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arXiv 2024年

作者： Liu, Peng Fei, Zesong Wang, Xinyi Zhou, Yiqing Zhang, Yan Liu, Fan The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The State Key Laboratory of Processors The Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

Mobile edge computing (MEC) is powerful to alleviate the heavy computing tasks in integrated sensing and communication (ISAC) systems. In this paper, we investigate joint beamforming and offloading design in a three-tier integrated sensing, communication and computation (ISCC) framework comprising one cloud server, multiple mobile edge servers, and multiple terminals. While executing sensing tasks, the user terminals can optionally offload sensing data to either MEC server or cloud servers. To minimize the execution latency, we jointly optimize the transmit beamforming matrices and offloading decision variables under the constraint of sensing performance. An alternating optimization algorithm based on multidimensional fractional programming is proposed to tackle the non-convex problem. Simulation results demonstrates the superiority of the proposed mechanism in terms of convergence and task execution latency reduction, compared with the state-of-the-art two-tier ISCC framework. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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VAction: A Lightweight and Integrated VR Training System for Authentic Film-Shooting Experience 25

VAction: A Lightweight and Integrated VR Training System for...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Wang, Shaocong Qu, Che Yu, Minjing Zhou, Chao Wang, Yuntao Wen, Yu-Hui Shi, Yuanchun Liu, Yong-Jin Department of Computer Science and Technology Tsinghua University Beijing China Film & TV Technology Department Beijing Film Academy Beijing China College of Intelligence and Computing Tianjin University Tianjin China Human Computer Interaction Technology Intelligent Information Processing Laboratory Institute of Software Chinese Acadamy of Sciences Beijing China School of Computer Science and Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400713941

The film industry exerts significant economic and cultural influence, and its rapid development is contingent upon the expertise of industry professionals, underscoring the critical importance of film-shooting education. However, this process typically necessitates multiple practice in complex professional venues using expensive equipment, presenting a significant obstacle for ordinary learners who struggle to access such training environments. Despite VR technology has already shown its potential in education, existing research has not addressed the crucial learning component of replicating the shooting process. Moreover, the limited functionality of traditional controllers hinder the fulfillment of the educational requirements. Therefore, we developed VAction VR system, combining high-fidelity virtual environments with a custom-designed controller to simulate the real-world camera operation experience. The system's lightweight design ensures cost-effective and efficient deployment. Experiment results demonstrated that VAction significantly outperforms traditional methods in both practice effectiveness and user experience, indicating its potential and usefulness in film-shooting education. © 2025 Copyright held by the owner/author(s).

关键词： Film Production Education Interaction Design Training System Virtual Reality

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OpenViewer: Openness-Aware Multi-View Learning

arXiv

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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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GP-Net: Global Perceptual Network with Adaptive Gradient Clipping for Emotional Image Classification

GP-Net: Global Perceptual Network with Adaptive Gradient Cli...

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6th International Conference on intelligent computing and Signal Processing (ICSP)

作者： Xiaohua Wang Jie Yang Min Hu Xia Wu Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Traditional image emotion recognition focuses only on the emotion information embedded in the subject or part of the image, while ignoring the global emotion information. In this paper, we propose a new network based on Vision Transformer: a global perception network, containing a main module and a background module. The main module enhances the weighting of objects by showing the interdependencies between modelled channels. The background module further searches for emotional information in the background. In addition, we employ multi-task learning and Adaptive Gradient Clipping to improve the network. Extensive experiments show that our proposed method outperforms the state-of-the-art methods for image sentiment analysis.

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Design and Implementation of a Web-based Collaborative Manufacturing System Based on Knowledge Graph 4

Design and Implementation of a Web-based Collaborative Manuf...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Yuan, Meng Wu, Huaiyu Shen, Zhen Han, Yunjun Zhou, Jiehan Yue, Zhijian Xiong, Gang Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan523808 China Qingdao Academy of Intelligent Industries Intelligent Manufacturing Center Qingdao266109 China University of Oulu Faculty of Information Technology and Electrical Engineering OuluTS352 Finland

ISBN: (纸本)9798350349252

Collaborative manufacturing is central to agile, intelligent, and cloud-based manufacturing paradigms. This paper presents the design and implementation of a web-based collaborative manufacturing system based on a knowledge graph (KG). The system achieves the full spectrum of knowledge extraction, fusion, and reasoning across the industrial chain, thereby enhancing efficiency and reducing operational costs. The proposed system constructs an industrial chain KG and multi-agent systems, offering comprehensive digital services across the manufacturing value chain. It supports the integration of business processes, data, and standard information for enterprise groups, presenting collaborative manufacturing data of the industrial chain in real-time visualization. The paper further discusses a method for real-time perception of anomalies in the collaborative manufacturing process, utilizing expert knowledge and data-driven insights to construct an operational risk KG. The system's architecture, workflow, and functional design are detailed, along with application cases in visual dynamic management. The conclusion underscores the system's potential to improve supply-demand chain management and operational efficiency in collaborative manufacturing. © 2024 IEEE.

关键词： Smart manufacturing

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Layout-based Causal Inference for Object Navigation

Layout-based Causal Inference for Object Navigation

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Sixian Zhang Xinhang Song Weijie Li Yubing Bai Xinyao Yu Shuqiang Jiang Key Lab of Intelligent Information Processing Laboratory of the Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing University of Chinese Academy of Sciences Beijing

Previous works for ObjectNav task attempt to learn the association (e.g. relation graph) between the visual inputs and the goal during training. Such association contains the prior knowledge of navigating in training environments, which is denoted as the experience. The experience performs a positive effect on helping the agent infer the likely location of the goal when the layout gap between the unseen environments of the test and the prior knowledge obtained in training is minor. However, when the layout gap is significant, the experience exerts a negative effect on navigation. Motivated by keeping the positive effect and removing the negative effect of the experience, we propose the layout-based soft Total Direct Effect (L-sTDE) framework based on the causal inference to adjust the prediction of the navigation policy. In particular, we propose to calculate the layout gap which is defined as the KL divergence between the posterior and the prior distribution of the object layout. Then the sTDE is proposed to appropriately control the effect of the experience based on the layout gap. Experimental results on AI2THOR, RoboTHOR, and Habitat demonstrate the effectiveness of our method. The code is available at https://***/sx-zhang/***.

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Graph attention based on contextual reasoning and emotion-shift awareness for emotion recognition in conversations

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Complex and intelligent Systems 2025年第7期11卷 1-20页

作者： Yang, Juan Wei, Puling Du, Xu Shen, Jun College of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Hubei Province Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan 430081 China National Engineering Research Center for E‑Learning Central China Normal University Wuhan 430079 China School of Computing and Information Technology University of Wollongong 2522 NSW Australia

Emotion recognition in conversations has recently emerged as a hot research topic owing to its increasingly important role in developing intelligent empathy services. Thoroughly exploring the conversational context and accurately capturing emotion-shift information are highly crucial for accurate emotion recognition in conversations. However, existing studies generally failed to fully understand the complex conversational context due to their insufficient capabilities in extracting and integrating emotional cues. Moreover, they mainly focused on the speaker’s emotion inertia while paying less attention to explore multi-perspective emotion-shift patterns. To address these limitations, this study proposes a novel multimodal approach, namely, GAT-CRESA (Graph ATtention based on Contextual Reasoning and Emotion-Shift Awareness). Specifically, the multi-turn global contextual reasoning module iteratively performs contextual perception and cognitive reasoning for efficiently understanding the global conversational context. Then, GAT-CRESA explores emotion-shift information among utterances from both the speaker-dependent and the global context-based perspectives. Next, the emotion-shift awareness graphs are constructed for extracting significant local-level conversational context, where edge relations are determined by the learnt emotion-shift labels. Finally, the outputs of graphs are concatenated for final emotion recognition. The loss of emotion prediction task is combined together with those of two perspective’s emotion-shift learning for guiding the training process. Experimental results show that our GAT-CRESA achieves new state-of-art records with 72.77% ACC and 72.81% wa-F1 on IEMOCAP, and 65.44% ACC and 65.04% wa-F1 on MELD, respectively. The ablation results also indicate the effectiveness and rationality of each component in our approach. © The Author(s) 2025.

关键词： Contextual reasoning Emotion recognition in conversations (ERC) Emotion-shift Graph attention

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Bi-Level Meta-Learning for Few-Shot Domain Generalization

Bi-Level Meta-Learning for Few-Shot Domain Generalization

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xiaorong Qin Xinhang Song Shuqiang Jiang Key Lab of Intelligent Information Processing Laboratory of the Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing University of Chinese Academy of Sciences Beijing

The goal of few-shot learning is to learn the generalization from seen to unseen data with only a few samples. Most previous few-shot learning methods focus on learning the generalization within particular domains. However, the more practical scenarios may also require the generalization ability across domains. In this paper, we study the problem of few-shot domain generalization (FSDG), which is a more challenging variant of few-shot classification. FSDG requires additional generalization with larger gap from seen domains to unseen domains. We address FSDG problem by meta-learning two levels of meta-knowledge, where the lower-level meta-knowledge is domain-specific embedding spaces as subspaces of a base space for intra-domain generalization, and the upper-level meta-knowledge is the base space and a prior subspace over domain-specific spaces for inter-domain generalization. We formulate the two levels of meta-knowledge learning problem with bi-level optimization, and further develop an optimization algorithm without higher-order derivative information to solve it. We demonstrate our method is significantly superior to the previous works by evaluating it on the widely used benchmark Meta-Dataset.

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Wavelet-Driven Masked Image Modeling: A Path to Efficient Visual Representation 39

Wavelet-Driven Masked Image Modeling: A Path to Efficient Vi...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xiang, Wenzhao Liu, Chang Yu, Hongyang Chen, Xilin Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Peng Cheng Laboratory China University of Chinese Academy of Sciences China Department of Electronic Engineering Shanghai Jiao Tong University China

ISBN: (纸本)157735897X

Masked Image Modeling (MIM) has garnered significant attention in self-supervised learning, thanks to its impressive capacity to learn scalable visual representations tailored for downstream tasks. However, images inherently contain abundant redundant information, leading the pixel-based MIM reconstruction process to focus excessively on finer details such as textures, thus prolonging training times unnecessarily. Addressing this challenge requires a shift towards a compact representation of features during MIM reconstruction. Frequency domain analysis provides a promising avenue for achieving compact image feature representation. In contrast to the commonly used Fourier transform, wavelet transform not only offers frequency information but also preserves spatial characteristics and multi-level features of the image. Additionally, the multi-level decomposition process of wavelet transformation aligns well with the hierarchical architecture of modern neural networks. In this study, we leverage wavelet transform as a tool for efficient representation learning to expedite the training process of MIM. Specifically, we conduct multi-level decomposition of images using wavelet transform, utilizing wavelet coefficients from different levels to construct distinct reconstruction targets representing various frequencies and scales. These reconstruction targets are then integrated into the MIM process, with adjustable weights assigned to prioritize the most crucial information. Extensive experiments demonstrate that our method achieves comparable or superior performance across various downstream tasks while exhibiting higher training efficiency. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Wavelet decomposition

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Improving Molecular Graph Generation with Flow Matching and Optimal Transport

arXiv

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arXiv 2024年

作者： Hou, Xiaoyang Zhu, Tian Ren, Milong Bu, Dongbo Gao, Xin Zhang, Chunming Sun, Shiwei Key Laboratory of Intelligent Information Processing Institute of Computing Technology School of Computer Science and Technology University of Chinese Academy of Sciences China Central China Research Institute for Artificial Intelligence Technologies China Western Institute of Computing Technology Phil Rivers Technology King Abdullah University of Science and Technology Saudi Arabia

Generating molecular graphs is crucial in drug design and discovery but remains challenging due to the complex interdependencies between nodes and edges. While diffusion models have demonstrated their potentiality in molecular graph design, they often suffer from unstable training and inefficient sampling. To enhance generation performance and training stability, we propose GGFlow, a discrete flow matching generative model incorporating optimal transport for molecular graphs and it incorporates an edge-augmented graph transformer to enable the direct communications among chemical bounds. Additionally, GGFlow introduces a novel goal-guided generation framework to control the generative trajectory of our model, aiming to design novel molecular structures with the desired properties. GGFlow demonstrates superior performance on both unconditional and conditional molecule generation tasks, outperforming existing baselines and underscoring its effectiveness and potential for wider application. © 2024, CC BY.

关键词： Molecular docking

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