检索结果-内蒙古大学图书馆

DNACoder: a CNN-LSTM attention-based network for genomic sequence data compression

Neural Computing and Applications 2024年第29期36卷 18363-18376页

作者： Sheena, K.S. Nair, Madhu S. Artificial Intelligence & amp Computer Vision Lab Department of Computer Science Cochin University of Science and Technology Kerala Kochi682022 India

Genomic sequencing has become increasingly prevalent, generating massive amounts of data and facing a significant challenge in long-term storage and transmission. A solution that reduces the storage and transfer requirements without compromising data integrity is needed. The effectiveness of neural networks has already been endorsed in tasks like image and speech compression. Adapting them to recognize the intricate patterns in genomic sequences could help to find more redundancies and reduce storage requirements. The proposed method, called DNACoder, leverages deep learning techniques to achieve significant compression ratios while preserving the essential information in genomic data and offers a high-performance compression for genomic sequences in any data format. The results of the experiments clearly demonstrate the effectiveness of the method and its potential applications in genomic data storage. Our proposed method improves compression by 21.1% on bits per base compared to existing compressors on the benchmarked dataset. By using a deep learning prediction model that is structured as a convolutional layer followed by an attention-based long short-term memory network, we propose a novel lossless and reference-free compression approach (DNACoder), which can also be utilized as a reference-based compressor. The experimental outcome on the tested data illustrates that the advocated compression algorithm’s CNN-LSTM model makes generalizations effectively for genomic sequence data and outperforms the state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

VISTA: Visual Integrated System for Tailored Automation in Math Problem Generation Using LLM 1

VISTA: Visual Integrated System for Tailored Automation in M...

引用

1st Workshop on Large Foundation Models for Educational Assessment

作者： Lee, Jeongwoo Park, Kwangsuk Park, Jihyeon Department of Applied Artificial Intelligence Sungkyunkwan University Deep Computer Vision LAB MODULABS Korea Republic of Deep Computer Vision LAB MODULABS Korea Republic of

Generating accurate and consistent visual aids is a critical challenge in mathematics education, where visual representations like geometric shapes and functions play a pivotal role in enhancing student comprehension. This paper introduces a novel multi-agent framework that leverages Large Language Models (LLMs) to automate the creation of complex mathematical visualizations alongside coherent problem text. Our approach not only simplifies the generation of precise visual aids but also aligns these aids with the problem’s core mathematical concepts, improving both problem creation and assessment. By integrating multiple agents, each responsible for distinct tasks—such as numeric calculation, geometry validation, and visualization—our system delivers mathematically accurate and contextually relevant problems with visual aids. Evaluation across Geometry and Function problem types shows that our method significantly outperforms basic LLMs in terms of text coherence, consistency, relevance and similarity, while maintaining the essential geometrical and functional integrity of the original problems. Although some challenges remain in ensuring consistent visual outputs, our framework demonstrates the immense potential of LLMs in transforming the way educators generate and utilize visual aids in math education. © 2024 J. Lee, K. Park & J. Park.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

VSMCNN-dynamic summarization of videos using salient features from multi-CNN model

引用

Journal of Ambient intelligence and Humanized Computing 2023年第10期14卷 14071-14080页

作者： Nair, Madhu S. Mohan, Jesna Artificial Intelligence & Computer Vision Lab Department of Computer Science Cochin University of Science and Technology Kerala Kochi682022 India Department of Computer Science and Engineering Mar Baselios College of Engineering and Technology Nalanchira Kerala Thiruvananthapuram695015 India

A dynamic video summarization system detects key parts of the input video to generate its compact representation. The summaries can be used for efficient management of video data. This paper proposes an approach, Video summarization based on multi-CNN model (VSMCNN), that exploits major aspects of human cognition to generate meaningful summaries from videos. As the method focuses on dynamic summarization, the input video is divided into a set of shots. A multi-CNN model, which is a combination of different pre-trained models of CNN, is used for feature extraction from shots. The salient features are extracted from high dimensional feature vector using an unsupervised feature reduction technique applied in multiple subspaces to rank features in the vector. The distance measure between feature vectors is then thresholded to detect prime parts of the tested video. Experiments are performed on SumMe dataset and the results prove that our approach is successful in detecting portions of the tested video that has an essential message. The analysis shows that the method outperforms the state-of-the-art methods in the literature. Further evaluation on comparison with human-generated summaries in the ground truth proves the effectiveness of the proposed method. The paper also presents a detailed analysis to show which combination of pre-trained models of CNN is best suitable for generating dynamic summaries. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Video recording

来源：评论

学校读者我要写书评

暂无评论

How Good is Google Bard's Visual Understanding? An Empirical Study on Open Challenges

引用

Machine intelligence Research 2023年第5期20卷 605-613页

作者： Haotong Qin Ge-Peng Ji Salman Khan Deng-Ping Fan Fahad Shahbaz Khan Luc Van Gool Computer Vision Lab(CVL) ETH ZurichZurich 8001Switzerland College of Engineering Computing&CyberneticsAustralian National UniversityCanberra 8105Australia Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi 999041UAE

Google's Bard has emerged as a formidable competitor to OpenAI's ChatGPT in the field of conversational ***,Bard has recently been updated to handle visual inputs alongside text prompts during *** Bard's impressive track record in handling textual inputs,we explore its capabilities in understanding and interpreting visual data(images)conditioned by text *** exploration holds the potential to unveil new insights and challenges for Bard and other forthcoming multi-modal Generative models,especially in addressing complex computer vision problems that demand accurate visual and language ***,in this study,we focus on 15 diverse task scenarios encompassing regular,camouflaged,medical,under-water and remote sensing data to comprehensively evaluate Bard's *** primary finding indicates that Bard still struggles in these vision scenarios,highlighting the significant gap in vision-based understanding that needs to be bridged in future *** expect that this empirical study will prove valuable in advancing future models,leading to enhanced capabilities in comprehending and interpreting finegrained visual *** project is released on https://***/htqin/GoogleBard-VisUnderstand.

关键词： Google Bard multi-modal understanding visual comprehension large language models conversational AI chatbot.

来源：评论

学校读者我要写书评

暂无评论

Prostate Segmentation in Magnetic Resonance Images Using artificial Neural Networks: A Systematic Literature Review

Prostate Segmentation in Magnetic Resonance Images Using Ar...

引用

International Conference on computer Science, Electronics and Industrial Engineering, CSEI 2023

作者： Vacacela, Silvia-P. Benalcázar, Marco-E. Artificial Intelligence and Computer Vision Research Lab Departamento de Informática y Ciencias de la Computación Escuela Politécnica Nacional Quito Ecuador

ISBN: (纸本)9783031692277

Currently, artificial Neural Networks have become a very popular machine learning approach for the automatic segmentation of medical images. To understand how the field of automatic prostate segmentation using artificial neural networks over MRIs has evolved lately, we present a Systematic Literature Review (SLR) on this topic. This SLR is based on the Kitchenham methodology applied to 372 primary studies published from 2015 to 2020. Inclusion and exclusion criteria were applied to reduce the number of papers to 65. The contributions of this SLR consist of determining the segmentation structure, type of segmentation, standard architectures, and the metrics used. We also identified findings that could open new directions of work for future research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Urology

来源：评论

学校读者我要写书评

暂无评论

Face to Cartoon Incremental Super-Resolution Using Knowledge Distillation 27th

Face to Cartoon Incremental Super-Resolution Using Knowledg...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Devkatte, Trinetra Dubey, Shiv Ram Singh, Satish Kumar Hadid, Abdenour Computer Vision and Biometrics Lab Indian Institute of Information Technology Allahabad Allahabad India Sorbonne Centre for Artificial Intelligence Sorbonne University Abu Dhabi Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031781827

Facial super-resolution/hallucination is an important area of research that seeks to enhance low-resolution facial images for a variety of applications. While Generative Adversarial Networks (GANs) have shown promise in this area, their ability to adapt to new, unseen data remains a challenge. This paper addresses this problem by proposing an incremental super-resolution using GANs with knowledge distillation (ISR-KD) for face to cartoon. Previous research in this area has not investigated incremental learning, which is critical for real-world applications where new data is continually being generated. The proposed ISR-KD aims to develop a novel unified framework for facial super-resolution that can handle different settings, including different types of faces such as cartoon face and various levels of detail. To achieve this, a GAN-based super-resolution network was pre-trained on the CelebA dataset and then incrementally trained on the iCartoonFace dataset, using knowledge distillation to retain performance on the CelebA test set while improving the performance on iCartoonFace test set. Our experiments demonstrate the effectiveness of knowledge distillation in incrementally adding capability to the model for cartoon face super-resolution while retaining the learned knowledge for facial hallucination tasks in GANs. The code is released at https://***/TRINETRA-DEVKATTE/ISR-KD-Network. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Cross-View Meets Diffusion: Aerial Image Synthesis with Geometry and Text Guidance

Cross-View Meets Diffusion: Aerial Image Synthesis with Geom...

引用

2025 IEEE/CVF Winter Conference on Applications of computer vision, WACV 2025

作者： Arrabi, Ahmad Zhang, Xiaohan Sultani, Waqas Chen, Chen Wshah, Safwan University of Vermont Vermont Artificial Intelligence Lab Department of Computer Science United States Information Technology University Intelligent Machines Lab Pakistan University of Central Florida Center for Research in Computer Vision United States

ISBN: (纸本)9798331510831

Aerial imagery analysis is critical for many research fields. However, obtaining frequent high-quality aerial images is not always accessible due to its high effort and cost requirements. One solution is to use the Ground-to-Aerial (G2A) technique to synthesize aerial images from easily collectible ground images. However, G2A is rarely studied, because of its challenges, including but not limited to, the drastic view changes, occlusion, and range of visibil-ity. In this paper, we present a novel Geometric Preserving Ground-to-Aerial (G2A) image synthesis (GPG2A) model that can generate realistic aerial images from ground images. GPG2A consists of two stages. The first stage predicts the Bird's Eye View (BEV) segmentation (referred to as the BEV layout map) from the ground image. The second stage synthesizes the aerial image from the predicted BEV layout map and text descriptions of the ground image. To train our model, we present a new multimodal cross-view dataset, namely VIGORv2, built upon VIGOR [64] with newly collected aerial images, maps, and text descriptions. Our extensive experiments illustrate that GPG2A synthesizes better geometry-preserved aerial images than existing models. We also present two applications, data augmentation for cross-view geo-localization and sketch-based region search, to further verify the effectiveness of our GPG2A. The code and dataset are available at https://***/AhmadArrabi/GPG2A. © 2025 IEEE.

关键词： Photointerpretation

来源：评论

学校读者我要写书评

暂无评论

Full-duplex strategy for video object segmentation

引用

Computational Visual Media 2023年第1期9卷 155-175页

作者： Ge-Peng Ji Deng-Ping Fan Keren Fu Zhe Wu Jianbing Shen Ling Shao School of Computer Science Wuhan UniversityWuhanChina Computer Vision Lab ETH ZurichETF C113.2Sternwartstrasse 78092 ZurichSwitzerland College of Computer Science Sichuan UniversityChengduChina Peng Cheng Laboratory ShenzhenChina School of Computer Science Beijing Institute of TechnologyBeijingChina Inception Institute of Artificial Intelligence Abu DhabiUnited Arab Emirates

Previous video object segmentation approachesmainly focus on simplex solutions linking appearance and motion,limiting effective feature collaboration between these two *** this work,we study a novel and efficient full-duplex strategy network(FSNet)to address this issue,by considering a better mutual restraint scheme linking motion and appearance allowing exploitation of cross-modal features from the fusion and decoding ***,we introduce a relational cross-attention module(RCAM)to achieve bidirectional message propagation across embedding *** improve the model’s robustness and update inconsistent features from the spatiotemporal embeddings,we adopt a bidirectional purification module after the *** experiments on five popular benchmarks show that our FSNet is robust to various challenging scenarios(e.g.,motion blur and occlusion),and compares well to leading methods both for video object segmentation and video salient object *** project is publicly available at https://***/GewelsJI/FSNet.

关键词： video object segmentation(VOS) video salient object detection(V-SOD) visual attention

来源：评论

学校读者我要写书评

暂无评论

Effects on Hand Gesture Recognition Accuracy after Severe Cranial Trauma 7

Effects on Hand Gesture Recognition Accuracy after Severe Cr...

引用

7th IEEE Ecuador Technical Chapters Meeting, ECTM 2023

作者： Zea, Jonathan A. Santillán, Luis Gabriel Macias López, Lorena Isabel Barona Caraguay, Ángel Leonardo Valdivieso Benalcázar, Marco E. Artificial Intelligence and Computer Vision Research Lab Departamento de Informática y Ciencias Computación Escuela Politécnica Nacional Quito Ecuador

ISBN: (纸本)9798350338232

This study examines the impact of severe cranial trauma on the accuracy of Hand Gesture Recognition (HGR) models based on electromyography (EMG) signals. The study focuses on a single subject, who suffered left-sided hemiplegia, and exhibits limited mobility and spasticity in the left hand. EMG samples corresponding to six hand gestures from his both forearms were collected using the Myo Armband. The 2023HGR5-CNN-LSTM HGR model was used to evaluate the recognition accuracy of these samples. Results showed significantly lower HGR performance in the left side (10.67% recognition accuracy) compared to the right side (56% recognition accuracy). As the selected model was trained with EMG data only from right arms, a channel mapping to adapt the EMG signals from the left side was also developed. Unfortunately, the channel mapping implemented did not improve the recognition accuracy (9.33%). These findings highlight the challenges in achieving high performance HGR for individuals with cranial injuries. It might also point out that developing myoelectric prostheses using datasets from unaffected individuals may be inadequate for training robust models. © 2023 IEEE.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

On the Performance of CNN and GAN models for Melanoma Classification 2

On the Performance of CNN and GAN models for Melanoma Classi...

引用

2nd International Conference on artificial intelligence and Signal Processing, AISP 2022

作者： Joson, Nikhil Nair, Madhu S. Artificial Intelligence & Computer Vision Lab Department of Computer Science Cochin University of Science and Technology Kerala Kochi682022 India

ISBN: (纸本)9781665442909

Melanoma is the most harmful form of skin cancer and they emerge from melanocytes, a type of pigment-producing cells. Melanomas usually occur on the skin;they mostly form around the back for men, whereas the common place for women are legs. Once formed, symptoms of melanoma might include an increase in the size of some mole with irregular edges, or itchiness or changes in its color, etc. This paper reviews the latest research works on melanoma classification using CNN. The performance analysis of the state-of-the-art methods is done focusing only on the binary classification of melanoma. The dataset used for the performance analysis is based on SIIM-ISIC Melanoma Classification Challenge 2020, where the task was to identify melanoma using images of skin lesions by predicting a binary target for each image. The main intent of this study is to review the state-of-the-art deployments which identifies the recent research trends, challenges, and newer domains, like Generative Adversarial Networks (GAN) for melanoma classification and scrutinize the existing solutions for melanoma detection. © 2022 IEEE.

关键词： Dermatology

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：