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Micro-KTNet: Microstructure knowledge transfer learning for fiber masterbatch agglomeration recognition

JOURNAL OF INDUSTRIAL TEXTILES 2025年 55卷

作者： Zhang, Xianqi Wei, Bing Hao, Kuangrong Gao, Lei Xie, Ruimin Wang, Huaping Donghua Univ Coll Informat Sci & Technol Minist Educ Engn Res Ctr Digitized Text & Apparel Technol Shanghai Peoples R China Australias Commonwealth Sci & Ind Res Org CSIRO Waite Campus Urrbrae SA Australia Donghua Univ Coll Mat Sci & Engn State Key Lab Modificat Chem Fibers & Polymer Mat Shanghai Peoples R China

Fiber masterbatch production suffers from inherent agglomeration effect of high-concentration color masterbatches, negatively impacting color uniformity, particle dispersion, and thermal stability in fiber masterbatch, and ultimately the quality of fiber products. However, accurately recognizing and classifying the agglomeration in scanning electron microscopy (SEM) images remain a challenge due to the complexity of agglomeration status, difficulty in distinguishing micro-size agglomeration, and limited data availability. To address this challenge, this paper proposes a novel microstructure recognition architecture, named Micro-KTNet, which is designed for segmenting SEM images of fiber masterbatch aggregation. First, Micro-KTNet leverages transferable microstructural features to initialize the proposed network, mitigating the impact of limited data. Then, to handle multi-scale features in the microstructure, an encoding-decoding structure is constructed and skip connections are used to transfer the output features of the encoding layer. Finally, the decoder incorporates a novel attention module to effectively process both global and local features. To evaluate Micro-KTNet, a unique fiber masterbatch SEM database is built as the benchmark for micro-size agglomeration recognition. Experimental results indicate that Micro-KTNet surpasses existing state-of-the-art methods and improves the recognition precision from 75. 37% to 87. 04%.

关键词： Fiber masterbatch agglomeration effects encoder-decoder framework transfer learning microstructure segmentation attention module

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A chest imaging diagnosis report generation method based on dual-channel transmodal memory network

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 100卷

作者： Dong, Zilong Lian, Jing Zhang, Xiangzi Zhang, Bingtao Liu, Jizhao Zhang, Jiajun Zhang, Huaikun Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Gansu Peoples R China Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Gansu Peoples R China Northwest Normal Univ Sch Psychol Lanzhou 730070 Gansu Peoples R China

Medical imaging is critical for clinical diagnosis. In the past few years, medical imaging data has been steadily growing at an annual rate of 30%, while the annual growth rate in the number of radiologists is only 4. 1%. The traditional manual methods of chest imaging reports result in high information load and workload with low relevance of images and texts. Here we propose a chest CT imaging diagnosis report generation network to effectively integrate image and text feature information, improve the relevance of the cross-modal networks and automatically generate imaging diagnosis reports. The network includes three major modules: a novel ResNetII for information extraction, an image-text dual-channel transmodal memory network (DCTMN), and a dual-channel decoder module combined with LSTM gate. We validate the effectiveness of the proposed network on the IU X-ray and the MIMIC-CXR datasets. Our analysis indicates that the proposed method can strengthen the relationship between chest imaging and text information, ultimately achieving the automatic generation of chest imaging diagnosis reports.

关键词： Automatic report generation Cross-modal network encoder-decoder framework LSTM gate IU X-ray and MIMIC-CXR

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DiffMamba: semantic diffusion guided feature modeling network for semantic segmentation of remote sensing images

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GISCIENCE & REMOTE SENSING 2025年第1期62卷

作者： Wang, Zhen Xu, Nan You, Zhuhong Zhang, Shanwen Xijing Univ Sch Elect Informat Xian Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Shaanxi Peoples R China Hohai Univ Sch Earth Sci & Engn Nanjing Jiangsu Peoples R China

With the rapid development of remote sensing technology, the application scope of high-resolution remote sensing images (HR-RSIs) has been continuously expanding. The emergence of convolutional neural networks and Transformer models has significantly enhanced the accuracy of semantic segmentation. However, these methods primarily focus on local feature extraction and long-range dependency modeling of global information, neglecting the spatial correlation of local features, which leads to poor segmentation of small-scale regions. To address this issue, based on Diffusion Model and State Space Model (SSM), we propose a semantic diffusion guided feature modeling network (DiffMamba) for HR-RSI semantic segmentation. DiffMamba uses a hybrid CNNs-Transformer as the encoder structure, and is equipped with the efficient phase sensing module (EPSM), the multi-view transformer module (MVTrans), the semantic diffusion alignment module (SDAM), and the coordinate state space model (CAMamba). EPSM focuses on enhancing local feature representation in the channel dimension, using the phase information of object region features to improve local information interaction and filter out clutter noise interference. MVTrans can observe the spatial location information of the object region from various perspectives to obtain refined global context details. SDAM utilizes the diffusion propagation process to fuse local and global information, alleviating the feature redundancy caused by semantic information differences. CAMamba employs state space transformation to construct the correlation of enhanced local features, and guides the model to achieve feature decoding to obtain refined semantic segmentation results. Extensive experiments on the widely used ISPRS 2-D Semantic Labeling dataset and the 15-Class Gaofen Image dataset confirm the superior efficiency of DiffMamba over several state-of-the-art methods.

关键词： Diffusion model state space model (SSM) encoder-decoder framework remote sensing images semantic segmentation

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Automatic medical report generation based on deep learning: A state of the art survey

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COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2025年 120卷 102486-102486页

作者： Liu, Xinyao Xin, Junchang Shen, Qi Huang, Zhihong Wang, Zhiqiong Northeastern Univ Coll Med & Biol Informat Engn Shenyang 110819 Peoples R China Northeastern Univ Coll Comp Sci & Engn Shenyang 110819 Peoples R China Univ Dundee Sch Sci & Engn Dundee DD1 4HN Scotland

With the increasing popularity of medical imaging and its expanding applications, posing significant challenges for radiologists. Radiologists need to spend substantial time and effort to review images and manually writing reports every day. To address these challenges and speed up the process of patient care, researchers have employed deep learning methods to automatically generate medical reports. In recent years, researchers have been increasingly focusing on this task and a large amount of related work has emerged. Although there have been some review articles summarizing the state of the art in this field, their discussions remain relatively limited. Therefore, this paper provides a comprehensive review of the latest advancements in automatic medical report generation, focusing on four key aspects: (1) describing the problem of automatic medical report generation, (2) introducing datasets of different modalities, (3) thoroughly analyzing existing evaluation metrics, (4) classifying existing studies into five categories: retrieval-based, domain knowledge-based, attention- based, reinforcement learning-based, large language models-based, and merged model. In addition, we point out the problems in this field and discuss the directions of future challenges. We hope that this review provides a thorough understanding of automatic medical report generation and encourages the continued development in this area.

关键词： Automatic medical report generation Deep learning encoder-decoder framework Medical image Natural language process

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A position-enhanced attention framework for video deblurring

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DIGITAL SIGNAL PROCESSING 2025年 161卷

作者： Lia, Zhi Zhou, Yangfan Tao, Fazhan Li, Jiadong Henan Univ Sci & Technol Coll Informat Engn Luoyang Peoples R China Chinese Acad Sci Suzhou Inst Nanotech & Nanob Suzhou Peoples R China Guangdong Inst Semicond Micronano Mfg Technol Foshan Peoples R China Henan Key Lab Sensing Technol & Syst Internet Thin Luoyang Peoples R China

Video deblurring holds significant importance for the field of motion photography such as hand-held photography, UAV aerial photography, and vehicle video. However, the non-uniform dynamic blur brings great challenges to the existing methods to realize video deblurring in high dynamic scenes. To address this challenge, we innovatively proposes a video deblurring method that precisely captures and compensates for complex spatial- temporal blur variations caused by intense motion and dynamic scene changes. The proposed method presents a Position-Enhanced Channel Attention Block (PEAB), which integrates channel information and significant positional information. Moreover, this block is embedded within an encoder-decoder network, enabling robust multi-scale feature modeling and enhancing deblurring performance across various motion-induced blurs. Additionally, the PEAB within the spatial-temporal shift module improves multi-frame information aggregation, reduces computational overhead, and enhances overall restoration quality. To further refine the process, we introduce an image optimization module based on the transformer architecture, which simplifies feature processing while maintaining high deblurring accuracy. Experimental results show that, compared to existing methods, the proposed approach achieves excellent performance while maintaining a remarkable balance between precision and efficiency on the GOPRO and DVD datasets. It also demonstrates strong generalization ability on the RBVD dataset.

关键词： Video restoration Non-uniform dynamic blurring Attention mechanism encoder-decoder framework Transformer

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An efficient encoder-decoder network for the capacitated vehicle routing problem

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 278卷

作者： Luo, Jia Li, Chaofeng Ningbo Univ Technol Sch Econ & Management Ningbo 315211 Zhejiang Peoples R China Shanghai Maritime Univ Inst Logist Sci & Engn Shanghai 200135 Peoples R China

The capacitated vehicle routing problem (CVRP) is of great importance to intelligent transportation systems. In recent, deep reinforcement learning (DRL) approaches have shown great potential in solving the CVRP efficiently. Specifically, encoder-decoder frameworks are trained via reinforcement learning with different schemes to construct solutions incrementally. As the total customer demands and remaining vehicle capacity are dynamic with time steps, it is still a challenge for this kind of methods to obtain optimal solutions. In this work, we develop an efficient encoder-decoder framework, termed the residual graph convolutional encoder and multiple attention-based decoders (RGCMA), which is trained by a reinforcement learning method with an elite baseline. The encoder produces powerful node representations while being dedicated to aggregating neighborhood features by a fitted dense residual edge and node features updating block. Compared to the popular single decoder strategy, our multiple decoders mechanism incrementally constructs a variety of solutions for any CVRP instance, which diversifies solution space, and eventually improves the solution quality. Extensive experiments demonstrate that RGCMA performs competitively with existing methods on variety CVRP datasets. RGCMA narrows the gap to LKH solver on four benchmark tasks, and its run time is less than 1 s. It also exhibits good generalization ability on large-scale tasks, well-known CVRPLIB dataset and real-world Jingdong Logistics distribution task.

关键词： Capacitated vehicle routing problem Deep reinforcement learning encoder-decoder framework Graph convolutional neural network Attention-based mechanism

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Visuals to Text:A Comprehensive Review on Automatic Image Captioning

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1339-1365页

作者： Yue Ming Nannan Hu Chunxiao Fan Fan Feng Jiangwan Zhou Hui Yu Beijing University of Posts and Telecommunications Beijing 100876China School of Creative Technologies University of PortsmouthPortsmouth PO12DJUK

Image captioning refers to automatic generation of descriptive texts according to the visual content of *** is a technique integrating multiple disciplines including the computer vision(CV),natural language processing(NLP)and artificial *** recent years,substantial research efforts have been devoted to generate image caption with impressive *** summarize the recent advances in image captioning,we present a comprehensive review on image captioning,covering both traditional methods and recent deep learning-based ***,we first briefly review the early traditional works based on the retrieval and *** deep learning-based image captioning researches are focused,which is categorized into the encoder-decoder framework,attention mechanism and training strategies on the basis of model structures and training manners for a detailed *** that,we summarize the publicly available datasets,evaluation metrics and those proposed for specific requirements,and then compare the state of the art methods on the MS COCO ***,we provide some discussions on open challenges and future research directions.

关键词： Artificial intelligence attention mechanism encoder-decoder framework image captioning multi-modal understanding training strategies

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Keyphrase Generation Based on Self-Attention Mechanism

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Computers, Materials & Continua 2019年第8期61卷 569-581页

作者： Kehua Yang Yaodong Wang Wei Zhang Jiqing Yao Yuquan Le College of Computer Science and Electronic Engineering and Key Laboratory for Embedded and Network Computing of Hunan Province Hunan UniversityChangsha410082China Oath Verizon Company ManhattanNew York10007USA

Keyphrase greatly provides summarized and valuable *** information can help us not only understand text semantics,but also organize and retrieve text content *** task of automatically generating it has received considerable attention in recent *** the previous studies,we can see many workable solutions for obtaining *** method is to divide the content to be summarized into multiple blocks of text,then we rank and select the most important *** disadvantage of this method is that it cannot identify keyphrase that does not include in the text,let alone get the real semantic meaning hidden in the *** approach uses recurrent neural networks to generate keyphrases from the semantic aspects of the text,but the inherently sequential nature precludes parallelization within training examples,and distances have limitations on context *** works have demonstrated the benefits of the self-attention mechanism,which can learn global text dependency features and can be *** by the above observation,we propose a keyphrase generation model,which is based entirely on the self-attention *** is an encoder-decoder model that can make up the above disadvantage *** addition,we also consider the semantic similarity between keyphrases,and add semantic similarity processing module into the *** proposed model,which is demonstrated by empirical analysis on five datasets,can achieve competitive performance compared to baseline methods.

关键词： Keyphrase generation self-attention mechanism encoder-decoder framework

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Jointly Modeling Spatio-Temporal Dependencies and Daily Flow Correlations for Crowd Flow Prediction

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2021年第4期15卷 1–20页

作者： Zang, Tianzi Zhu, Yanmin Xu, Yanan Yu, Jiadi Shanghai Jiao Tong Univ Dept Comp Sci & Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China

Crowd flow prediction is a vital problem for an intelligent transportation system construction in a smart city. It plays a crucial role in traffic management and behavioral analysis, thus it has raised great attention from many researchers. However, predicting crowd flows timely and accurately is a challenging task that is affected by many complex factors such as the dependencies of adjacent regions or recent crowd flows. Existing models mainly focus on capturing such dependencies in spatial or temporal domains and fail to model relations between crowd flows of distant regions. We notice that each region has a relatively fixed daily flow and some regions (even very far away from each other) may share similar flow patterns which show strong correlations among them. In this article, we propose a novel model named Double-encoder which follows a general encoder-decoder framework for multi-step citywide crowd flow prediction. The model consists of two encoder modules named ST-encoder and FR-encoder to model spatial-temporal dependencies and daily flow correlations, respectively. We conduct extensive experiments on two real-world datasets to evaluate the performance of the proposed model and show that our model consistently outperforms state-of-the-art methods.

关键词： Multi-step crowd flow prediction encoder-decoder framework CNN ConvLSTM latent representation

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High-Order Interaction Learning for Image Captioning

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第7期32卷 4417-4430页

作者： Wang, Yanhui Xu, Ning Liu, An-An Li, Wenhui Zhang, Yongdong Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230052 Peoples R China

Image captioning aims at understanding various semantic concepts (e.g., objects and relationships) from an image and integrating them in a sentence-level description. Hence, it is necessary to learn the interaction among these concepts. If we define the context of the interaction to be involved in the subject-predicate-object triplet, most current methods only focus on the single triplet for the first-order interaction to generate sentences. Intuitively, we humans are able to perceive the high-order interaction among concepts from two or more triplets to describe an image. For example, when we see the triplets man-cutting-sandwich and man-with-knife, it is natural to integrate and predict the sentence man cutting sandwich with knife. This depends on the high-order interaction between cutting and knife in different triplets. Therefore, exploiting high-order interaction is expected to benefit image captioning and focus on reasoning. In this paper, we introduce the novel high-order interaction learning method over detected objects and relationships for image captioning under the umbrella of the encoder-decoder framework. We first extract a set of object and relationship features in an image. During the encoding stage, the interactive refining network is proposed to learn high-order representations by modeling intra- and inter-object feature interaction in the self-attention fashion. During the decoding stage, the interactive fusion network is proposed to integrate object and relationship information by strengthening their high-order interaction based on language context for sentence generation. In this way, we learn the object-relationship dependencies in different stages, which can provide abundant cues for both visual understanding and caption generation. Extensive experiments show that the proposed method can achieve competitive performances against the state-of-the-art methods on MSCOCO dataset. Additional ablation studies further validate its effectiveness.

关键词： Visualization Semantics Feature extraction Decoding Task analysis Ions Encoding Image captioning high-order interaction encoder-decoder framework

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