedia and Visual Computing Lab New York University Abu Dhabi

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Fairness is a critical concern in deep learning, especially in healthcare, where these models influence diagnoses and treatment decisions. Although fairness has been investigated in the vision-only domain, the fairness of medical vision-language (VL) models remains unexplored due to the scarcity of medical VL datasets for studying fairness. To bridge this research gap, we introduce the first fair vision-language medical dataset (Harvard-FairVLMed) that provides detailed demographic attributes, ground-truth labels, and clinical notes to facilitate an in-depth examination of fairness within VL foundation models. Using Harvard-FairVLMed, we conduct a comprehensive fairness analysis of two widely-used VL models (CLIP and BLIP2), pre-trained on both natural and medical domains, across four different protected attributes. Our results highlight significant biases in all VL models, with Asian, Male, Non-Hispanic, and Spanish being the preferred subgroups across the protected attributes of race, gender, ethnicity, and language, respectively. In order to alleviate these biases, we propose FairCLIP an optimal-transport-based approach that achieves a favorable trade-off between performance and fairness by reducing the Sinkhorn distance between the overall sample distribution and the distributions corresponding to each demographic group. As the first VL dataset of its kind, Harvard-FairVLMed holds the potential to catalyze advancements in the development of machine learning models that are both ethically aware and clinically effective. Our dataset and code are available at https://***/datasets/harvard-fairvlmed10k.

关键词： Deep learning Bridges Analytical models Ethics Computer vision Codes Computational modeling

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How did we get there? AI applications to biological networks and sequences

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Computers in Biology and Medicine 2025年 190卷 110064-110064页

作者： Anteghini, Marco Gualdi, Francesco Oliva, Baldo Structural Bioinformatics Lab Universitat Pompeu Fabra Barcelona Spain University of Bologna Bologna Italy Visual and Data-Centric Computing Zuse Institut Berlin Berlin Germany Lugano Switzerland

The rapidly advancing field of artificial intelligence (AI) has transformed numerous scientific domains, including biology, where a vast and complex volume of data is available for analysis. This paper provides a comprehensive overview of the current state of AI-driven methodologies in genomics, proteomics, and systems biology. We discuss how machine learning algorithms, particularly deep learning models, have enhanced the accuracy and efficiency of embedding sequences, motif discovery, and the prediction of gene expression and protein structure. Additionally, we explore the integration of AI in the embedding and analysis of biological networks, including protein–protein interaction networks and multi-layered networks. By leveraging large-scale biological data, AI techniques have enabled unprecedented insights into complex biological processes and disease mechanisms. This work underlines the potential of applying AI to complex biological data, highlighting current applications and suggesting directions for future research to further explore AI in this rapidly evolving field. © 2025 The Authors

关键词： Adversarial machine learning

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Evaluating Deep Learning Methods for Low Resolution Point Cloud Registration in Outdoor Scenarios 8

Evaluating Deep Learning Methods for Low Resolution Point Cl...

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8th Smart Tools and Applications in Graphics Conference, STAG 2021

作者： Siddique, Arslan Corsini, Massimiliano Ganovelli, Fabio Cignoni, Paolo Visual Computing Lab Instituto di Scienza e Tecnologia dell'Informazione CNR Pisa Italy

ISBN: (纸本)9783038681656

Point cloud registration is a fundamental task in 3D reconstruction and environment perception. We explore the performance of modern Deep Learning-based registration techniques, in particular Deep Global Registration (DGR) and Learning Multi-view Registration (LMVR), on an outdoor real world data consisting of thousands of range maps of a building acquired by a Velodyne LIDAR mounted on a drone. We used these pairwise registration methods in a sequential pipeline to obtain an initial rough registration. The output of this pipeline can be further globally refined. This simple registration pipeline allow us to assess if these modern methods are able to deal with this low quality data. Our experiments demonstrated that, despite some design choices adopted to take into account the peculiarities of the data, more work is required to improve the results of the registration. © 2021 The Author(s)

关键词： Pipelines

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ReWaRD: Retinal Waves for Pre-Training Artificial Neural Networks Mimicking Real Prenatal Development

arXiv

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arXiv 2023年

作者： Cappell, Benjamin Stoll, Andreas Umah, Williams Chukwudi Egger, Bernhard Cognitive Computer Vision Lab Chair of Visual Computing Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

Computational models trained on a large amount of natural images are the state-of-the-art to study human vision – usually adult vision. Computational models of infant vision and its further development are gaining more and more attention in the community. In this work we aim at the very beginning of our visual experience – pre- and post-natal retinal waves which suggest to be a pre-training mechanism for the primate visual system at a very early stage of development. We see this approach as an instance of biologically plausible data driven inductive bias through pre-training. We built a computational model that mimics this development mechanism by pre-training different artificial convolutional neural networks with simulated retinal wave images. The resulting features of this biologically plausible pre-training closely match the V1 features of the primate visual system. We show that the performance gain by pre-training with retinal waves is similar to a state-of-the art pre-training pipeline. Our framework contains the retinal wave generator, as well as a training strategy, which can be a first step in a curriculum learning based training diet for various models of development. We release code, data and trained networks to build the basis for future work on visual development and based on a curriculum learning approach including prenatal development to support studies of innate vs. learned properties of the primate visual system. An additional benefit of our pre-trained networks for neuroscience or computer vision applications is the absence of biases inherited from datasets like ImageNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Ophthalmology

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Language Models Represent Beliefs of Self and Others 41

Language Models Represent Beliefs of Self and Others

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41st International Conference on Machine Learning, ICML 2024

作者： Zhu, Wentao Zhang, Zhining Wang, Yizhou Center on Frontiers of Computing Studies School of Compter Science Peking University China Inst. for Artificial Intelligence Peking University China Nat'l Eng. Research Center of Visual Technology Peking University China Nat'l Key Lab of General Artificial Intelligence Peking University China

Understanding and attributing mental states, known as Theory of Mind (ToM), emerges as a fundamental capability for human social *** Large Language Models (LLMs) appear to possess certain ToM abilities, the mechanisms underlying these capabilities remain *** this study, we discover that it is possible to linearly decode the belief status from the perspectives of various agents through neural activations of language models, indicating the existence of internal representations of self and others' *** manipulating these representations, we observe dramatic changes in the models' ToM performance, underscoring their pivotal role in the social reasoning ***, our findings extend to diverse social reasoning tasks that involve different causal inference patterns, suggesting the potential generalizability of these representations. Copyright 2024 by the author(s)

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FOCUS: Towards Universal Foreground Segmentation

arXiv

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arXiv 2025年

作者： You, Zuyao Kong, Lingyu Meng, Lingchen Wu, Zuxuan Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China

Foreground segmentation is a fundamental task in computer vision, encompassing various subdivision tasks. Previous research has typically designed task-specific architectures for each task, leading to a lack of unification. Moreover, they primarily focus on recognizing foreground objects without effectively distinguishing them from the background. In this paper, we emphasize the importance of the background and its relationship with the foreground. We introduce FOCUS, the Foreground ObjeCts Universal Segmentation framework that can handle multiple foreground tasks. We develop a multi-scale semantic network using the edge information of objects to enhance image features. To achieve boundary-aware segmentation, we propose a novel distillation method, integrating the contrastive learning strategy to refine the prediction mask in multi-modal feature space. We conduct extensive experiments on a total of 13 datasets across 5 tasks, and the results demonstrate that FOCUS consistently outperforms the state-of-the-art task-specific models on most metrics. © 2025, CC BY-NC-SA.

关键词： Contrastive Learning

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