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International Conference on computer Vision (ICCV)

作者： Ruihan Xu Haokui Zhang Wenze Hu Shiliang Zhang Xiaoyu Wang National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Intellifusion Harbin Institute of Technology Shenzhen

Transformers have shown great potential in various computer vision tasks. By borrowing design concepts from transformers, many studies revolutionized CNNs and showed remarkable results. This paper falls in this line of studies. Specifically, we propose a new convolutional neural network, ParCNetV2, that extends the research line of ParCNetV1 by bridging the gap between CNN and ViT. It introduces two key designs: 1) Oversized Convolution (OC) with twice the size of the input, and 2) Bifurcate Gate Unit (BGU) to ensure that the model is input adaptive. Fusing OC and BGU in a unified CNN, ParCNetV2 is capable of flexibly extracting global features like ViT, while maintaining lower latency and better accuracy. Extensive experiments demonstrate the superiority of our method over other convolutional neural networks and hybrid models that combine CNNs and transformers. The code are publicly available at https://***/XuRuihan/ParCNetV2.

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AUTOMATIC CHINESE NATIONAL PENTATONIC MODES RECOGNITION USING CONVOLUTIONAL NEURAL NETWORK 23

AUTOMATIC CHINESE NATIONAL PENTATONIC MODES RECOGNITION USIN...

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23rd International Society for Music information Retrieval Conference, ISMIR 2022

作者： Wang, Zhaowen Che, Mingjin Yang, Yue Meng, Wenwu Li, Qinyu Xia, Fan Li, Wei Department of Music AI and Information Technology Central Conservatory of Music China College of Experimental Art Sichuan Conservatory of Music China School of Computer Science and Technology Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

ISBN: (纸本)9781732729926

Chinese national pentatonic modes, with five tones of Gong, Shang, Jue, Zhi and Yu as the core, play an essential role in traditional Chinese music culture. After the early twentieth century, with the development of new Chinese music, the ancient Chinese theory of scales gradually developed into a new pentatonic modes theory under the influence of western music. In this paper, we briefly introduce our self-built CNPM (Chinese National Pentatonic Modes) Dataset, then design residual convolutional neural network models to identify which TongGong system the mode belongs, the pitch of tonic, the mode pattern and the mode type from audio signals, in combination with musical domain knowledge. We use both single-task and multi-task models with three strategies for identification, and compare them with a simple template-based baseline method. In experiments, we use seven accuracy metrics to evaluate the models. The results on identifying both the tonic pitch and the pattern of mode correctly achieve an average accuracy of 69.65%. As an initial research on automatic Chinese national pentatonic modes recognition, this work will contribute to the development of multicultural music information retrieval, computational ethnomusicology and five-tone music therapy. © Z. Wang, M. Che, Y. Yang, W. Meng, Q. Li, F. Xia and W. Li.

关键词： Convolutional neural networks

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MIXED-ATTENTION AUTO ENCODER FOR MULTI-CLASS INDUSTRIAL ANOMALY DETECTION

arXiv

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arXiv 2023年

作者： Liu, Jiangqi Wang, Feng Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University Shanghai China

Most existing methods for unsupervised industrial anomaly detection train a separate model for each object category. This kind of approach can easily capture the category-specific feature distributions, but results in high storage cost and low training efficiency. In this paper, we propose a unified mixed-attention auto encoder (MAAE) to implement multi-class anomaly detection with a single model. To alleviate the performance degradation due to the diverse distribution patterns of different categories, we employ spatial attentions and channel attentions to effectively capture the global category information and model the feature distributions of multiple classes. Furthermore, to simulate the realistic noises on features and preserve the surface semantics of objects from different categories which are essential for detecting the subtle anomalies, we propose an adaptive noise generator and a multi-scale fusion module for the pre-trained features. MAAE delivers remarkable performances on the benchmark dataset compared with the state-of-the-art *** Codes 68T45 Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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LVOS: A Benchmark for Long-term Video Object Segmentation

LVOS: A Benchmark for Long-term Video Object Segmentation

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International Conference on computer Vision (ICCV)

作者： Lingyi Hong Wenchao Chen Zhongying Liu Wei Zhang Pinxue Guo Zhaoyu Chen Wenqiang Zhang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Academy for Engineering and Technology Fudan University

Existing video object segmentation (VOS) benchmarks focus on short-term videos which just last about 3-5 seconds and where objects are visible most of the time. These videos are poorly representative of practical applications, and the absence of long-term datasets restricts further investigation of VOS on the application in realistic scenarios. So, in this paper, we present a new benchmark dataset named LVOS, which consists of 220 videos with a total duration of 421 minutes. To the best of our knowledge, LVOS is the first densely annotated long-term VOS dataset. The videos in our LVOS last 1.59 minutes on average, which is 20 times longer than videos in existing VOS datasets. Each video includes various attributes, especially challenges deriving from the wild, such as long-term reappearing and cross-temporal similar objeccts. Based on LVOS, we assess existing video object segmentation algorithms and propose a Diverse Dynamic Memory network (DDMemory) that consists of three complementary memory banks to exploit temporal information adequately. The experimental results demonstrate the strength and weaknesses of prior methods, pointing promising directions for further study. Data and code are available at https://***/***/.

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A Simulation-Based Framework for Urban Traffic Accident Detection

A Simulation-Based Framework for Urban Traffic Accident Dete...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Haohan Luo Feng Wang School of Computer Science and Technology Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China

Urban traffic accident detection in surveillance videos is an essential task in intelligent traffic monitoring. The current deep-learning based approaches suffer from the lack of large-scale traffic accident video datasets due to the privacy concerns. In this paper, we propose a framework to synthesize traffic videos containing both normal traffic and accident events by simulating the real urban traffic scenarios. By introducing various weather conditions, viewpoints of surveillance cameras, and traffic statuses, we can simulate the complex real-world traffic scenarios. The vehicles are automatically controlled to behave normally or trigger accidents randomly. Based on our framework, we automatically generate a synthetic dataset containing 1,100 distinct traffic videos with video-level annotations. For accident detection, we observe that the existing reconstruction-based models produce many false alarms by predicting the pseudo accidents (non-accident anomalous traffic events) as real accidents. To address this ambiguity problem, we propose an improved approach by employing multiple instance learning to refine the detection results. Our experiments demonstrate the great potentials of our proposed framework in boosting the performance of accident detection on real-world datasets. We make our code and dataset publicly available at https://***/hankluo2/UrbanTrafficAccidentDetection.

关键词： Training Surveillance Traffic control Signal processing Boosting Task analysis Speech processing

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Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction

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IEEE Transactions on Medical Imaging 2025年第6期44卷 2504-2514页

作者： Ma, Chenglong Li, Zilong Li, Yuanlin Han, Jing Zhang, Junping Zhang, Yi Liu, Jiannan Shan, Hongming Fudan University Institute of Science and Technology for Brain-inspired Intelligence Moe Frontiers Center for Brain Science Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Shanghai200433 China Fudan University Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Shanghai200433 China Shanghai Ninth People's Hospital Department of Oral Maxillofacial Head and Neck Oncology China Shanghai Jiao Tong University School of Medicine College of Stomatology China Shanghai Key Laboratory of Stomatology Shanghai Research Institute of Stomatology National Center for Stomatology National Clinical Research Center for Oral Diseases Shanghai200011 China Sichuan University School of Cyber Science and Engineering Chengdu Sichuan610065 China

Metal artifacts in computed tomography (CT) images can significantly degrade image quality and impede accurate diagnosis. Supervised metal artifact reduction (MAR) methods, trained using simulated datasets, often struggle to perform well on real clinical CT images due to a substantial domain gap. Although state-of-the-art semi-supervised methods use pseudo ground-truths generated by a prior network to mitigate this issue, their reliance on a fixed prior limits both the quality and quantity of these pseudo ground-truths, introducing confirmation bias and reducing clinical applicability. To address these limitations, we propose a novel radiologist-in-the-loop self-training framework for MAR, termed RISE-MAR, which can integrate radiologists' feedback into the semi-supervised learning process, progressively improving the quality and quantity of pseudo ground-truths for enhanced generalization on real clinical CT images. For quality assurance, we introduce a clinical quality assessor model that emulates radiologist evaluations, effectively selecting high-quality pseudo ground-truths for semi-supervised training. For quantity assurance, our self-training framework iteratively generates additional high-quality pseudo groundtruths, expanding the clinical dataset and further improving model generalization. Extensive experimental results on multiple clinical datasets demonstrate the superior generalization performance of our RISE-MAR over state-of-the-art methods, advancing the development of MAR models for practical application. © 2025 IEEE.

关键词： computerized tomography

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A New Prediction Strategy for Dynamic Multi-objective Optimization Using Diffusion Model

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IEEE Transactions on Evolutionary Computation 2025年

作者： Wang, Feng Xie, Jinsong Zhou, Aimin Tang, Ke Wuhan University School of Computer Science Hubei430072 China East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing The Department of Computer Science and Technology Shanghai200241 China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China

To solve dynamic multi-objective optimization problems (DMOPs), the optimization algorithms are required to track the movement of the Pareto set after the environmental changes effectively. Many prediction-based dynamic multi-objective evolutionary algorithms (DMOEAs) have been proposed to address this challenge by utilizing environmental information for population reinitialization. However, when environmental changes are complex, irregular, and severe, the solutions and information during the evolution process often contain noise, making it difficult for prediction-based DMOEAs to accurately predict and reinitialize the population. To address this issue, we propose a novel dynamic multi-objective evolutionary algorithm (DM-DMOEA) which uses a diffusion model based prediction strategy. In DM-DMOEA, to improve the prediction accuracy, the diffusion model is introduced to extract the relationships of high-quality solutions and reinitialize the population, and a PS estimation method is employed to integrate both historical and new environmental information, providing a set of high-quality solutions for diffusion model training. To speed up the response time, a variational autoencoder (VAE) is used to map the decision space to a latent space, which can reduce the diffusion model size and accelerate the diffusion process. To evaluate the effectiveness of the proposed DM-DMOEA on DMOPs, comprehensive experiments are conducted on several benchmarks and a practical problem. The results show that the DM-DMOEA outperforms other four state-of-the-art DMOEAs in most cases. © 1997-2012 IEEE.

关键词： Optimization algorithms

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Mamba-VA: A Mamba-based Approach for Continuous Emotion Recognition in Valence-Arousal Space

arXiv

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arXiv 2025年

作者： Liang, Yuheng Liu, Feng Liu, Mingzhou Yao, Yu Wang, Zheyu Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology China Nanjing University of Science and Technology ZiJin College School of Computer and Artificial Intelligence China

Continuous Emotion Recognition (CER) plays a crucial role in intelligent human-computer interaction, mental health monitoring, and autonomous driving. Emotion modeling based on the Valence-Arousal (VA) space enables a more nuanced representation of emotional states. However, existing methods still face challenges in handling long-term dependencies and capturing complex temporal dynamics. To address these issues, this paper proposes a novel emotion recognition model, Mamba-VA, which leverages the Mamba architecture to efficiently model sequential emotional variations in video frames. First, the model employs a Masked Autoencoder (MAE) to extract deep visual features from video frames, enhancing the robustness of temporal information. Then, a Temporal Convolutional Network (TCN) is utilized for temporal modeling to capture local temporal dependencies. Subsequently, Mamba is applied for long-sequence modeling, enabling the learning of global emotional trends. Finally, a fully connected (FC) layer performs regression analysis to predict continuous valence and arousal values. Experimental results on the Valence-Arousal (VA) Estimation task of the 8th competition on Affective Behavior Analysis in-the-wild (ABAW) demonstrate that the proposed model achieves valence and arousal scores of 0.5362 (0.5036) and 0.4310 (0.4119) on the validation (test) set, respectively, outperforming the baseline. The source code is available on GitHub: https://***/FreedomPuppy77/Charon. Copyright © 2025, The Authors. All rights reserved.

关键词： Regression analysis

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A Sparsity Knowledge Transfer-Based Evolutionary Algorithm for Large-Scale Multitasking Multi-Objective Optimization

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IEEE Transactions on Evolutionary Computation 2024年

作者： Wu, Chengming Tian, Ye Zhang, Limiao Xiang, Xiaoshu Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230039 China Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China

Multitasking multi-objective evolutionary algorithms (MMEAs) have been extensively studied in the past decade, which mainly concentrate on multitasking multi-objective optimization problems (MMOPs) with dozens of decision variables. Nevertheless, many real-world MMOPs have thousands of decision variables and are of sparse nature, which are regarded as large-scale MMOPs (LSMMOPs) in this study. To address LSMMOPs, a sparsity knowledge transfer-based evolutionary multi-objective algorithm, termed EMO-SKT, is proposed for efficiently finding high-quality sparse solutions of LSMMOPs. For each target optimization task, a sparsity knowledge transfer strategy extracts sparse distribution information from a source task and incorporates the information into the target task for two types of sparsity knowledge: variable importance and sparse degree. The variable importance is utilized to produce high-quality sparse solutions during the evolutionary search for EMO-SKT, while the sparse degree facilitates the reduction of search space and thus speeds up the convergence of the evolutionary search. Experimental results on eight benchmark problems and six practical LSMMOPs demonstrate the effectiveness of the sparsity knowledge transfer strategy. Furthermore, the proposed EMO-SKT is capable of efficiently finding high-quality sparse solutions on an LSMMOP with over 1000 decision variables. In comparison with five state-of-the-art multitasking or sparse optimization algorithms, the proposed EMO-SKT exhibits superior performance in terms of both solution quality and search efficiency. © 1997-2012 IEEE.

关键词： Optimization algorithms

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A Single-Cell Clustering Algorithm Based on Structure Perturbation Non-Negative Matrix Factorization

A Single-Cell Clustering Algorithm Based on Structure Pertur...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jiang, Hanjing Wang, Meineng Zhang, Luping Huang, Yu-An Li, Yangyuan Huang, Yabing Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Hubei Wuhan430074 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Yichun University School of Mathematics and Computer Science Yichun336000 China East China University of Technology School of Information Engineering Nanchang330013 China Northwestern Polytechnical University School of Computer Science Xi'an710129 China Renmin Hospital Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350386226

The advent of single-cell RNA sequencing (scRNA-seq) has facilitated the acquisition of high-resolution data regarding cell heterogeneity across various tissues. A fundamental and critical step in the analysis of scRNA-seq data is cell type identification. An appropriate graph construction strategy can reflect the topological structure between cells and allows for detailed exploration of the relationships between cells and genes. Given the sparsity of scRNA-seq data and the pronounced noise generated by shallow sequencing, choosing an appropriate graph construction strategy has become a significant challenge. In this paper, we propose a single-cell clustering method, named Sc-PNNMF, based on structural perturbation of nonnegative matrix factorization. Sc-PNNMF employs a structural perturbation algorithm to optimize the gene expression matrix. The optimized matrix guides the matrix factorization process, effectively overcoming the impact of data sparsity and significant noise in gene expression data on the graph construction strategy. We compared the clustering abilities of Sc-PNNMF with five other methods on ten real datasets. © 2024 IEEE.

关键词： Non-negative matrix factorization

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