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Group-CLIP Uncertainty Modeling for Group Re-Identification

Group-CLIP Uncertainty Modeling for Group Re-Identification

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhang, Qingxin Wei, Haoyan Qian, Yang Glasgow College Hainan UESTC University of Electronic Science and Technology of China Hainan China School of Electronic Information Sichuan University Chengdu China USC Viterbi School of Engineering University of Southern California Los AngelesCA United States

ISBN: (纸本)9798350368741

Group Re-Identification (Group ReID) aims matching groups of pedestrians across non-overlapping cameras. Unlike single person ReID, Group ReID focuses more on the changes in group structure, emphasizing the number of members and their spatial arrangement. However, most methods rely on certainty-based models, which consider only the specific group structures in the group images, often failing to match unseen group configurations. To this end, we propose a novel Group-CLIP Uncertainty Modeling (GCUM) approach that adapts group text descriptions to undetermined accommodate member and layout variations. Specifically, we design a Member Variant Simulation (MVS) module that simulates member exclusions using a Bernoulli distribution and a Group Layout Adaptation (GLA) module that generates uncertain group text descriptions with identity-specific tokens. In addition, we design a Group Relationship Construction Encoder (GRCE) that uses group features to refine individual features, and employ cross-modal contrastive loss to obtain generalizable knowledge from group text descriptions. It is worth noting that we are the first to employ CLIP to Group ReID, and extensive experiments show that GCUM significantly outperforms state-of-the-art Group ReID methods. © 2025 IEEE.

关键词： CLIP Group Re-Identification Uncertainty Modeling vision-Language Pre-training

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PADNET: A PATCH-BASED ANOMALY DETECTION FRAMEWORK FOR INDUSTRIAL PIPELINE DAMAGE DETECTION

PADNET: A PATCH-BASED ANOMALY DETECTION FRAMEWORK FOR INDUST...

引用

2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Alexaki, Erofili Papaioannidis, Christos Mygdalis, Vasileios Pitas, Ioannis Department of Informatics Aristotle University of Thessaloniki Thessaloniki54124 Greece

ISBN: (纸本)9798350368741

Industrial pipeline inspection in petrochemical refineries is dangerous, expensive, time-consuming and prone to errors. Anomaly detection can play a crucial role towards its automation. Damages in this type of infrastructure are few and can be considered as anomalies (essentially outliers). This paper proposes a novel patch-based Anomaly Detection Network (PADNet), that employs deep learning for detecting insulated pipe damages. It consists of three main components: a) a pipeline segmentation module, b) an image patch proposal module, and c) an anomaly detection module. These components work sequentially first to localize insulated pipelines in the input UAV or ground camera images or video frames and then analyze image patches to detect and localize any damages. Importantly, the anomaly detection module can be trained using undamaged pipeline image data only, hence eliminating the need for costly damaged pipeline image annotation. Experimental results demonstrate the effectiveness of the proposed PADNet method in detecting pipeline damages, making it a promising solution for autonomous industrial infrastructure inspection. © 2025 IEEE.

关键词： Anomaly Detection Autoencoders Computer vision Deep Learning Insulated Pipe Damage Detection

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Exploring Facial Kinship Verification through Contactless Heart Activity Analysis

Exploring Facial Kinship Verification through Contactless He...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Wu, Xiaoting Feng, Xiaoyi Casado, Constantino Álvarez Liu, Lili López, Miguel Bordallo Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland School of Electronics and Information Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350368741

Facial Kinship Verification (FKV) aims at automatically determining whether two subjects have a kinship relation based on human faces. It has potential applications in finding missing children and social media analysis. Traditional FKV faces challenges as it is vulnerable to spoof attacks and raises privacy issues. In this paper, we explore for the first time the FKV by analyzing cardiac activity through physiological signals, with a specific focus on remote Photoplethysmography (rPPG). rPPG signals are extracted from facial videos, resulting in a one-dimensional signal that measures the changes in visible light reflection emitted to and detected from the skin caused by the heartbeat. Specifically, in this paper, we employed a straightforward one-dimensional Convolutional Neural Network (1DCNN) with a 1DCNN-Attention module and kinship contrastive loss to learn the kin similarity from rPPGs. The network takes multiple rPPG signals extracted from various facial Regions of Interest (ROIs) as inputs. Additionally, the 1DCNN attention module is designed to learn and capture the discriminative kin features from feature embeddings. Finally, we demonstrate the feasibility of rPPG to detect kinship with the experiment evaluation on the UvANEMO Smile Database from different kin relations. © 2025 IEEE.

关键词： 1DCNN Biosignals Channel attention Kinship verification Remote Photoplethysmography

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Trust Prediction in Assistive robotics Using Multi-modal Video Transformers 16th

Trust Prediction in Assistive Robotics Using Multi-modal Vi...

引用

16th international conference on Social robotics, ICSR + AI 2024

作者： Lingg, Nico Demiris, Yiannis Imperial College London LondonSW7 2AZ United Kingdom

ISBN: (纸本)9789819635184

Trust is crucial for effective human-robot interaction in assistive robotics. This paper presents a proof-of-concept study for transformer-based trust prediction, building on our previous work in understanding and measuring trust. We introduce a novel multi-modal video transformer to predict user trust in human-robot interactions with an autonomous wheelchair. We collected a comprehensive dataset comprising over 850,000 tokens, equivalent to approximately 4 h of egocentric video streams, along with synchronized continuous trust recordings, eye gaze patterns, physiological data, and trust measurements from 34 diverse participants. Our research utilizes Trusty, a tool for continuous trust measurement, which has been previously validated against traditional methods. Our model utilizes a pre-trained vision Transformer (ViT) with frozen weights to extract spatial features from video frames. Visual representations are enriched with the user’s eye gaze, heart rate, and electrodermal activity in a fusion module. Fusion outputs are temporally processed by a transformer, and the final token in the sequence is used to predict trust. This architectural design leverages transformers’ proven capabilities in processing high-dimensional visual data, effectively fusing signals across modalities, and scaling to larger datasets. The best-performing model achieves approximately 64% accuracy in predicting trust levels (high, medium, low), with eye gaze data significantly enhancing performance. Future work will focus on personalisation and real-time adaptive robot behaviour based on trust predictions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Human robot interaction

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Sound-Based Recognition of Touch Gestures and Emotions for Enhanced Human-robot Interaction

Sound-Based Recognition of Touch Gestures and Emotions for E...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yuanbo Hou Qiaoqiao Ren Wenwu Wang Dick Botteldooren WAVES Ghent University Belgium AIRO Ghent University-IMEC Belgium CVSSP University of Surrey UK

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Emotion recognition and touch gesture decoding are crucial for advancing human-robot interaction (HRI), especially in social environments where emotional cues and tactile perception play important roles. However, many humanoid robots, such as Pepper, Nao, and Furhat, lack full-body tactile skin, limiting their ability to engage in touch-based emotional and gesture interactions. In addition, vision-based emotion recognition methods usually face strict GDPR compliance challenges due to the need to collect personal facial data. To address these limitations and avoid privacy issues, this paper studies the potential of using the sounds produced by touching during HRI to recognise tactile gestures and classify emotions along the arousal and valence dimensions. Using a dataset of tactile gestures and emotional interactions from 28 participants with the humanoid robot Pepper, we design an audio-only lightweight touch gesture and emotion recognition model with only 0.24M parameters, 0.94MB model size, and 0.7G FLOPs. Experimental results show that the proposed model effectively recognises the arousal and valence states of different emotions, as well as various tactile gestures, when the input audio length varies. The proposed model is of low-latency and achieves similar results as well-known pretrained audio neural networks (PANNs), but with much smaller FLOPs, number of parameters, and model size.

关键词： Emotion recognition Privacy Neural networks Human-robot interaction Humanoid robots Skin Real-time systems Low latency communication Speech processing signal resolution

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Patch based image processing for complex environment characterization 6

Patch based image processing for complex environment charact...

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6th IEEE international conference on Image processing, Applications and Systems, IPAS 2025

作者： Menier, Corentin Meurie, Cyril Guillemaille, Timothee Ruichek, Yassine Marais, Juliette Univ Gustave Eiffel COSYS-LEOST Villeneuve d'AscqF-59650 France UTBM BelfortF-90010 France

ISBN: (纸本)9798331506520

Environment analysis is a critical part of autonomous vehicle for transport applications and for passenger safety. The solutions demonstrating the greatest robustness have been integrating multiple sensors used for redundancy and refinement purposes. vision applications have proven to offer a high degree of flexibility and performance. One particular instance of this is higlighted in vehicle localisation, which predominantly relies on GNSS-based systems for positioning calculation using propagation time measurements. However, this signal may be degraded through the environment around the vehicle, worst case being urban canyons leading to Non Line Of Sight(NLOS) scenarios or multipaths issues due to reflecting obstacles. Previous work have shown vision-based algorithms can be used to mitigate these effects. One widely studied approach relies on the segmentation of an acquired wide-angle image installed on the roof of the vehicle and oriented toward the *** the sky processing module is binary, the pipeline lack any way to express its uncertainty when applying weighting policies to the detected satellite state, which can be detrimental to the resulting positioning. In this paper, we propose a novel way of analysing wide-angle camera images, also known as fisheye images, dividing the image into patches to output the corresponding situation of each region of interest. Additionally we propose a new class to the previous sky versus non-sky segmentation, designated as mixed class and designed to serve as a fuzzy answer by the deep learning model to improve confidence to other scenarios as well as allow for new analysis policies of satellites signals. The data-driven algorithm is designed and tested on a publicly available dataset, composed of a large number of finely labelled images provided by ISAE-SUPAERO reaching a 94% accuracy. © 2025 IEEE.

关键词： Image segmentation

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Emerging Technology for the Biohybrid robotics 38

Emerging Technology for the Biohybrid Robotics

引用

38th IEEE international conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Takeuchi, Shoji The University of Tokyo Graduate School of Information Science and Technology Japan Japan

ISBN: (纸本)9798331508890

Biohybrid robotics integrates living tissues with synthetic systems, enabling robots to perform nature-inspired functions. Recent advancements include biohybrid (BH) sensors for highly sensitive and selective detection, BH reactors replicating organ-like processes for drug testing and artificial organ development, BH actuators enabling efficient and silent movement, and BH processors offering energy-efficient signal processing and adaptive learning. Future biohybrid robots may feature living skin and energy systems that metabolize food, potentially serving roles in organ transplantation. Although challenges such as scalability, stability, and ethical considerations remain, interdisciplinary research continues to drive progress, merging biology and engineering to address critical societal needs. © 2025 IEEE.

关键词： Biofabrication Biohybrid robot Biohybrid Skin Cultured Meat Edible robot Food technology Olfactory sensors Wet Machine

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Explore the Hallucination on Low-level Perception for MLLMs

Explore the Hallucination on Low-level Perception for MLLMs

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Sun, Yinan Zhang, Zicheng Wu, Haoning Liu, Xiaohong Lin, Weisi Zhai, Guangtao Min, Xiongkuo Shanghai Jiao Tong University China S-Lab Nanyang Technological University Singapore Nanyang Technological University Singapore Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350368741

The rapid development of Multi-modality Large Language Models (MLLMs) has significantly influenced various aspects of industry and daily life, showcasing impressive capabilities in visual perception and understanding. However, these models also exhibit hallucinations, which limit their reliability as AI systems, especially in tasks involving low-level visual perception and understanding. We believe that hallucinations stem from a lack of explicit self-awareness in these models, which directly impacts their overall performance. In this paper, we aim to define and evaluate the self-awareness of MLLMs in low-level visual perception and understanding tasks. To this end, we present QL-Bench, a benchmark settings to simulate human responses to low-level vision, investigating self-awareness in low-level visual perception through visual question answering related to low-level attributes such as clarity and lighting. Specifically, we construct the LLSAvisionQA dataset, comprising 2, 990 single images and 1, 999 image pairs, each accompanied by an open-ended question about its low-level features. Through the evaluation of 15 MLLMs, we demonstrate that while some models exhibit robust low-level visual capabilities, their self-awareness remains relatively underdeveloped. Notably, for the same model, simpler questions are often answered more accurately than complex ones. However, self-awareness appears to improve when addressing more challenging questions. We hope that our benchmark will motivate further research, particularly focused on enhancing the self-awareness of MLLMs in tasks involving low-level visual perception and understanding. © 2025 IEEE.

关键词： benchmark hallucination low-level vision Multi-modality large language models perception

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On the use of vision for the Weighting of GNSS observation: comparisons 6

On the use of Vision for the Weighting of GNSS observation: ...

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6th IEEE international conference on Image processing, Applications and Systems, IPAS 2025

作者： Cheng, Zhiye Guillemaille, Timothee Menier, Corentin Meurie, Cyril Marais, Juliette Univ Gustave Eiffel COSYS-LEOST Villeneuve d'AscqF-59650 France

ISBN: (纸本)9798331506520

Positioning is a critical function in every intelligent vehicle application. Most applied system is a GNSS (Global Navigation Satellite System)-based receiver, cheap and offering a continuous meter-level accuracy. However accuracy strongly depends on the satellite signal reception state: LOS (Line of sight), that is direct, optimal signal or NLOS (Non LOS), i.e received without direct visibilty and after one or more reflections of the signal. Based on previous work, this paper summarizes different weighting schemes applied to mitigate these local effects on GNSS signals and enhance position accuracy in land transport environment. A database collected by ISAE is used for application and comparison of the different schemes. One of them not only relies on GNSS signals but also on satellite state identification thanks to the use of a fisheye camera. This state allows us to deweight degraded measurements without excluding them, in order to keep availability. The paper shows that considering this additional information allows the WLS (Weighting Least Square) to significantly increase accuracy in every types of environments. If state of the art weighting schemes already improve accuracy by 65 and 77% over ordinary least squares in the 2D plan, our weight further improves accuracy by 40% compared to the classical elevation-based weight. © 2025 IEEE.

关键词： Global positioning system

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Graph-Enhanced Dual-Stream Feature Fusion with Pre-Trained Model for Acoustic Traffic Monitoring

Graph-Enhanced Dual-Stream Feature Fusion with Pre-Trained M...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Fan, Shitong Xiao, Feiyang Wang, Wenbo Qi, Shuhan Zhu, Qiaoxi Wang, Wenwu Guan, Jian Group of Intelligent Signal Processing Harbin Engineering University Harbin China Faculty of Computing Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Acoustics Lab University of Technology Sydney Ultimo Australia Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

ISBN: (纸本)9798350368741

Microphone array techniques are widely used in sound source localization and smart city acoustic-based traffic monitoring, but these applications face significant challenges due to the scarcity of labeled real-world traffic audio data and the complexity and diversity of application scenarios. The DCASE Challenge's Task 10 focuses on using multi-channel audio signals to count vehicles (cars or commercial vehicles) and identify their directions (left-to-right or vice versa). In this paper, we propose a graph-enhanced dual-stream feature fusion network (GEDF-Net) for acoustic traffic monitoring, which simultaneously considers vehicle type and direction to improve detection. We propose a graph-enhanced dual-stream feature fusion strategy which consists of a vehicle type feature extraction (VTFE) branch, a vehicle direction feature extraction (VDFE) branch, and a frame-level feature fusion module to combine the type and direction feature for enhanced performance. A pre-trained model (PANNs) is used in the VTFE branch to mitigate data scarcity and enhance the type features, followed by a graph attention mechanism to exploit temporal relationships and highlight important audio events within these features. The frame-level fusion of direction and type features enables fine-grained feature representation, resulting in better detection performance. Experiments demonstrate the effectiveness of our proposed method. GEDF-Net is our submission that achieved 1st place in the DCASE 2024 Challenge Task 10. © 2025 IEEE.

关键词： Acoustic-based traffic monitoring feature fusion graph attention pre-trained model transfer learning

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