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Vision-Text Bidirectional Collaborative Image Captioning Algorithm

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IAENG International Journal of computer Science 2025年第2期52卷 515-523页

作者： Li, Mei-Qi Zhou, Zi-Wei School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

Image captioning is an interdisciplinary research hotspot at the intersection of computer vision and natural language processing, representing a multimodal task that integrates core technologies from both fields. This task requires the use of computer vision techniques to analyze and extract key visual features from images, followed by the application of natural language processing techniques to generate descriptive text that is syntactically and semantically aligned with human cognition. This process poses a significant challenge for computers. Existing models mostly ignore the relative positional information of visual objects and struggle to efficiently capture the complex relationships between visual and textual data. To address these challenges, we propose a vision-to-text bidirectional collaborative image captioning method. This approach extracts both visual features and positional information of objects, allowing the model to better understand the spatial relationships between objects. The CEW word embedding approach encodes textual information more profoundly, enhancing semantic expression and contextual understanding. In the decoding phase, a bidirectional cross-attention mechanism strengthens the interaction between vision and text, leading to improved accuracy in image understanding. The model is trained and tested on the MSCOCO 2014 dataset and compared with several popular models. Experimental results demonstrate that the proposed method achieves significant improvements on the CIDEr and BLEU-1 evaluation metrics with an increase of 1.5 and 1.1, respectively. In addition, we conduct ablation experiments, quantitative analysis, and qualitative analysis to comprehensively validate the effectiveness and stability of the proposed algorithm. © (2025), (International Association of Engineers). All rights reserved.

关键词： Embeddings

Revolutionizing Image Captioning: Integrating Attention Mechanisms with Adaptive Fusion Gates

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IAENG International Journal of computer Science 2024年第3期51卷 212-221页

作者： Sheng, Shou-Jun Zhou, Zi-Wei School of Computer and Software Engineering University of Science and Technology Liaoning AnShan114051 China

In order to dynamically create a sequence of textual descriptions for images, image description models often make use of the attention mechanism, which involves an automatic focus on different regions within an image. However, a prevalent issue with current attention mechanisms is their tendency to overlook essential elements within the image, prioritizing contextual aspects of the object when generating descriptive text. This constraint results in a decrease in the precision of the textual descriptions produced. To address this issue and improve the accuracy of image interpretation, a proposed model for image description utilizes an attention-based approach and includes a multi-layer decoder and a fusion gate. This model is based on an encoder-decoder architecture and utilizes Residual Network (ResNet) framework for feature extraction during the encoding phase, thereby extending the encoder-decoder structure into the decoding phase. Within this framework, an adaptive fusion gate mechanism is introduced and combined with multi-layer cascade decoders to facilitate the generation of utterances. This allows decoders from lower layers to actively contribute to the final text prediction phase, thereby incrementally improving the accuracy of the predicted text and generating more precise descriptions. MS COCO 2014 dataset has been utilized for the purpose of training and validating the effectiveness of this model in understanding images. The results clearly and unequivocally establish the model’s capacity to generate exceptional predictions. When compared with the top-performing models, it has demonstrated significant enhancements, as indicated by a 0.096 rise in BLEU_1 metric, a 0.153 improvement in ROUGE_L metric, and a remarkable 0.32 increase in CIDEr metric on MS COCO dataset. The overall improvement in performance across all evaluation criteria highlights the model’s alignment with the requirements of image understanding applications. © (2024), (International Associ

关键词： computer vision

The Improved Unet Semantic Segmentation Network for Remote Sensing Images

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IAENG International Journal of computer Science 2025年第4期52卷 1187-1195页

作者： Zhu, Hang Zhao, Ji School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

With the development of artificial intelligence, deep learning has been increasingly used to achieve automatic detection of geographic information, replacing manual interpretation and improving efficiency. However, remote sensing images themselves have the issue of slight inter-class variance and significant intra-class variance, making it challenging to extract valuable information. Additionally, the increasing resolution and size of remote sensing images in recent years have introduced more complexity in the types of information, further increasing the difficulty of extracting valuable data. This paper proposes an improved Unet semantic segmentation network (referred to as RAUnet). First, in the encoder, continuous convolutional blocks are enhanced to extract features. At the same time, the EMAM multi-scale attention module is employed for cross-channel learning, capturing information from different feature channels of the target and using the surrounding feature information to assist in distinguishing target information. To capture multi-directional long-range dependencies, the Lo2 module is used for long-range modeling, which captures not only local contextual information but also long-range dependencies. In the decoder, a Dysample upsampling module is used to restore feature details, and in the skip connection layer, features are added for feature fusion. Experimental results show that compared to mainstream models, the proposed method achieves superior segmentation results on the Potsdam and Vihingen datasets. © (2025), (International Association of Engineers). All rights reserved.

关键词： Semantic Segmentation

YOLOv7-DSE: An Efficient Safety Equipment Detection Network

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IAENG International Journal of computer Science 2024年第6期51卷 572-581页

作者： Ren, Jiaxin Cui, Wenhua Tao, Ye Shi, Tianwei School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

Safety equipment detection is an important application of object detection, receiving widespread attention in fields such as smart construction sites and video surveillance. Significant progress has been made in object detection due to the rapid development of deep learning. Multi-scale targets and complex scenes increase the likelihood of false positives and missed detections, which can affect the accuracy of the detection. To address this issue, this study proposes YOLOv7-DSE. It is a small complex target scene detection network based on the improved YOLOv7. Also, we have created a private dataset of safety equipment. First, we enhanced the ELAN and MP backbone networks. Backbone is replaced by ordinary convolution by the depthwise separable convolution. We enabled the backbone network to extract deeper image features without increasing the amount of parameters and computation. Simultaneously, the model incorporates the EIOU loss function to improve its convergence speed and positioning effect. Secondly, we propose a new ELAN-SPD structure in the head network. Based on the ELAN structure, a space-to-depth convolutional layer is added to fully downsample the feature map, preserving all learnable features. Our network model can better detect objects with significant size differences faced with complex scenes. YOLOv7-DSE achieved the mAP of 82.38%, surpassing the original YOLOv7 with 2.64%. The YOLOv7-DSE model has a minor size compared to the baseline model. Our improvement has reduced the model parameters by 22.4%. © (2024), (International Association of Engineers). All rights reserved.

关键词： Object detection

Application of Face Detection and Recognition Algorithm Based on Deep Learning in Automatic Interception of Target Person Video Clips

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IAENG International Journal of computer Science 2024年第10期51卷 1546-1559页

作者： Wang, Zhibiao Tao, Ye Liu, Xu Yu, Jiayi Cui, Wenhua School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

Current automatic segment extraction techniques for identifying target characters in videos have several limitations, including low accuracy, slow processing speeds, and poor adaptability to diverse scenes. This paper introduces an optimized algorithm to address these issues that enhance the RetinaFace and FaceNet models. We selected RetinaFace for face detection, employing MobileNetV1-0.25 as the backbone network and simplifying its Feature Pyramid Network (FPN) structure to boost detection speeds. Analysis of 460 images with a 720P resolution demonstrated an average speed improvement of 20.6%. For face recognition, we utilized FaceNet with MobileNetV3 as the backbone, augmenting its feature extraction capability by integrating four Receptive Field Block (RFB) structures and replacing the Squeeze-and-Excitation (SE) module with the Convolutional Block Attention Module (CBAM). Experimental results indicate that our enhancements elevate the maximum accuracy to 97%, outperforming the original model. Additionally, we integrated these refined algorithms and conducted disintegration experiments on segment extraction in 10 videos, evaluating various metrics. The findings show improvements in both precision and recall. We also compared our algorithm against the Dlib model;our system achieved an overall interception accuracy of 79.94%, surpassing Dlib’s 75.55%. This confirms the enhanced performance and feasibility of our proposed algorithm. © (2024), (International Association of Engineers). All rights reserved.

关键词： Face recognition

Deep learning for code generation: a survey

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Science China(Information Sciences) 2024年第9期67卷 5-40页

作者： Huangzhao ZHANG Kechi ZHANG Zhuo LI Jia LI Jia LI Yongmin LI Yunfei ZHAO Yuqi ZHU Fang LIU Ge LI Zhi JIN Key Lab of High Confidence Software Technologies (Peking University) Ministry of Education School of Computer Science Peking University School of Computer Science and Engineering Beihang University

In the past decade, thanks to the powerfulness of deep-learning techniques, we have witnessed a whole new era of automated code generation. To sort out developments, we have conducted a comprehensive review of solutions to deep learning-based code generation. In this survey, we generally formalize the pipeline and procedure of code generation and categorize existing solutions according to taxonomy from perspectives of architecture, model-agnostic enhancing strategy, metrics, and tasks. In addition, we outline the challenges faced by current dominant large models and list several plausible directions for future research. We hope that this survey may provide handy guidance to understanding, utilizing, and developing deep learning-based code-generation techniques for researchers and practitioners.

关键词： code generation automated software engineering deep learning large model artificial intelligence

Piercing Diametral Disks Induced by Edges of Maximum Spanning Trees

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Journal of Graph Algorithms and Applications 2024年第3期28卷 3-10页

作者： Karim Abu-Affash, A. Carmi, Paz Maman, Meytal Department of Software Engineering Shamoon College of Engineering Israel Department of Computer Science Ben-Gurion University Israel

Let P be a set of points in the plane and let T be a maximum-weight spanning tree of P. For an edge (p, q), let Dpq be the diametral disk induced by (p, q), i.e., the disk having the segment pq as its diameter. Let DT be the set of the diametral disks induced by the edges of T. In this paper, we show that one point is sufficient to pierce all the disks in DT. Actually, we show that the center of the smallest enclosing circle of P is contained in all the disks of DT, and thus the piercing point can be computed in linear time. © 2024, Brown University. All rights reserved.

关键词： Piercing

Convolutional Neural Network Based Predictive Framework for Intensive Care Unit Readmission 5

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Convolutional Neural Network Based Predictive Framework for ...

5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Rasheed, Nadia Akram, Muhammad Usman Zeb, Jahan Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350394764

The cost of intensive care is huge, which necessitates careful thought regarding transfer of patients to lower-level ward care. Discharging a patient too early carries the risk of inadequate monitoring and care, often leading to readmission to the ICU. This risk can be mitigated by state-of-the-art machine learning methods. Limited research was carried out on readmission prediction tasks and the methods used were unable to attain good results. This study focuses on developing an ICURP (Intensive Care Unit Readmission Prediction) framework that can be used for the effective prediction of unplanned ICU readmission within 30 days. Particularly, the framework deals with the missing values (via the last observation carried forward technique) and data imbalance (via the Over-sampling Technique). Our approach incorporates temporal features from chart events data with low-dimensional embeddings of medical concepts such as diseases coded using the ICD-9 code. Convolutional neural network (CNN) is used to fit three alternative CNN models using the last 24-hour, 48-hour and 72-hour ICU stay data. Models are trained and validated using the Medical Information Mart for Intensive Care (MIMIC-III) dataset. To evaluate the effectiveness of our proposed methods, we conducted testing on the unseen data of the MIMIC-III dataset. The model trained using the last 48-hour ICU data has outperformed other models and reached an area under the curve of receiver operating characteristic (AUC-ROC) of 0.88. The results suggested that our ICURP framework has the potential to surpass the existing standard of ICU discharge by accurately predicting readmissions up to 30 days of discharge time using a reduced features set. © 2024 IEEE.

关键词： Forecasting

Formal Verification of Data Modifications in Cloud Block Storage Based on Separation Logic

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Chinese Journal of Electronics 2024年第1期33卷 112-127页

作者： Bowen ZHANG Zhao JIN Hanpin WANG Yongzhi CAO Key Laboratory of High Confidence Software Technologies (MOE) School of Computer Science Peking University School of Computer and Artificial Intelligence Zhengzhou University School of Computer Science and Cyber Engineering Guangzhou University

Cloud storage is now widely used, but its reliability has always been a major concern. Cloud block storage(CBS) is a famous type of cloud storage. It has the closest architecture to the underlying storage and can provide interfaces for other types. Data modifications in CBS have potential risks such as null reference or data *** verification of these operations can improve the reliability of CBS to some extent. Although separation logic is a mainstream approach to verifying program correctness, the complex architecture of CBS creates some challenges for verifications. This paper develops a proof system based on separation logic for verifying the CBS data modifications. The proof system can represent the CBS architecture, describe the properties of the CBS system state, and specify the behavior of CBS data modifications. Using the interactive verification approach from Coq, the proof system is implemented as a verification tool. With this tool, the paper builds machine-checked proofs for the functional correctness of CBS data modifications. This work can thus analyze the reliability of cloud storage from a formal perspective.

关键词： Formal verification Separation logic Cloud block storage Verification tool Coq