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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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RainyGS: Efficient Rain Synthesis with Physically-Based Gaussian Splatting

arXiv

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arXiv 2025年

作者： Dai, Qiyu Ni, Xingyu Shen, Qianfan Chen, Wenzheng Chen, Baoquan Chu, Mengyu School of Intelligence Science and Technology Peking University China School of Computer Science Peking University China School of EECS Peking University China Wangxuan Institute of Computer Technology Peking University China State Key Laboratory of Multimedia Information Processing Peking University China State Key Laboratory of General Artificial Intelligence Peking University China

We consider the problem of adding dynamic rain effects to in-the-wild scenes in a physically-correct manner. Recent advances in scene modeling have made significant progress, with NeRF and Gaussian Splatting techniques emerging as powerful tools for reconstructing complex scenes. However, while effective for novel view synthesis, these methods typically struggle with challenging scene editing tasks, such as physics-based rain simulation. In contrast, traditional physics-based simulations can generate realistic rain effects, such as raindrops and splashes, but they often rely on skilled artists to carefully set up high-fidelity scenes. This process lacks flexibility and scalability, limiting its applicability to broader, open-world environments. In this work, we introduce RainyGS, a novel approach that leverages the strengths of both physics-based modeling and Gaussian Splatting to generate photorealistic, dynamic rain effects in open-world scenes with physical accuracy. At the core of our method is the integration of physically-based raindrop and shallow water simulation techniques within the fast Gaussian Splatting rendering framework, enabling realistic and efficient simulations of raindrop behavior, splashes, and reflections. Our method supports synthesizing rain effects at over 30 fps, offering users flexible control over rain intensity—from light drizzles to heavy downpours. We demonstrate that RainyGS performs effectively for both real-world outdoor scenes and large-scale driving scenarios, delivering more photorealistic and physically-accurate rain effects compared to state-of-the-art methods. Project page can be found at https: //***/RainyGS/. Copyright © 2025, The Authors. All rights reserved.

关键词： Gaussian distribution

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PRF-Net: A Progressive Remote Sensing Image Registration and Fusion Network

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IEEE Transactions on Neural Networks and Learning Systems 2025年第5期36卷 9437-9450页

作者： Xiong, Zhangxi Li, Wei Zhao, Xiaobin Zhang, Baochang Tao, Ran Du, Qian Beijing Institute of Technology School of Information and Electronics Beijing100081 China Zhongguancun Laboratory Beijing102206 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China Institute of Artificial Intelligence Beihang University Beijing100191 China Mississippi State University Department of Electrical and Computer Engineering Mississippi StateMS39762 United States

Most of the existing fusion algorithms are not robust to unregistered input images. Even after image registration, nonlinear nonregistration may persist in the local areas of the images, leading to poor quality in the fused image. So, as to tackle these challenges, a progressive remote sensing image registration and fusion network is proposed in this article, and named PRF-Net, which is particularly useful when two images are from different platforms. First, a registration network is designed to register the input image patches, which includes a global spatial transform network (GSTN) and a local spatial warp network (LSWN). The GSTN is primarily used for coarse registration, applying rigid transformation to globally align the input images. After coarse registration, the preliminarily registered moving image is input into the LSWN for local fine-tuning to maximize correlation between the input image patches. Subsequently, the fine registered images are degraded and input into the fusion network to generate the fused image. To maintain sufficient spectral and spatial information of the fused image, a multiscale feature extraction (MSFE) block with a highly interpretable spatial details attention (SDA) block is designed, which can enhance the ability of fusion network to extract and preserve spatial details and spectral information. Three groups of experiments conducted on four types of remote sensing images give evidence of that the proposed PRF-Net exhibits excellent performance in both reduced and full resolutions, showcasing its outstanding registration and fusion quality. © 2012 IEEE.

关键词： Image registration

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Ultra-Low Concentration Biosensing Harnessing Aulter-Townes Doublets in Meta Waveguide Ring Resonators

SSRN

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SSRN 2025年

作者： Li, Wanxin Li, Jiewen Li, Rui Chen, Gina Jinna Shum, Perry Ping Zou, Yi Chen, Ray T. Dong, Yongkang Xu, Xiaochuan National Key Laboratory of Laser Spatial Information Guangdong Provincial Key Laboratory of Integrated Photonic-Electronic Chip Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Harbin Institute of Technology Xili University Town Harbin Institute of Technology campus Guangdong Shenzhen518055 China State Key Laboratory of Optical Fiber and Cable Manufacture Technology Guangdong Key Laboratory of Integrated Optoelectronics Intellisense Department of EEE Southern University of Science and Technology Shenzhen518055 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Department of Electrical and Computer Engineering The University of Texas at Austin 10100 Burnet Rd. AustinTX78758 United States

Pushing sensitivity to ultra-low concentrations unlocks new possibilities in sample analysis. However, the limit of detection (LOD) in highly sensitive optical sensors is often constrained by noise. In this study, we address this limitation by leveraging Autler-Townes Splitting (ATS) within meta waveguide microring resonators (MWMRs) for self-referenced biological detection. This approach effectively suppresses noise, achieving a minimal baseline drift of 4.6 fm/min without the need for sophisticated environmental controls. By combining high sensitivity with robust noise suppression, ATS-based MWMRs enable the detection of streptavidin at an ultra low concentration of 0.01 pg/mL, with the blank solution serving as the baseline. These results highlight the significant potential of ATS-based MWMRs as a robust and versatile platform for biomolecule detection at ultra-low concentrations, paving the way for advancements in biological research and medical diagnostics. © 2025, The Authors. All rights reserved.

关键词： Optical resonators

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A Knowledge Acquisition Method for Consumer Electronics Based on Triadic Hesitant Fuzzy Concept

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IEEE Transactions on Consumer Electronics 2025年

作者： Xin, Xianwei Zhao, Ye Wang, Chenyang Xu, Jiucheng Taleb, Tarik Henan Normal University School of Computer and Information Engineering Xinxiang453007 China Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education Taiyuan237016 China Computer and Software Engineering School of College of Shenzhen University Guangdong Shenzhen518060 China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Henan Province 453007 China Ruhr University Bochum Faculty of Electrical Engineering and Information Technology Bochum44801 Germany

With the growing intelligence and personalization of consumer electronic products, extracting effective information has become a key challenge in Consumer Electronics (CE). Triadic Concept Analysis (TCA), an important extension of Formal Concept Analysis (FCA), is widely applied but often struggles to handle the hesitant fuzzy characteristics in real-world data. To address this gap, we propose a Triadic Hesitant Fuzzy Concept (THFC) model. First, we introduce the concept of a triadic hesitant fuzzy background, integrating the fuzzy and hesitant nature of data in the CE environment. We propose a knowledge acquisition method based on THFC, which constructs a multi-granularity knowledge space with a dynamic adjustment mechanism. This method captures subtle distinctions between different types of information and their hierarchical structures in the context of CE, offering significant support for knowledge discovery and decision-making in complex environments. Building on this, we design a variable-precision hybrid similarity measurement (VP-HSM) method to extract core, adjunct, and edge concepts, thus providing more precise and structured knowledge for data analysis and decision-making in CE. Experimental results on publicly available datasets show that the proposed method significantly improves classification accuracy and stability, effectively handling hesitant fuzzy data and improving the accuracy of concept extraction. © 1975-2011 IEEE.

关键词： Data accuracy

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CTNet: A Convolutional Transformer Network for Color Image Steganalysis

引用

Journal of computer Science and technology 2025年第2期40卷 413-427页

作者： Wei, Kang-Kang Luo, Wei-Qi Tan, Shun-Quan Huang, Ji-Wu School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China

Compared with convolutional neural network (CNN), Transformer can obtain global receptive field features more effectively and has recently achieved great success in natural language processing and computer vision. Due to the particularity of steganography, however, almost all existing steganalytic networks just employ CNN with local receptive fields to detect embedding artifacts. In this paper, we propose a novel convolutional Transformer network for color image steganalysis. Specifically, we firstly obtain various image residuals for each color channel of an input image in the pre-processing module. To capture more comprehensive steganalytic features, the truncated residuals after channel concatenation will pass through a feature extraction module composed of a CNN group and a Transformer group. The CNN group aims to extract local receptive fields features, while the Transformer group with multi-head self-attention as the key tries to extract global steganalytic features. Finally, we employ a global covariance pooling (GCP) and two fully-connected (FC) layers with dropout for classification. Extensive comparative experiments demonstrate that the proposed method can significantly improve the detection performances in color image steganalysis and achieve state-of-the-art results. Although the proposed method is originally designed for color images, it can also obtain competitive results for grayscale images compared with the current best detector. In addition, we provide numerous ablation studies to verify the rationality of the proposed network architecture. © Institute of Computing technology, Chinese Academy of Sciences 2025.

关键词： Convolutional neural networks

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Instance-Level Moving Object Segmentation from a Single Image with Events

arXiv

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arXiv 2025年

作者： Wan, Zhexiong Fan, Bin Hui, Le Dai, Yuchao Lee, Gim Hee School of Electronics and Information Northwestern Polytechnical University Shaanxi Key Laboratory of Information Acquisition and Processing Shaanxi Xi’an710129 China Department of Computer Science National University of Singapore 117417 Singapore National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Beijing100871 China

Moving object segmentation plays a crucial role in understanding dynamic scenes involving multiple moving objects, while the difficulties lie in taking into account both spatial texture structures and temporal motion cues. Existing methods based on video frames encounter difficulties in distinguishing whether pixel displacements of an object are caused by camera motion or object motion due to the complexities of accurate image-based motion modeling. Recent advances exploit the motion sensitivity of novel event cameras to counter conventional images’ inadequate motion modeling capabilities, but instead lead to challenges in segmenting pixel-level object masks due to the lack of dense texture structures in events. To address these two limitations imposed by unimodal settings, we propose the first instance-level moving object segmentation framework that integrates complementary texture and motion cues. Our model incorporates implicit cross-modal masked attention augmentation, explicit contrastive feature learning, and flow-guided motion enhancement to exploit dense texture information from a single image and rich motion information from events, respectively. By leveraging the augmented texture and motion features, we separate mask segmentation from motion classification to handle varying numbers of independently moving objects. Through extensive evaluations on multiple datasets, as well as ablation experiments with different input settings and real-time efficiency analysis of the proposed framework, we believe that our first attempt to incorporate image and event data for practical deployment can provide new insights for future work in event-based motion related works. The source code with model training and pre-trained weights is released at https://npucvr. ***/EvInsMOS. Copyright © 2025, The Authors. All rights reserved.

关键词： Pixels

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QoS-Aware Joint Massive Random Access Control and Resource Allocation With Colliding Preamble Reuse for Prioritized IoT

引用

IEEE Transactions on Vehicular technology 2025年

作者： Guo, Ziming Zhu, Xu Wei, Zhongxiang Cao, Jie Jiang, Yufei Lau, Vincent K.N. Sun, Sumei Harbin Institute of Technology School of Information Science and Technology Shenzhen518055 China Shenzhen Municipal Key Laboratory of AIoT Communication Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen518055 China TongJi University College of Electronic and Information Engineering Shanghai Institute of Intelligent Science and Technology China Southeast University National Mobile Communications Research Laboratory Nanjing China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering 999077 Hong Kong Institute for Infocomm Research Agency for Science 138632 Singapore

Enabling the current Internet-of-Things (IoT) and the future Internet-of-Everything (IoE) paradigms in the existing networks is challenging due to the low access efficiency amidst massive random access (RA) requests and heterogeneous applications. To address this, we investigate a novel prioritized RA control technique for massive IoT networks that cooperatively identifies adaptive access class barring (ACB) factors and preamble allocations, taking into account IoT devices' heterogeneity and limited RA resources. We derived the closed-form expression for the preamble utilization ratio (PAUR), incorporating the reusable colliding preambles and the early-stage collision detection (es-CD) technique. Two priority-guided RA request transition models are established, precisely translating RA priorities into a flexible backoff window size. Our Markov-Chain-based analytical model enables a comprehensive RA performance evaluation, demonstrating the superior performance of the proposed QoS-Aware Joint Access Control and Resource Allocation (QA-JACRA) in terms of PAUR, access delay, and throughput. This is achieved at a lower computational complexity compared to existing RA control schemes, providing a promising design approach for the activation of massive IoT devices. © 2025 IEEE.

关键词： Markov chains

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SVFormer: A Direct Training Spiking Transformer for Efficient Video Action Recognition

SVFormer: A Direct Training Spiking Transformer for Efficie...

引用

4th International Workshop on Human Brain and Artificial Intelligence, HBAI 2024

作者： Yu, Liutao Huang, Liwei Zhou, Chenlin Zhang, Han Ma, Zhengyu Zhou, Huihui Tian, Yonghong AI Department Pengcheng Laboratory Shenzhen China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (纸本)9789819640003

Video action recognition (VAR) plays crucial roles in various domains such as surveillance, healthcare, and industrial automation, making it highly significant for the society. Consequently, it has long been a research spot in the computer vision field. As artificial neural networks (ANNs) are flourishing, convolution neural networks (CNNs), including 2D-CNNs and 3D-CNNs, as well as variants of the vision transformer (ViT), have shown impressive performance on VAR. However, they usually demand huge computational cost due to the large data volume and heavy information redundancy introduced by the temporal dimension. To address this challenge, some researchers have turned to brain-inspired spiking neural networks (SNNs), such as recurrent SNNs and ANN-converted SNNs, leveraging their inherent temporal dynamics and energy efficiency. Yet, current SNNs for VAR also encounter limitations, such as nontrivial input preprocessing, intricate network construction/training, and the need for repetitive processing of the same video clip, hindering their practical deployment. In this study, we innovatively propose the directly trained SVFormer (Spiking Video transFormer) for VAR. SVFormer integrates local feature extraction, global self-attention, and the intrinsic dynamics, sparsity, and spike-driven nature of SNNs, to efficiently and effectively extract spatio-temporal features. We evaluate SVFormer on two RGB datasets (UCF101, NTU-RGBD60) and one neuromorphic dataset (DVS128-Gesture), demonstrating comparable performance to the mainstream models in a more efficient way. Notably, SVFormer achieves a top-1 accuracy of 84.03% with ultra-low power consumption (21 mJ/video) on UCF101, which is state-of-the-art among directly trained deep SNNs, showcasing significant advantages over prior models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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A Novel Construction of Two-Dimensional Z-Complementary Array Code Sets Based on Extended Boolean Functions

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IEEE Transactions on Vehicular technology 2025年

作者： Tian, Liying Song, Xiaoshi Li, Yubo Zhang, Haijun Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110189 China Yanshan University School of Information Science and Engineering Qinhuangdao066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao066004 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Two-dimensional (2-D) array sets with good 2-D correlation properties have received considerable attention in wireless communication systems. This paper focuses on 2-D Z-complementary array code sets (ZCACSs), which have ideal 2-D correlation properties within a rectangular zone around the origin. By developing a theory linking 2-D extended Boolean functions with 2-D arrays, we establish an efficient construction of 2-D ZCACSs. In contrast to previous work, the proposed construction is capable of producing 2-D ZCACSs with new flexible parameters. Constructions of three-dimensional (3-D) and higher-dimensional ZCACSs are further investigated. We remark that 2-D and higher-dimensional complete complementary codes (CCCs) can also be generated from the proposed constructions. © 1967-2012 IEEE.

关键词： Codes (symbols)

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