找回密碼
 To register

QQ登錄

只需一步,快速開(kāi)始

掃一掃,訪問(wèn)微社區(qū)

打印 上一主題 下一主題

Titlebook: Computer Vision and Image Processing; 8th International Co Harkeerat Kaur,Vinit Jakhetiya,Sanjeev Kumar Conference proceedings 2024 The Edi

[復(fù)制鏈接]
樓主: T-Lymphocyte
51#
發(fā)表于 2025-3-30 09:58:21 | 只看該作者
Damage Segmentation and Restoration of Ancient Wall Paintings for Preserving Cultural Heritage,rating due to the passage of time, environmental factors, and human actions. Preserving and Restoring these delicate artworks is crucial. One approach to aid their digital restoration is leveraging advanced technologies like deep learning. This study applies image segmentation and restoration techni
52#
發(fā)表于 2025-3-30 13:21:38 | 只看該作者
53#
發(fā)表于 2025-3-30 17:39:07 | 只看該作者
,Fusion of?Handcrafted Features and?Deep Features to?Detect COVID-19,ures and handcrafted features to provide a unique method for COVID-19 identification using chest X-rays. In order to extract high-level features from the chest X-ray pictures, we first use a convolutional neural network (CNN) that has already been trained to take advantage of deep learning. The disc
54#
發(fā)表于 2025-3-30 23:49:04 | 只看該作者
,An Improved AttnGAN Model for?Text-to-Image Synthesis, text sequence length increases, these models suffer from a loss of information, leading to missed keywords and unsatisfactory results. To address this, we propose an attentional GAN (AttnGAN) model with a text attention mechanism. We evaluate AttnGAN variants on the MS-COCO dataset qualitatively an
55#
發(fā)表于 2025-3-31 01:41:34 | 只看該作者
56#
發(fā)表于 2025-3-31 05:21:36 | 只看該作者
,MAAD-GAN: Memory-Augmented Attention-Based Discriminator GAN for?Video Anomaly Detection,troduces a novel approach, named MAAD-GAN, for video anomaly detection (VAD) utilizing Generative Adversarial Networks (GANs). The MAAD-GAN framework combines a Wide Residual Network (WRN) in the generator with a memory module to learn the normal patterns present in the training video dataset, enabl
57#
發(fā)表于 2025-3-31 11:10:14 | 只看該作者
,AG-PDCnet: An Attention Guided Parkinson’s Disease Classification Network with?MRI, DTI and?Clinican Guided multi-class multi-modal PD Classification framework. In particular, we combine clinical assessments with the Neuroimaging data, namely, MRI and DTI. The three classes considered for this problem are PD, Healthy Controls (HC) and Scans Without Evidence of Dopamine Deficiency (SWEDD). Four CN
58#
發(fā)表于 2025-3-31 14:58:58 | 只看該作者
59#
發(fā)表于 2025-3-31 21:35:29 | 只看該作者
60#
發(fā)表于 2025-4-1 01:00:25 | 只看該作者
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛(ài)論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國(guó)際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-11 05:52
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
邵阳县| 阿克苏市| 平乡县| 荥阳市| 滨州市| 松桃| 旺苍县| 西乌珠穆沁旗| 西吉县| 平江县| 舒兰市| 沧州市| 桑植县| 汶川县| 邳州市| 三穗县| 石家庄市| 教育| 苍南县| 平凉市| 伊宁县| 昆明市| 桑植县| 漳平市| 天门市| 辉县市| 奉新县| 无极县| 甘泉县| 富平县| 乌鲁木齐市| 东阳市| 扶余县| 五常市| 丰宁| 金华市| 阿瓦提县| 获嘉县| 柳江县| 张掖市| 乌兰浩特市|