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8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023

作者： Wang, Liangliang Huo, Guanglei Chen, Xinwei Huang, Chengxi Wuhan University of Technology School of Computer Science and Artificial Intelligence Luoshi Road 122 Wuhan430070 China -HIT Research Institute of Engineering and Technology Building 9 Software Park Quanzhou362008 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9798350302776

To enable robots to understand a specific assistive task during human-robot interactions under complex home scenes, at the center is the problem of human-object interaction (HOI) recognition. In particular, aiming at identifying efficiently, this paper proposes a graph convolutional network (GCN) by considering the similarity and proximity of human and object nodes, based on which the geometric positions and morphological relationships are explored. Also, a supplementary convolutional neural network (CNN) is fused to GCN to extract the global semantic features, which also ensures the recognition applicability for the cases where detectors fall into failure. Experiments on V-COCO demonstrate our approach, and results in real-world scenes on our assistive robot - Xiaozhi II verify its robustness. © 2023 IEEE.

关键词： Human robot interaction

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Prompt learning in computer vision: a survey

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Frontiers of information technology & Electronic Engineering 2024年第1期25卷 42-63页

作者： Yiming LEI Jingqi LI Zilong LI Yuan CAO Hongming SHAN Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200438China Institute of Science and Technology for Brain-Inspired Intelligence Fudan UniversityShanghai 200433China MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200433China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai 201210China

Prompt learning has attracted broad attention in computer vision since the large pre-trained visionlanguagemodels (VLMs) exploded. Based on the close relationship between vision and language information builtby VLM, prompt learning becomes a crucial technique in many important applications such as artificial intelligencegenerated content (AIGC). In this survey, we provide a progressive and comprehensive review of visual promptlearning as related to AIGC. We begin by introducing VLM, the foundation of visual prompt learning. Then, wereview the vision prompt learning methods and prompt-guided generative models, and discuss how to improve theefficiency of adapting AIGC models to specific downstream tasks. Finally, we provide some promising researchdirections concerning prompt learning.

关键词： Prompt learning Visual prompt tuning(VPT) Image generation Image classification Artificial intelligence generated content(AIGC)

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Long Video Micro-expression Spotting Based On OCC Theory

Long Video Micro-expression Spotting Based On OCC Theory

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： BingTong Liu ZheYuan Zhang Ju Zhou HanPu Wang Tong Chen Institute of Legal Psychology and Intelligent Computing Southwest University Chongqing China Chongqing Key Laboratory of Non-Linear Circuit and Intelligent Information Processing Chongqing China School of Electronic and Information Engineering Southwest University Chongqing China

The generation of micro-expression (ME) is unconscious and unaffected by human subjective consciousness. It can reveal hidden emotions in humans, which has important applications in the domains of psychological diagnosis, lie detection, and so on. ME detection, which aims to locate ME apex frames in long videos, is the foundation of ME recognition. In this paper, we propose a ME detection method based on One-Class Classification (OCC) theory for long videos. The method is the first to use neutral expression frames to detect MEs, which addresses the problem of insufficient training samples in ME detection. We design a geometric motion feature based on the prior knowledge from the FACS and utilize it in conjunction with the features extracted by CNN to train separate one-class classifiers for ME detection. Finally, we evaluate the proposed method on publicly available datasets CASME I, CASME II, SAMM, and a mixed database comprising data from the three datasets, to verify the feasibility of the proposed method in both single-database and cross-database ME detection. The experimental results show that our method outperforms three other ME detection methods, demonstrating its ability to more accurately locate ME apex frames in long videos.

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EarthGPT-X: Enabling MLLMs to Flexibly and Comprehensively Understand Multi-Source Remote Sensing Imagery

arXiv

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arXiv 2025年

作者： Zhang, Wei Cai, Miaoxin Ning, Yaqian Zhang, Tong Zhuang, Yin Chen, He Li, Jun Mao, Xuerui Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing100081 China School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314003 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing100081 China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China School of Computer Science Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan430078 China

Recent advances in the visual-language area have developed natural multi-modal large language models (MLLMs) for spatial reasoning through visual prompting. However, due to remote sensing (RS) imagery containing abundant geospatial information that differs from natural images, it is challenging to effectively adapt natural spatial models to the RS domain. Moreover, current RS MLLMs are limited in overly narrow interpretation levels and interaction manner, hindering their applicability in real-world scenarios. To address those challenges, a spatial MLLM named EarthGPT-X is proposed, enabling a comprehensive understanding of multi-source RS imagery, such as optical, synthetic aperture radar (SAR), and infrared. EarthGPT-X offers zoom-in and zoom-out insight, and possesses flexible multi-grained interactive abilities. Moreover, EarthGPT-X unifies two types of critical spatial tasks (i.e., referring and grounding) into a visual prompting framework. To achieve these versatile capabilities, several key strategies are developed. The first is the multi-modal content integration method, which enhances the interplay between images, visual prompts, and text instructions. Subsequently, a cross-domain one-stage fusion training strategy is proposed, utilizing the large language model (LLM) as a unified interface for multi-source multi-task learning. Furthermore, by incorporating a pixel perception module, the referring and grounding tasks are seamlessly unified within a single framework. In addition, the experiments conducted demonstrate the superiority of the proposed EarthGPT-X in multi-grained tasks and its impressive flexibility in multi-modal interaction, revealing significant advancements of MLLM in the RS field. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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Streaming Media Load Balancing with Improved Genetic Algorithm

Streaming Media Load Balancing with Improved Genetic Algorit...

引用

Chinese Control and Decision Conference, CCDC

作者： Yang Zhang Xing Yang Yuan Xu Yanlin He Mingqing Zhang Qunxiong Zhu College of Information Science and Technology Beijing University of Chemical Technology Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Hangzhou China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The load balancing algorithm of streaming media server is facing unique challenges. Different from the traditional Web server, the streaming media server needs to consider the characteristics of real-time, bandwidth occupation and multimedia data transmission. Therefore, the traditional load balancing algorithm is not necessarily suitable for streaming media servers, and needs special design and research. Aiming at the network requests of streaming media server clusters, a multi-dimensional matrix evaluation method based on streaming media characteristics is proposed, the genetic algorithm is improved, and it is applied to the dynamic load balancing algorithm scheduling scenario, and analyzed and simulated. Experimental results show that the algorithm can effectively reduce the average execution time of all requests, speed up the average response time and improve throughput.

关键词： Heuristic algorithms Clustering algorithms Streaming media Throughput Load management Dynamic scheduling Web servers

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Characterizing and Understanding Development of Social computing Through DBLP: A Data-Driven Analysis

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Journal of Social computing 2022年第4期3卷 287-302页

作者： Jiaqi Wu Bodian Ye Qingyuan Gong Atte Oksanen Cong Li Jingjing Qu Felicia F.Tian Xiang Li Yang Chen the Shanghai Key Lab of Intelligent Information Processing the School of Computer ScienceFudan UniversityShanghai 200438China the Faculty of Social Sciences Tampere UniversityTampere 33100Finland School of Information Science and Technology Fudan UniversityShanghai 200438China Shanghai Artificial Intelligence Laboratory Shanghai 200232China. School of Social Development and Public Policy Fudan UniversityShanghai 200433China. Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous SystemsTongji UniversityShanghai 200092China.

During the past decades,the term“social computing”has become a promising interdisciplinary area in the intersection of computer science and social *** this work,we conduct a data-driven study to understand the development of social computing using the data collected from Digital Bibliography and Library Project(DBLP),a representative computer science bibliography *** have observed a series of trends in the development of social computing,including the evolution of the number of publications,popular keywords,top venues,international collaborations,and research *** findings will be helpful for researchers and practitioners working in relevant fields.

关键词： social computing Digital Bibliography and Library Project(DBLP) bibliometric evolution visualization

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Wearable and interactive multicolored photochromic fiber display

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Light(Science & Applications) 2024年第3期13卷 454-465页

作者： Pan Li Yuwei Wang Xiaoxian He Yuyang Cui Jingyu Ouyang Ju Ouyang Zicheng He Jiayu Hu Xiaojuan Liu Hang Wei Yu Wang Xiaoling Lu Qian Ji Xinyuan Cai Li Liu Chong Hou Ning Zhou Shaowu Pan Xiangru Wang Huamin Zhou Cheng-Wei Qiu Yan-Qing Lu Guangming Tao State Key Laboratory of Material Processing and Die&Mould Technology School of Materials Science and Engineering and Wuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Vascular Aging(HUST) Ministry of EducationWuhan 430030China School of Optoelectronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore National Laboratory of Solid State Microstructures Key Laboratory of Intelligent Optical Sensing and ManipulationCollege of Engineering and Applied Sciencesand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210023China School of Performing Arts Wuhan Conservatory of MusicWuhan 430060China School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China School of Architecture and Urban Planning Huazhong University of Science and TechnologyWuhan 430074China School of Fashion Beijing Institute of Fashion TechnologyBeijing 100029China School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Tongji Medical College Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and EngineeringDonghua UniversityShanghai 201620China

Endowing flexible and adaptable fiber devices with light-emitting capabilities has the potential to revolutionize the current design philosophy of intelligent,wearable interactive ***,significant challenges remain in developing fiber devices when it comes to achieving uniform and customizable light effects while utilizing lightweight ***,we introduce a mass-produced,wearable,and interactive photochromic fiber that provides uniform multicolored light *** designed independent waveguides inside the fiber to maintain total internal reflection of light as it traverses the *** impact of excessive light leakage on the overall illuminance can be reduced by utilizing the saturable absorption effect of fluorescent materials to ensure light emission uniformity along the transmission *** addition,we coupled various fluorescent composite materials inside the fiber to achieve artificially controllable spectral radiation of multiple color systems in a single *** prepared fibers on mass-produced kilometer-long using the thermal drawing *** fibers can be directly integrated into daily wearable devices or clothing in various patterns and combined with other signal input components to control and display patterns as *** work provides a new perspective and inspiration to the existing field of fiber display interaction,paving the way for future human–machine integration.

关键词： fiber interactive colored

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Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search

arXiv

引用

arXiv 2023年

作者： Yang, Shangshang Ma, Haiping Zhen, Cheng Tian, Ye Zhang, Limiao Jin, Yaochu Zhang, Xingyi The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei230039 China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Anhui Hefei230601 China The Faculty of Technology Bielefeld Unversity Bielefeld33619 Germany

Cognitive diagnosis plays a vital role in modern intelligent education platforms to reveal students’ proficiency in knowledge concepts for subsequent adaptive tasks. However, due to the requirement of high model interpretability, existing manually designed cognitive diagnosis models hold too simple architectures to meet the demand of current intelligent education systems, where the bias of human design also limits the emergence of effective cognitive diagnosis models. In this paper, we propose to automatically design novel cognitive diagnosis models by evolutionary multiobjective neural architecture search (NAS). Specifically, we observe existing models can be represented by a general model handling three given types of inputs and thus first design an expressive search space for the NAS task in cognitive diagnosis. Then, we propose multi-objective genetic programming (MOGP) to explore the NAS task’s search space by maximizing model performance and interpretability. In the MOGP design, each architecture is transformed into a tree architecture and encoded by a tree for easy optimization, and a tailored genetic operation based on four sub-genetic operations is devised to generate offspring effectively. Besides, an initialization strategy is also suggested to accelerate the convergence by evolving half of the population from existing models’ variants. Experiments on two real-world datasets demonstrate that the cognitive diagnosis models searched by the proposed approach exhibit significantly better performance than existing models and also hold as good interpretability as human-designed models. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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OpenViewer: Openness-Aware Multi-View Learning

arXiv

引用

arXiv 2024年

作者： Du, Shide Fang, Zihan Tan, Yanchao Wang, Changwei Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China

Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box models remain poorly explained;2) Insufficient Generalization: Most models are not adapted to multi-view scenarios involving unknown categories. To address these challenges, we propose OpenViewer, an openness-aware multi-view learning framework with theoretical support. This framework begins with a Pseudo-Unknown Sample Generation Mechanism to efficiently simulate open multi-view environments and previously adapt to potential unknown samples. Subsequently, we introduce an Expression-Enhanced Deep Unfolding Network to intuitively promote interpretability by systematically constructing functional prior-mapping modules and effectively providing a more transparent integration mechanism for multi-view data. Additionally, we establish a Perception-Augmented Open-Set Training Regime to significantly enhance generalization by precisely boosting confidences for known categories and carefully suppressing inappropriate confidences for unknown ones. Experimental results demonstrate that OpenViewer effectively addresses openness challenges while ensuring recognition performance for both known and unknown samples. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Visual Instruction Tuning for Holistic and Regional Remote Sensing Imagery Comprehension

Visual Instruction Tuning for Holistic and Regional Remote S...

引用

Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Wei Zhang Miaoxin Cai Tong Zhang Zhuang Yin Xuerui Mao Advanced Research Institute of Multidisciplinary Sciences School of Mechatronical Engineering National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Yangtze Delta Region Academy of Beijing Institute of Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Recently, the visual instruction multimodal large language models (MLLMs) have been extensively studied in the nature scenario. However, current remote sensing (RS) MLLMs mainly focus on image-level understanding and typically allow interaction only through text instructions, resulting in limited accuracy and efficiency. To address those limitations, a visual instruction model is proposed in this article to extend MLLM’s fine-grained perception ability by incorporating bounding boxes with language instruction, aiming at achieving region-level visual understanding. To achieve this goal, a visual instruction dataset featuring multi-modal box-based region-text pairs is constructed. Furthermore, the visual instructions and images are encoded by the different encoders and subsequently fed into a large language model (LLM) along with text instruction tokens for tuning. Finally, experimental results demonstrate the proposed model’s promising performance in region-level image comprehension.

关键词： Visualization Analytical models Accuracy Large language models Data processing Sensors Tuning Remote sensing

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