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arXiv 2024年

作者： He, Xuanhua Li, Lang Wang, Yingying Zheng, Hui Cao, Ke Yan, keyu Li, Rui Xie, Chengjun Zhang, Jie Zhou, Man Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen University Xiamen361102 China University of Science and Technology of China China The Intelligent Agriculture Engineering Laboratory of Anhui Province Institute of Intelligent Machines Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China

Image restoration aims to reconstruct the latent clear images from their degraded versions. Despite the notable achievement, existing methods predominantly focus on handling specific degradation types and thus require specialized models, impeding real-world applications in dynamic degradation scenarios. To address this issue, we propose Large Model Driven Image Restoration framework (LMDIR), a novel multiple-in-one image restoration paradigm that leverages the generic priors from large multi-modal language models (MMLMs) and the pretrained diffusion models. In detail, LMDIR integrates three key prior knowledges: 1) global degradation knowledge from MMLMs, 2) scene-aware contextual descriptions generated by MMLMs, and 3) fine-grained high-quality reference images synthesized by diffusion models guided by MMLM descriptions. Standing on above priors, our architecture comprises a query-based prompt encoder, degradation-aware transformer block injecting global degradation knowledge, content-aware transformer block incorporating scene description, and reference-based transformer block incorporating fine-grained image priors. This design facilitates single-stage training paradigm to address various degradations while supporting both automatic and user-guided restoration. Extensive experiments demonstrate that our designed method outperforms state-of-the-art competitors on multiple evaluation benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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Collaborative Inference for MEC Services Based on Multimodal Deep Neural Network

Collaborative Inference for MEC Services Based on Multimodal...

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IEEE International Conference on Communications in China (ICCC)

作者： Yu Liu Chuxuan Wang Yilin Xiao Zefang Lv Liang Xiao Xiangyang Ji Dept. of Institute of Artificial Intelligence Xiamen University Xiamen China Ministry of Education of China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Informatics Xiamen University Xiamen China Department of Automation Tsinghua University Beijing China

Collaborative inference can effectively ease the computational burden imposed on mobile devices by deep neural networks (DNNs), such as employing multimodal DNNs to achieve a comprehensive understanding of the environment. Although reinforcement learning (RL)-based collaborative inference adapts to dynamic environments and enhances inference performance for DNNs, especially those with chain-structured architectures, the policy selection for multimodal DNNs must account for multiple computation-intensive feature encoders. This paper proposes a hierarchical reinforcement learning (RL)-based collaborative inference scheme for multimodal DNNs that concurrently determines the partition point and identifies the appropriate DNN-based feature encoder for each modality within the mobile edge computing system. The hierarchical structure designed for policy selection has alleviated the exponential growth of the modalities. Moreover, the mobile device maintains a confidence score based on probabilities of predefined classes to assess the risk of inference policies that results in low inference accuracy and thus avoids exploring the risk policy. Simulation results demonstrate that our proposed scheme surpasses the Neurosurgeon benchmark in reducing inference latency and energy consumption, all while maintaining a high level of confidence score without significant degradation.

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Vib2Mol: from vibrational spectra to molecular structures-a versatile deep learning model

arXiv

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arXiv 2025年

作者： Lu, Xinyu Ma, Hao Li, Hui Li, Jia Zhu, Tong Liu, Guokun Ren, Bin College of Chemistry and Chemical Engineering Xiamen University Fujian Xiamen361005 China Shanghai Innovation Institute Shanghai200030 China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen University Fujian Xiamen361005 China Institute of Artificial Intelligence Xiamen University Fujian Xiamen361005 China School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China College of the Environment and Ecology Xiamen University Fujian Xiamen361005 China

There will be a paradigm shift in chemical and biological research, to be enabled by autonomous, closed-loop, real-time self-directed decision-making experimentation. Spectrum-to-structure correlation, which is to elucidate molecular structures with spectral information, is the core step in understanding the experimental results and to close the loop. However, current approaches usually divide the task into either database–dependent retrieval and database–independent generation and neglect the inherent complementarity between them. In this study, we proposed Vib2Mol, a general deep learning model designed to flexibly handle diverse spectrum-to-structure tasks according to the available prior knowledge by bridging the retrieval and generation. It achieves state-of-the-art performance, even for the most demanding Raman spectra, over previous models in predicting reaction products and sequencing peptides as well as analyzing experimental spectra and integrating multi-modal spectral data. Vib2Mol enables vibrational spectroscopy a real-time guide for autonomous scientific discovery workflows. Copyright © 2025, The Authors. All rights reserved.

关键词： Vibrational spectra

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More Clear, More Flexible, More Precise: A Comprehensive Oriented Object Detection benchmark for UAV

arXiv

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arXiv 2025年

作者： Ye, Kai Tang, Haidi Liu, Bowen Dai, Pingyang Cao, Liujuan Ji, Rongrong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China China Institute of Artificial Intelligence Xiamen University Xiamen361005 China Key Laboratory of Multi media Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Xiamen361005 China School of Informatics Xiamen University Xiamen361005 China Fujian Engineering Research Center of Trusted Artificial Intelligence Analysis and Application Institute of Artificial Intelligence Xiamen University Xiamen361005 China Peng Cheng Laboratory Shenzhen518066 China

Applications of unmanned aerial vehicle (UAV) in logistics, agricultural automation, urban management, and emergency response are highly dependent on oriented object detection (OOD) to enhance visual perception. Although existing datasets for OOD in UAV provide valuable resources, they are often designed for specific downstream tasks. Consequently, they exhibit limited generalization performance in real flight scenarios and fail to thoroughly demonstrate algorithm effectiveness in practical environments. To bridge this critical gap, we introduce CODrone, a comprehensive oriented object detection dataset for UAVs that accurately reflects real-world conditions. It also serves as a new benchmark designed to align with downstream task requirements, ensuring greater applicability and robustness in UAV-based OOD. Based on application requirements, we identify four key limitations in current UAV OOD datasets-low image resolution, limited object categories, single-view imaging, and restricted flight altitudes-and propose corresponding improvements to enhance their applicability and robustness. Furthermore, CODrone contains a broad spectrum of annotated images collected from multiple cities under various lighting conditions, enhancing the realism of the benchmark. To rigorously evaluate CODrone as a new benchmark and gain deeper insights into the novel challenges it presents, we conduct a series of experiments based on 22 classical or SOTA methods. Our evaluation not only assesses the effectiveness of CODrone in real-world scenarios but also highlights key bottlenecks and opportunities to advance OOD in UAV applications. Overall, CODrone fills the data gap in OOD from UAV perspective and provides a benchmark with enhanced generalization capability, better aligning with practical applications and future algorithm development. The dataset has been publicly released and is available at: https://***/AHideoKuzeA/CODrone-A-Com prehensive-Oriented-Object-Detection-benchmark-

关键词： Drones

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Towards Language-guided Visual Recognition via Dynamic Convolutions

arXiv

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arXiv 2021年

作者： Luo, Gen Zhou, Yiyi Sun, Xiaoshuai Wu, Yongjian Gao, Yue Ji, Rongrong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University 361005 China Institute of Artificial Intelligence Xiamen University 361005 China Youtu Lab Tencent China Software School of Tsinghua University China

In this paper, we are committed to establishing a unified and end-to-end multi-modal network via exploring language-guided visual recognition. To approach this target, we first propose a novel multimodal convolution module called Language-guided Dynamic Convolution (LaConv). Its convolution kernels are dynamically generated based on natural language information, which can help extract differentiated visual features for different multi-modal examples. Based on the LaConv module, we further build a fully language-driven convolution network, termed as LaConvNet, which can unify the visual recognition and multi-modal reasoning in one forward structure. To validate LaConv and LaConvNet, we conduct extensive experiments on seven benchmark datasets of three vision-and-language tasks, i.e., visual question answering (VQA), referring expression comprehension (REC) and segmentation (RES). The experimental results not only show the competitive or better performance of LaConvNet against existing multi-modal networks, but also witness the merits of LaConvNet as an unified structure, including compact network, low computational cost and high generalization ability. Our source code is released in SimREC project: https://***/luogen1996/LaConvNet. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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Graph-empowered Text-to-SQL generation on Electronic Medical Records

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Pattern Recognition 2026年 169卷

作者： Qian Chen Jun Peng Baiyang Song Yiyi Zhou Rongrong Ji Institute of Artificial Intelligence Xiamen University Xiamen China Peng Cheng Laboratory Shenzhen China Department of Artificial Intelligence School of Informatics Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China

Electronic Medical Records (EMR) store comprehensive patient data, offering immense potential for improving medical decision-making, education, and research. However, extracting actionable insights from EMR remains challenging due to its complex, structured nature, which requires expertise in Structured Query Language (SQL). To address this, we propose leveraging Large Language Models (LLMs) combined with graph-empowered techniques to enhance Text-to-SQL capabilities in EMR systems. LLMs provide the ability to understand natural language queries, making data access more intuitive for non-technical users, while graph representations enable the modeling of intricate relationships between medical entities. This integration not only simplifies data querying but also captures the contextual and relational nuances inherent in EMR data. In this paper, we proposed a Text-to-SQL method based on graph structure injection, which aims to bridge the gap between medical professionals and complex data systems, promoting more efficient and accessible use of EMR for clinical and research applications. To demonstrate the effectiveness of our proposed method, we conduct extensive experiments on the MIMICSQL dataset. The experimental results show that our proposed method achieves 0.942 execution accuracy, reaching a new SOTA.

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Lottery Jackpots Exist in Pre-trained Models

arXiv

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arXiv 2021年

作者： Zhang, Yuxin Lin, Mingbao Zhong, Yunshan Chao, Fei Ji, Rongrong The Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Xiamen361005 China School of Informatics Xiamen University Xiamen361005 China Youtu Laboratory Tencent Shanghai200233 China Institute of Artificial Intelligence Xiamen University Xiamen361005 China

Network pruning is an effective approach to reduce network complexity with acceptable performance compromise. Existing studies achieve the sparsity of neural networks via time-consuming weight training or complex searching on networks with expanded width, which greatly limits the applications of network pruning. In this paper, we show that high-performing and sparse sub-networks without the involvement of weight training, termed "lottery jackpots", exist in pre-trained models with unexpanded width. Our presented lottery jackpots are traceable through empirical and theoretical outcomes. For example, we obtain a lottery jackpot that has only 10% parameters and still reaches the performance of the original dense VGGNet-19 without any modifications on the pre-trained weights on CIFAR-10. Furthermore, we improve the efficiency for searching lottery jackpots from two perspectives. Firstly, we observe that the sparse masks derived from many existing pruning criteria have a high overlap with the searched mask of our lottery jackpot, among which, the magnitude-based pruning results in the most similar mask with ours. In compliance with this insight, we initialize our sparse mask using the magnitude-based pruning, resulting in at least 3× cost reduction on the lottery jackpot searching while achieving comparable or even better performance. Secondly, we conduct an in-depth analysis of the searching process for lottery jackpots. Our theoretical result suggests that the decrease in training loss during weight searching can be disturbed by the dependency between weights in modern networks. To mitigate this, we propose a novel short restriction method to restrict change of masks that may have potential negative impacts on the training loss, which leads to a faster convergence and reduced oscillation for searching lottery jackpots. Consequently, our searched lottery jackpot removes 90% weights in ResNet-50, while it easily obtains more than 70% top-1 accuracy using only 5 searching

关键词： Convolutional neural networks

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Uncovering the Over-smoothing Challenge in Image Super-Resolution: Entropy-based Quantification and Contrastive Optimization

arXiv

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arXiv 2022年

作者： Xu, Tianshuo Li, Lijiang Mi, Peng Zheng, Xiawu Chao, Fei Ji, Rongrong Tian, Yonghong Shen, Qiang Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University 361005 China Peng Cheng Laboratory Shenzhen518066 China Department of Computer Science Institute of Mathematics Physics and Computer Science Aberystwyth University SY23 3DB United Kingdom Institute of Artificial Intelligence Xiamen University Xiamen361005 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China

PSNR-oriented models are a critical class of super-resolution models with applications across various fields. However, these models tend to generate over-smoothed images, a problem that has been analyzed previously from the perspectives of models or loss functions, but without taking into account the impact of data properties. In this paper, we present a novel phenomenon that we term the center-oriented optimization (COO) problem, where a model's output converges towards the center point of similar high-resolution images, rather than towards the ground *** demonstrate that the strength of this problem is related to the uncertainty of data, which we quantify using entropy. We prove that as the entropy of high-resolution images increases, their center point will move further away from the clean image distribution, and the model will generate over-smoothed images. Implicitly optimizing the COO problem, perceptualdriven approaches such as perceptual loss, model structure optimization, or GAN-based methods can be viewed. We propose an explicit solution to the COO problem, called Detail Enhanced Contrastive Loss (DECLoss). DECLoss utilizes the clustering property of contrastive learning to directly reduce the variance of the potential high-resolution distribution and thereby decrease the entropy. We evaluate DECLoss on multiple super-resolution benchmarks and demonstrate that it improves the perceptual quality of PSNR-oriented models. Moreover, when applied to GAN-based methods, such as RaGAN, DECLoss helps to achieve state-of-the-art performance, such as 0.093 LPIPS with 24.51 PSNR on 4× downsampled Urban100, validating the effectiveness and generalization of our approach. © 2022, CC BY-NC-ND.

关键词： Benchmarking

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Reconfigurable Intelligent Surface Enhanced Wireless Localization: Phase Optimization for Malicious Interference Mitigation

引用

IEEE Transactions on Communications 2025年

作者： Zhang, Yi Xie, Yajing Wang, Lijia Liwang, Minghui Wang, Xianbin Xiamen University Ministry of Education of China Department of Information and Communication Engineering Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai China Western University Department of Electrical and Computer Engineering ON Canada

Recently, unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have merged as important enabling technologies for localization coverage extension and localization accuracy improvement under signal blockage and malicious interference conditions. However, most existing works assume known locations of jammers, which is generally impractical in real-world networks. To overcome this challenge, we propose a novel RIS-enhanced wireless localization framework against malicious interference with the support of either narrowband or orthogonal frequency division multiplexing (OFDM) pilot signals. A two-stage anti-jamming localization approach is developed to first estimate the unknown channel and signal information of the jammer and then localize the user’s position by eliminating the jamming signal. More importantly, we utilize the full potential of RIS to improve localization accuracy by optimizing the phase shift profile during the iterative process. Extensive simulation results demonstrate the commendable performance of our proposed framework, which can not only mitigate the jamming effect but also achieve better localization accuracy, offering a good reference for future heterogeneous and complex wireless networks. © 1972-2012 IEEE.

关键词： Frequency division multiplexing

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Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting

arXiv

引用

arXiv 2021年

作者： Zheng, Chuanpan Fan, Xiaoliang Pan, Shirui Jin, Haibing Peng, Zhaopeng Wu, Zonghan Wang, Cheng Yu, Philip S. Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Computer Science and Technology Department Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Xiamen361005 China School of Information and Communication Technology Griffith University Australia Centre for Artificial Intelligence FEIT University of Technology Sydney Australia Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

Recent studies have shifted their focus towards formulating traffic forecasting as a spatio-temporal graph modeling problem. Typically, they constructed a static spatial graph at each time step and then connected each node with itself between adjacent time steps to create a spatio-temporal graph. However, this approach failed to explicitly reflect the correlations between different nodes at different time steps, thus limiting the learning capability of graph neural networks. Additionally, those models overlooked the dynamic spatio-temporal correlations among nodes by using the same adjacency matrix across different time steps. To address these limitations, we propose a novel approach called Spatio-Temporal Joint Graph Convolutional Networks (STJGCN) for accurate traffic forecasting on road networks over multiple future time steps. Specifically, our method encompasses the construction of both pre-defined and adaptive spatio-temporal joint graphs (STJGs) between any two time steps, which represent comprehensive and dynamic spatio-temporal correlations. We further introduce dilated causal spatio-temporal joint graph convolution layers on the STJG to capture spatio-temporal dependencies from distinct perspectives with multiple ranges. To aggregate information from different ranges, we propose a multi-range attention mechanism. Finally, we evaluate our approach on five public traffic datasets and experimental results demonstrate that STJGCN is not only computationally efficient but also outperforms 11 state-of-the-art baseline methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

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